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See also Bioconductor > BiocViews > Visualization. Search 'genom' as the keyword.


nano ~/binary/IGV_2.3.52/ # Change -Xmx2000m to -Xmx4000m in order to increase the memory to 4GB

Simulated DNA-Seq

The following shows 3 simulated DNA-Seq data; the top has 8 insertions (purple '|') per read, the middle has 8 deletions (black '-') per read and the bottom has 8 snps per read.

File:Igv dna simul.png

Whole genome


File:Igv prjeb1486 wgs.png

Whole exome

  • (Left) GSE48215, UCSC hg19. It is seen there is a good coverage on all exons.
  • (Right) 1 of 3 whole exome data from SRP066363, UCSC hg19.

File:Igv gse48215.png File:Igv srp066363.png


  • (Left) Anders2013, Drosophila_melanogaster/Ensembl/BDGP5. It is seen there are no coverages on some exons.
  • (Right) GSE46876, UCSC/hg19.

File:Igv anders2013 rna.png File:Igv gse46876 rna.png

Tell DNA or RNA

  • DNA: no matter it is whole genome or whole exome, the coverage is more even. For whole exome, there is no splicing.
  • RNA: focusing on expression so the coverage changes a lot. The base name still A,C,G,T (not A,C,G,U).


GIVE: Genomic Interactive Visualization Engine

Build your own genome browser


Heat map plotting by genome coordinate.


NOISeq package

Exploratory analysis (Sequencing depth, GC content bias, RNA composition) and differential expression for RNA-seq data.


R interface to genome browsers and their annotation tracks

  • Retrieve annotation from GTF file and parse the file to a GRanges instance. See the 'Counting reads with summarizeOverlaps' vignette from GenomicAlignments package.


A small RNA-seq visualizer and analysis toolkit. It includes a function to draw bar plot of counts per million in tag length with two datasets (control and treatment).


See fig on p22 of Sushi vignette where genes with different strands are shown with different directions when plotGenes() was used. plotGenes() can be used to plot gene structures that are stored in bed format.

cBioPortal and TCGA

The cBioPortal for Cancer Genomics provides visualization, analysis and download of large-scale cancer genomics data sets.

Tutorial: retrieve full TCGA datasets from cBioportal with R


Qualimap 2 is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.


SeqMonk is a program to enable the visualisation and analysis of mapped sequence data.

Copy Number

Copy number work flow using Bioconductor

Detect copy number variation (CNV) from the whole exome sequencing

Whole exome sequencing != whole genome sequencing

Consensus CDS/CCDS

DBS segmentation algorithm

DBS: a fast and informative segmentation algorithm for DNA copy number analysis



Introduction to Sequence Data Analysis

NIH only

RNA-seq: Basics, Applications and Protocol

Why Do You Need To Use Cdna For Rna-Seq?

RNA-Seq vs DNA-Seq

  • With DNA, you'd be randomly sequencing the entire genome
  • DNA-Seq cannot be used for measuring gene expression.

Total RNA-Seq

How to know that your RNA-seq is stranded or not?


  • UC Davis Bioinformatics Core.
  • CSAMA 2017: Statistical Data Analysis for Genome-Scale Biology



Quality control

For base quality score, the quality value, Q(sanger) = - 10log10(prob) where prob = probability that the call at base b is correct. Sanger (Phred quality scores) is between 0 and 93. In practice the maximum quality score is ~40. Quality values below 20 are typically considered low.

Phred quality score and scales

The original Phred scaling of 33-126 (representing scores 0-93) is also called the Sanger scale. There are also 2 other scales used by Illumina that shifts the range up:

  • Illumina 1.0 format uses ASCII 59 - 126 representing scores -5 - 62.
  • Illumina 1.3+ format uses ASCII 64 - 126 representing scores 0 - 62.
  • Illumina 1.8, the quality scores have basically returned to the use of the Sanger format (Phred+33).

FastQC (java based)

One problem is the reads in trimmed fastq files from one end may not appear in other end for paired data. So consider Trimmomatic.

Qualimap (java based)

Qualimap examines sequencing alignment data in SAM/BAM files according to the features of the mapped reads and provides an overall view of the data that helps to the detect biases in the sequencing and/or mapping of the data and eases decision-making for further analysis.

The first time when we launch Qualimap we may get a message: The following R packages are missing: optparse, NOISeq, Repitools. Features dependent on these packages are disabled. See user manual for details. OK.


Trim Galore!


A comprehensive toolset for quality control and data processing of RNA-Seq experiments.


A cross-platform and ultrafast toolkit for FASTA/Q file manipulation


An interactive Bioconductor application for quality-control, filtering and trimming of FASTQ files and BMC Bioinformatics

Alignment and Indexing

SAM file format

SAM format

Here are some important fields

  • Col 1: Read name
  • Col 2: FLAG [0,2^16-1]
  • Col 3: RNAME/Chrom
  • Col 4: Pos [0,2^31-1]
  • Col 5: MAPQ [0,2^8-1]
  • Col 6: CIGAR
  • Col 7: RNEXT (se as "=" if RNEXT is equal to RNAME)
  • Col 8: PNEXT - Mate position [0,2^31-1]
  • Col 9: Insert size [-2^31+1, 2^31-1]
  • Col 10: SEQ
  • Col 11: QUAL
  • Col 14 (not fixed): AS

Note pair-end data,

1. Fastq files
   A_1.fastq   A_2.fastq
   read1       read1
   read2       read2
   ...         ...

2. SAM files (sorted by read name)

For example, consider paired-end reads SRR925751.192 that was extracted from so called inproper paired reads in samtools. If we extract the paired reads by grep -w SRR925751.192 bt20/bwa.sam we will get

SRR925751.192  97  chr1  13205  60  101M = 13429  325 .....
SRR925751.192 145  chr1  13429  60  101M = 13205 -325 .....

By entering the flag values 97 & 145 into the SAM flag entry on, we see

  • FLAG value 97 means read paired, mate reverse strand, first in pair.
  • FLAG value 145 means read paired, read reverse strand, second in pair.

The two reads have positions. As we can see the insert size (it's the name shown on samtools help page and IGV program) is defined by start positions instead of end positions)

   13205       13305          13429    13529
    |------------->             <----------|

Consider a properly paired reads SRR925751.1. If we extract the paired reads, we will get

SRR925751.1   99  chr1  10010  0  90M11S  =  10016  95  .....
SRR925751.1  147  chr1  10016  0  12S89M  =  10010 -95 .....

Again by entering the flag values 99 and 147 into the Decoding SAM flags website, we see

  • FLAG value 99 means read paired, read mapped in proper pair, mate reverse strand, first in pair.
  • FLAG value 147 means read paired, read mapped in proper pair, read reverse strand, second in pair.
    10010        10099
     10016          10104

The insert size here is 10104-10010+1=95. Running the following command,

cat foo.bam | awk '{ if ($9 >0) {S+=$9; T+=1}} END {print "Mean: "S/T}'

I get 133.242 for the average insert size from properly paired reads on my data (GSE48215subset, read length=101).

If I run the same command on in-properly paired reads, I got 1.9e07 for the average insert size.

Bowtie2 (RNA/DNA)

Extremely fast, general purpose short read aligner.

Create index files

bowtie needs to have an index of the genome in order to perform its alignment functionality. For example, to build a bowtie index against UCSC hg19 (See Getting started with Bowtie 2 -> Indexing a reference genome)

bowtie2-build /data/ngs/public/sequences/hg19/genome.fa hg19

Even the index file can be directly downloaded without going through Bowtie program, Bowtie program is still needed by Tophat program where Tophat's job is to align the RNA-seq data to reference genome.


To run alignment,

bowtie2 -p 4 -x /data/ngs/public/sequences/hg19 XXX.fastq -S XXX.bt2.sam

At the end of alignment, it will show how many (and percent) of reads are aligned 0 times, exactly 1 time, and >1 times.

We can transform them into a bam format ('[email protected]' means threads and '-T' means the reference file, it is optional)

samtools view [email protected] 16 -T XXX.fa XXX.bt2.sam > XXX.bt2.bam

and view the bam file

samtools view XXX.bt2.bam

Note that it is faster to use pipe to directly output the file in a bam format (reducing files I/O) if the aligner does not provide an option to output a binary bam format.


Used by whole-exome sequencing. For example, and Whole Exome Analysis.

Creating index file

Without creating index files, we will get an error [E::bwa_idx_load_from_disk] fail to locate the index files when we run the 'bwa mem' command (though the command only requires fa file in its arguments).

Prepare a reference for use with BWA and GATK

bwa index genome.fa          # generate 4 new files amb, ann, pac and bwt (not enough for running 'bwa mem')
bwa index -a bwtsw genome.fa # generate 5 new files amb, ann, pac, bwt and sa (right thing to do)

where -a bwtsw specifies that we want to use the indexing algorithm that is capable of handling the whole human genome.

It takes 3 hours to create the index files on the combined human+mouse genomes. Though there is no multhreads optionin bwa index, we can use the -b option to increase the block size in order to speed up the time. See The default value is 10000000 (1.0e8). See the following output from bwa index:

[bwa_index] Pack FASTA... 46.43 sec
[bwa_index] Construct BWT for the packed sequence...
[BWTIncCreate] textLength=11650920420, availableWord=831801828
[BWTIncConstructFromPacked] 10 iterations done. 99999988 characters processed.
[BWTIncConstructFromPacked] 20 iterations done. 199999988 characters processed.
[BWTIncConstructFromPacked] 1230 iterations done. 11647338276 characters processed.
[bwt_gen] Finished constructing BWT in 1232 iterations.
[bwa_index] 4769.18 seconds elapse.
[bwa_index] Update BWT... 45.78 sec
[bwa_index] Pack forward-only FASTA... 32.66 sec
[bwa_index] Construct SA from BWT and Occ... 2054.29 sec
[main] Version: 0.7.15-r1140
[main] CMD: bwa index -a bwtsw genomecb.fa
[main] Real time: 7027.396 sec; CPU: 6948.342 sec

Note that another index file fai is created by samtools

samtools faidx genome.fa

For example, the BWAIndex folder contains the following files

$ ls -l ~/igenomes/Homo_sapiens/UCSC/hg19/Sequence/BWAIndex/
total 12
lrwxrwxrwx 1 brb brb   22 Mar 10 03:40 genome.fa -> version0.6.0/genome.fa
lrwxrwxrwx 1 brb brb   26 Mar 10 03:37 genome.fa.amb -> version0.6.0/genome.fa.amb
lrwxrwxrwx 1 brb brb   26 Mar 10 03:40 genome.fa.ann -> version0.6.0/genome.fa.ann
lrwxrwxrwx 1 brb brb   26 Mar 10 03:40 genome.fa.bwt -> version0.6.0/genome.fa.bwt
-rw-r--r-- 1 brb brb  783 Apr 12 14:46 genome.fa.fai
lrwxrwxrwx 1 brb brb   26 Mar 10 03:40 genome.fa.pac -> version0.6.0/genome.fa.pac
lrwxrwxrwx 1 brb brb   25 Mar 10 03:37 -> version0.6.0/
drwxrwxr-x 2 brb brb 4096 Mar 15  2012 version0.5.x
drwxrwxr-x 2 brb brb 4096 Mar 15  2012 version0.6.0


bwa mem  # display the help
bwa mem -t 4 XXX.fa XXX.fastq > XXX.sam
more XXX.sam
samtools view -dT XXX.fa XXX.sam > XXX.bam # transform to bam format
samtools view XXX.bam | more

and output directly to a binary format

bwa mem -t 4 XXX.fa XXX.fastq | samtools view -bS - > XXX.bam  # transform to bam format directly

where '-S' means Ignoring for compatibility with previous samtools versions. Previously this option was required if input was in SAM format, but now the correct format is automatically detected by examining the first few characters of input. See

And this is a tutorial to use bwa and freebayes to do variant calling by Freebayes's author.

This tutorial is using a whole genome data SRR030257 from SRP001369

BWA for whole genome and GATK

Best pipeline for human whole exome sequencing

Summary report of a BAM file: samtools flagstat

Unlike Tophat and STAR, BWA mem does not provide a summary report of percentage of mapped reads. To get the percentage of mapped reads, use samtoolss flagstat command

$ export PATH=$PATH:/opt/SeqTools/bin/samtools-1.3

# fastq files from ExomeLungCancer/test.SRR2923335_*.fastq
# 'bwa mem' and 'samtools fixmate' have been run to generate <accepted_hits.bam>

$ wc -l ../test.SRR2923335_1.fastq   # 5000 reads in each of PAIRED end fastq files
20000 ../test.SRR2923335_1.fastq

$ samtools flagstat accepted_hits.bam 
10006 + 0 in total (QC-passed reads + QC-failed reads)
6 + 0 secondary
0 + 0 supplementary
0 + 0 duplicates
9971 + 0 mapped (99.65% : N/A)
10000 + 0 paired in sequencing
5000 + 0 read1
5000 + 0 read2
8502 + 0 properly paired (85.02% : N/A)
9934 + 0 with itself and mate mapped
31 + 0 singletons (0.31% : N/A)
1302 + 0 with mate mapped to a different chr
1231 + 0 with mate mapped to a different chr (mapQ>=5)

See also for alignment (including BWA mem and samtools flagstat).

  • Properly mapped (inward oriented & same chromosome & good insert size): samtools view -f 3 -F 2 foo.bam. But the result is not the same as flagstat??
  • Itself and mate mapped: samtools view -f 1 -F 12 foo.bam. But the result is not the same as flagstat??
  • Singleton: samtools view -F 4 -f 8 foo.bam. The flags indicate that the current read is mapped but its mate isn't. On GSE48215subset data, the return number of reads does not match with flagstat result but samtools view -f 4 -F 8 foo.bam (current read is unmapped and mate is mapped) gives the same result as the flagstat command.

Stringent criterion

Applied to RNA-Seq data? BWA isn't going to handle spliced alignment terribly well, so it's not normally recommended for RNAseq datasets.


Tophat (RNA)

Aligns RNA-Seq reads to the genome using Bowtie/Discovers splice sites. It does so by splitting longer reads into small sections and aligning those to the genome. It then looks for potential splice sites between pairs of sections to construct a final alignment.

Linux part.

$ type -a tophat # Find out which command the shell executes:
tophat is /home/mli/binary/tophat
$ ls -l ~/binary

Quick test of Tophat program

$ wget
$ tar xzvf test_data.tar.gz
$ cd ~/tophat_test_data/test_data
$ PATH=$PATH:/home/mli/bowtie-0.12.8
$ export PATH
$ ls
reads_1.fq      test_ref.1.ebwt  test_ref.3.bt2  test_ref.rev.1.bt2   test_ref.rev.2.ebwt
reads_2.fq      test_ref.2.bt2   test_ref.4.bt2  test_ref.rev.1.ebwt
test_ref.1.bt2  test_ref.2.ebwt  test_ref.fa     test_ref.rev.2.bt2
$ tophat -r 20 test_ref reads_1.fq reads_2.fq
$ # This will generate a new folder <tophat_out>
$ ls tophat_out
accepted_hits.bam  deletions.bed  insertions.bed  junctions.bed  logs  unmapped.bam

TopHat accepts FASTQ and FASTA files of sequencing reads as input. Alignments are reported in BAM files. BAM is the compressed, binary version of SAM43, a flexible and general purpose read alignment format. SAM and BAM files are produced by most next-generation sequence alignment tools as output, and many downstream analysis tools accept SAM and BAM as input. There are also numerous utilities for viewing and manipulating SAM and BAM files. Perhaps most popular among these are the SAM tools ( and the Picard tools (

Note that if the data is DNA-Seq, we can merely use Bowtie2 or BWA tools since we don't have to worry about splicing.

An example of using Tophat2 (paired end in this case, 5 threads) is

tophat2  --no-coverage-search -p 5 \
       -o "Sample1" \
       -G ~/iGenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf \
       --transcriptome-index=transcriptome_data/known \
       ~/iGenomes/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/genome \
       myfastq_R1.fastq.gz myfastq_R2.fastq.gz

To find out the alignment rate for ALL bam files (eg we have ctrl1, ctrl2, test1, test2 directories and each of them have align_summary.txt file),

grep "overall read mapping rate" */align_summary.txt

Novel transcripts

How does TopHat find junctions?

Can Tophat Be Used For Mapping Dna-Seq

  • In contrast to DNA-sequence alignment, RNA-seq mapping algorithms have two additional challenges. First, because genes in eukaryotic genomes contain introns, and because reads sequenced from mature mRNA transcripts do not include these introns, any RNA-seq alignment program must be able to handle gapped (or spliced) alignment with very large gaps.
  • TopHat is designed to map reads to a reference allowing splicing. In your case, the reads are not spliced because are genomic, so don't waste your time and resources and use Bowtie/BWA directly.

Hisat (RNA/DNA)

STAR (Spliced Transcripts Alignment to a Reference, RNA)

Optimizing RNA-Seq Mapping with STAR by Alexander Dobin and Thomas R. Gingeras

Its manual is on github. The 2015 paper includes scripts to run STAR.

Note that the readme file says HARDWARE/SOFTWARE REQUIREMENTS:

  • x86-64 compatible processors
  • 64 bit Linux or Mac OS X
  • 30GB of RAM for human genome. In fact, it requires 34GB (tested on UCSC/hg38). See the following jobhist output from running indexing on Biowulf.
Submission Command : sbatch --cpus-per-task=2 --mem=64g --time=4:00:00 createStarIndex

Jobid        Partition       State  Nodes  CPUs      Walltime       Runtime         MemReq  MemUsed  Nodelist
44714895          norm   COMPLETED      1     2      04:00:00      02:54:21    64.0GB/node   33.6GB  cn3144

See the blog on for a comparison of speed and memory requirement.

In short, the notable increase in speed comes at the price of a larger memory requirement.

To build STAR on Ubuntu (14.04)

sudo tar -xzf STAR_2.4.2a.tar.gz -C /opt/RNA-Seq/bin

cd /opt/RNA-Seq/bin/STAR-STAR_2.4.2a
cd source
sudo make STAR

Create index folder

STAR --runMode genomeGenerate --runThreadN 11 \
     --genomeDir STARindex \
     --genomeFastaFiles genome.fa \
     --sjdbGTFfile genes.gtf \
     --sjdbOverhang 100

where 100 = read length (one side) -1 = 101 - 100.

STARindex folder

[email protected] ~/SRP050992 $ ls ~/SRP012607/STARindex/
chrLength.txt      chrStart.txt          SA  
chrNameLength.txt  Genome                SAindex
chrName.txt      genomeParameters.txt  sjdbInfo.txt
[email protected] ~/SRP050992 $ cat ~/SRP012607/STARindex/genomeParameters.txt
### STAR   --runMode genomeGenerate   --runThreadN 11   --genomeDir STARindex   --genomeFastaFiles /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa      --sjdbGTFfile /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.gtf   --sjdbOverhang 100
versionGenome   20201
genomeFastaFiles        /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa
genomeSAindexNbases     14
genomeChrBinNbits       18
genomeSAsparseD 1
sjdbOverhang    100
sjdbFileChrStartEnd     -
sjdbGTFfile     /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Annotation/Genes/genes.gtf
sjdbGTFchrPrefix        -
sjdbGTFfeatureExon      exon
sjdbGTFtagExonParentTranscript  transcript_id
sjdbGTFtagExonParentGene        gene_id
sjdbInsertSave  Basic

Splice junction

  • Manual. Search 'sjdbOverhang' (length of the donor/acceptor sequence on each side of the junctions). The --sjdbOverhang is used only at the genome generation step, and tells STAR how many bases to concatenate from donor and acceptor sides of the junctions. If you have 100b reads, the ideal value of --sjdbOverhang is 99, which allows the 100b read to map 99b on one side, 1b on the other side. One can think of --sjdbOverhang as the maximum possible overhang for your reads. See A post.
  • For the <> format see section 4.4 of STAR manual. The first few lines of the file looks like (It is strange that the values in column 7, 8 are so large)
 $ head
chrM    711     764     1       1       1       2       1       44
chrM    717     1968    1       1       1       1       0       43
chrM    759     1189    0       0       1       11      3       30
chrM    795     830     2       2       1       335     344     30
chrM    822     874     1       1       1       1       1       17
chrM    831     917     2       2       1       733     81      38
chrM    831     3135    2       2       1       1       0       30
chrM    846     1126    0       0       1       4       0       50
chrM    876     922     1       1       1       26      9       31
chrM    962     1052    2       2       1       3       0       30
 ^       ^      ^       ^       ^       ^       ^       ^        ^
 |       |      |       |       |       |       |       |        |
chrom  start   end    strand  intron annotated  |     # of       |
                      0=undef  motif           # of  multi mapped|
                      1=+                   uniquely mapped    max spliced alignment overhang  
                      2=-                      reads
  • Tophat. Search overhang, donor/acceptor keywords. Check out <junctions.bed>. Tophat suggest users use this second option (--coverage-search) for short reads (< 45bp) and with a small number of reads (<= 10 million).
  • HiSAT comes with a python script that can extract splice sites from a GTF file. See this post.
$ cd /tmp
$ /opt/SeqTools/bin/hisat2-2.0.4/ \
  ~/igenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf > splicesites.txt
$ head splicesites.txt
chr1    12226   12612   +
chr1    12720   13220   +
chr1    14828   14969   -
chr1    15037   15795   -
chr1    15946   16606   -
chr1    16764   16857   -
chr1    17054   17232   -
chr1    17367   17605   -
chr1    17741   17914   -
chr1    18060   18267   -
  • To extract the splice alignment from bam files using samtools, see this and this posts.

Screen output

Oct 07 19:25:19 ..... started STAR run
Oct 07 19:25:19 ... starting to generate Genome files
Oct 07 19:27:08 ... starting to sort Suffix Array. This may take a long time...
Oct 07 19:27:38 ... sorting Suffix Array chunks and saving them to disk...
Oct 07 20:07:07 ... loading chunks from disk, packing SA...
Oct 07 20:22:52 ... finished generating suffix array
Oct 07 20:22:52 ... generating Suffix Array index
Oct 07 20:26:32 ... completed Suffix Array index
Oct 07 20:26:32 ..... processing annotations GTF
Oct 07 20:26:38 ..... inserting junctions into the genome indices
Oct 07 20:30:15 ... writing Genome to disk ...
Oct 07 20:31:00 ... writing Suffix Array to disk ...
Oct 07 20:37:24 ... writing SAindex to disk
Oct 07 20:37:39 ..... finished successfully

An example of the file

$ cat
                                 Started job on |       Jul 21 13:12:11
                             Started mapping on |       Jul 21 13:35:06
                                    Finished on |       Jul 21 13:57:42
       Mapping speed, Million of reads per hour |       248.01

                          Number of input reads |       93418927
                      Average input read length |       202
                                    UNIQUE READS:
                   Uniquely mapped reads number |       61537064
                        Uniquely mapped reads % |       65.87%
                          Average mapped length |       192.62
                       Number of splices: Total |       15658546
            Number of splices: Annotated (sjdb) |       15590438
                       Number of splices: GT/AG |       15400163
                       Number of splices: GC/AG |       178602
                       Number of splices: AT/AC |       14658
               Number of splices: Non-canonical |       65123
                      Mismatch rate per base, % |       0.86%
                         Deletion rate per base |       0.02%
                        Deletion average length |       1.61
                        Insertion rate per base |       0.03%
                       Insertion average length |       1.40
                             MULTI-MAPPING READS:
        Number of reads mapped to multiple loci |       4962849
             % of reads mapped to multiple loci |       5.31%
        Number of reads mapped to too many loci |       62555
             % of reads mapped to too many loci |       0.07%
                                  UNMAPPED READS:
       % of reads unmapped: too many mismatches |       0.00%
                 % of reads unmapped: too short |       28.70%
                     % of reads unmapped: other |       0.05%
                                  CHIMERIC READS:
                       Number of chimeric reads |       0
                            % of chimeric reads |       0.00%

Note that 65.87 (uniquely mapped) + 5.31 (mapped to multiple loci) + 0.07 (mapped too many loci) + 28.70 (unmapped too short) + 0.05 (unmapped other) = 100

By default, STAR only outputs reads that map to <=10 loci, others are considered "mapped to too many loci". You can increase this threshold by increasing --outFilterMultimapNmax. See here.

Subread (RNA/DNA)

In practice,

  1. For DNA-Seq data, the subread aligner is used.
  2. For RNA-Seq data,
    • the subread aligner is used when selecting gene counting analysis only.
    • the subjunc aligner is used when variant calling analysis is selected.


RNASequel: accurate and repeat tolerant realignment of RNA-seq reads


Scalable Nucleotide Alignment Program -- a fast and accurate read aligner for high-throughput sequencing data

snap is the best choice so far. star/hisat2 are as fast but not as good for genomic reads. accurate mappers need to do alignment anyway

Keep in mind SNAP's index is 10-15x bigger than input. 4Gbp->50GB index. SNAP loads index into memory. HISAT2 index is similar size as input


  • Tophat and STAR needs index files. So if we want to run the alignment using multiple nodes, we want to first run the alignment on a single sample first. Then we can run alignment on others samples using multiple nodes.
  • The index files of Tophat only depends on the reference genome. However the index files of STAR depends on both the reference genome and the read length of the data.

Alignment Algorithms

Alignment free


SAMtools bundle include samtools, bcftools, bgzip, tabix, wgsim and htslib. samtools and bcftools are based on htslib.

$ /opt/SeqTools/bin/samtools-1.3/samtools

Program: samtools (Tools for alignments in the SAM format)
Version: 1.3 (using htslib 1.3)

Usage:   samtools <command> [options]

  -- Indexing
     dict           create a sequence dictionary file
     faidx          index/extract FASTA
     index          index alignment

  -- Editing
     calmd          recalculate MD/NM tags and '=' bases
     fixmate        fix mate information
     reheader       replace BAM header
     rmdup          remove PCR duplicates
     targetcut      cut fosmid regions (for fosmid pool only)
     addreplacerg   adds or replaces RG tags

  -- File operations
     collate        shuffle and group alignments by name
     cat            concatenate BAMs
     merge          merge sorted alignments
     mpileup        multi-way pileup
     sort           sort alignment file
     split          splits a file by read group
     quickcheck     quickly check if SAM/BAM/CRAM file appears intact
     fastq          converts a BAM to a FASTQ
     fasta          converts a BAM to a FASTA

  -- Statistics
     bedcov         read depth per BED region
     depth          compute the depth
     flagstat       simple stats
     idxstats       BAM index stats
     phase          phase heterozygotes
     stats          generate stats (former bamcheck)

  -- Viewing
     flags          explain BAM flags
     tview          text alignment viewer
     view           SAM<->BAM<->CRAM conversion
     depad          convert padded BAM to unpadded BAM

To compile the new version (v1.x) of samtools,

git clone
git clone
cd samtools

Multi-thread option

Only view and sort have multi-thread ([email protected]) option.

samtools sort

A lot of temporary files will be created.

For example, if my output file is called <bwa_homo2_sort.bam>, there are 80 <bwa_homo2_sort.bam.tmp.XXXX.bam> files (each is about 210 MB) generated.

samtools sort [email protected] $SLURM_CPUS_PER_TASK -O bam -n bwa_homo2.sam -o bwa_homo2_sort.bam

Decoding SAM flags

samtools flagstat: Know how many alignment a bam file contains

[email protected]:~/GSE11209$ /opt/RNA-Seq/bin/samtools-0.1.19/samtools flagstat dT_bio_s.bam
1393561 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 duplicates
1393561 + 0 mapped (100.00%:-nan%)
0 + 0 paired in sequencing
0 + 0 read1
0 + 0 read2
0 + 0 properly paired (-nan%:-nan%)
0 + 0 with itself and mate mapped
0 + 0 singletons (-nan%:-nan%)
0 + 0 with mate mapped to a different chr
0 + 0 with mate mapped to a different chr (mapQ>=5)

We can see how many reads have multiple alignments by comparing the number of reads and the number of alignments output from samtools flagstat.

[email protected]:~/GSE11209$ wc -l SRR002062.fastq
13778616 SRR002062.fastq

Note: there is no multithread option in samtools flagstat.

samtools flagstat source code

samtools view

See also Anders2013-sam/bam and

Note that we can use both -f and -F flags together.

 $ /opt/SeqTools/bin/samtools-1.3/samtools view -h

Usage: samtools view [options] <in.bam>|<in.sam>|<in.cram> [region ...]

  -b       output BAM
  -C       output CRAM (requires -T)
  -1       use fast BAM compression (implies -b)
  -u       uncompressed BAM output (implies -b)
  -h       include header in SAM output
  -H       print SAM header only (no alignments)
  -c       print only the count of matching records
  -o FILE  output file name [stdout]
  -U FILE  output reads not selected by filters to FILE [null]
  -t FILE  FILE listing reference names and lengths (see long help) [null]
  -L FILE  only include reads overlapping this BED FILE [null]
  -r STR   only include reads in read group STR [null]
  -R FILE  only include reads with read group listed in FILE [null]
  -q INT   only include reads with mapping quality >= INT [0]
  -l STR   only include reads in library STR [null]
  -m INT   only include reads with number of CIGAR operations consuming
           query sequence >= INT [0]
  -f INT   only include reads with all bits set in INT set in FLAG [0]
  -F INT   only include reads with none of the bits set in INT set in FLAG [0]
  -x STR   read tag to strip (repeatable) [null]
  -B       collapse the backward CIGAR operation
  -s FLOAT integer part sets seed of random number generator [0];
           rest sets fraction of templates to subsample [no subsampling]
  [email protected], --threads INT
           number of BAM/CRAM compression threads [0]
  -?       print long help, including note about region specification
  -S       ignored (input format is auto-detected)
      --input-fmt-option OPT[=VAL]
               Specify a single input file format option in the form
               of OPTION or OPTION=VALUE
  -O, --output-fmt FORMAT[,OPT[=VAL]]...
               Specify output format (SAM, BAM, CRAM)
      --output-fmt-option OPT[=VAL]
               Specify a single output file format option in the form
               of OPTION or OPTION=VALUE
  -T, --reference FILE
               Reference sequence FASTA FILE [null]

View bam files:

samtools view XXX.bam    # No header
samtools view [email protected] 16 -h XXX.bam # include header in SAM output
samtools view [email protected] 16 -H xxx.bam # See the header only. The header may contains the reference genome fasta information.

Note that according to SAM pdf, the header is optional.

Sam and bam files conversion:

/opt/RNA-Seq/bin/samtools-0.1.19/samtools view --help

# sam -> bam (need reference genome file)
samtools view [email protected] 16 -b XXX.fa XXX.bt2.sam > XXX.bt2.bam

# bam -> sam
samtools view [email protected] 16 -h -o out.sam in.bam


# assuming bam file is sorted & indexed
samtools view [email protected] 16 XXX.bam "chr22:24000000-25000000" | more

Remove unpaired reads

According to the samtools manual, the 7th column represents the chromosome name of the mate/next read. If the field is set as '*' when the information is unavailable and set as '=' if RNEXT is identical RNAME.

When we use samtools view command to manually removed certain reads from a pair-end sam/bam file, the bam file will has a problem when it is used in picard MarkDuplicates. A solution is to run the following line to remove these unpaired reads (so we don't need to the VALIDATION_STRINGENCY parameter)

samtools view -h accepted_hits_bwa.bam | awk -F "\t" '$7!="*" { print $0 }' > accepted_hits_bwa2.sam

less bam files

Convert SAM to BAM

samtools view -b input.sam  >  output.bam
# OR
samtools view -b input.sam -o output.bam

There is no need to add "-S". The header will be kept in the bam file.

Convert BAM to SAM

samtools view -h input.bam -o output.sam

where '-h' is to ensure the converted SAM file contains the header information. Generally, it is useful to store only sorted BAM files as they use much less disk space and are faster to process.

Convert BAM to FASTQ

# Method 1: samtools
samtools bam2fq SAMPLE.bam > SAMPLE.fastq 
cat SAMPLE.fastq | grep '^@.*/1$' -A 3 --no-group-separator > SAMPLE_r1.fastq 
cat SAMPLE.fastq | grep '^@.*/2$' -A 3 --no-group-separator > SAMPLE_r2.fastq

# Method 2: picard
java -Xmx2g -jar Picard/SamToFastq.jar I=SAMPLE.bam F=SAMPLE_r1.fastq F2=SAMPLE_r2.fastq

# Method 3: bam2fastx

# Method 4: Bedtools - bamtofastq
# Single fastq
bedtools bamtofastq -i input.bam -fq output.fastq
#  BAM should be sorted by query name (samtools sort -n aln.bam aln.qsort) if creating paired FASTQ 
samtools sort -n input.bam -o input_sorted.bam  # sort by read namem (-n)
bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq

# Method 5: Bamtools

Consider the ExomeLungCancer example

# ExomeLungCancer/test.SRR2923335_*.fastq

$ # Method 1
$ samtools bam2fq bwa.sam > SAMPLE.fastq 
$ cat SAMPLE.fastq | grep '^@.*/1$' -A 3 --no-group-separator > SAMPLE_r1.fastq
$ cat SAMPLE.fastq | grep '^@.*/2$' -A 3 --no-group-separator > SAMPLE_r2.fastq
$ head -n 4 SAMPLE_r1.fastq 
$ head -n 4 output_r2.fastq
CCCFFFFFFHHGHJJJJJEHJIJI##11CCG?DFGI#0)88##0/[email protected]##--;?##,,5=BABBDDD)[email protected]>

$ # Method 4
$ samtools sort -n accepted_hits.bam -o accepted_sortbyname.bam
$ /opt/SeqTools/bin/bedtools2/bin/bedtools bamtofastq -i accepted_sortbyname.bam -fq output_r1.fastq -fq2 output_r2.fastq

# Compare the first read
# Original fastq file
$ head -n 4 ../test.SRR2923335_1.fastq 
@SRR2923335.1 1 length=100
+SRR2923335.1 1 length=100
$ head -n 4 ../test.SRR2923335_2.fastq 
@SRR2923335.1 1 length=100
+SRR2923335.1 1 length=100
CCCFFFFFFHHGHJJJJJEHJIJI##11CCG?DFGI#0)88##0/[email protected]##--;?##,,5=BABBDDD)[email protected]>

$ ##################################################################################################
$ head -n 4 output_r1.fastq 
$ head -n 4 output_r2.fastq 
CCCFFFFFFHHGHJJJJJEHJIJI##11CCG?DFGI#0)88##0/[email protected]##--;?##,,5=BABBDDD)[email protected]>

Using CRAM files

  • CRAM files are more dense than BAM files. CRAM files are smaller than BAM by taking advantage of an additional external "reference sequence" file.
  • CRAM format
  • The CRAM format was used (to replace the BAM format) in 1000genome

Extract single chromosome

samtools sort accepted_hits.bam -o accepted_hits_sorted.bam
samtools index accepted_hits_sorted.bam
samtools view -h accepted_hits_sorted.bam chr22 > accepted_hits_sub.sam

Primary, secondary, supplementary alignment

  • Multiple mapping and primary from SAM format specification
  • supplementary alignment = chimeric alignments = non-linear alignments. It's often the case that the sample we're sequencing has structural variations when compared to the reference sequence. Imagine a 100bp read. Let us suppose that the first 50bp align to chr1 and the last 50bp to chr6.
  • How to extract unique mapped results from Bowtie2 bam results? If you don't extract primary or unique reads from the sam/bam, the reads that maps equally well to multiple sequences will cause a serious bias in quantification of repeated elements.

To exact primary alignment reads (here), use

samtools view -F 260 input.bam

Extract reads based on read IDs

samtools  view Input.bam chrA:x-y  | cut -f1 > idFile.txt

LC_ALL=C grep -w -F -f idFile.txt  < in.sam > subset.sam

Extract mapped/unmapped reads from BAM

0x1	PAIRED	        paired-end (or multiple-segment) sequencing technology
0x2	PROPER_PAIR	each segment properly aligned according to the aligner
0x4	UNMAP	        segment unmapped
0x8	MUNMAP	        next segment in the template unmapped
0x10	REVERSE	        SEQ is reverse complemented
0x20	MREVERSE	SEQ of the next segment in the template is reverse complemented
0x40	READ1	        the first segment in the template
0x80	READ2	        the last segment in the template
0x100	SECONDARY	secondary alignment
0x200	QCFAIL	        not passing quality controls
0x400	DUP	        PCR or optical duplicate
0x800	SUPPLEMENTARY	supplementary alignment
# get the unmapped reads from a bam file use :
samtools view -f 4 file.bam > unmapped.sam

# get the output in bam use : 
samtools view -b -f 4 file.bam > unmapped.bam

# get only the mapped reads, the parameter 'F', which works like -v of grep
samtools view -b -F 4 file.bam > mapped.bam

# get only properly aligned/properly paired reads (
# Q: What Does The "Proper Pair" Bitwise Flag?
# A1:
#     Properly paired means the read itself as well as its mate are both mapped and
#     they were mapped within a reasonable distance given the expected distance
# A2:
#     It means means both mates of a read pair map to the same chromosome, oriented towards each other, 
#     and with a sensible insert size. 
samtools view -b -f 0x2 accepted_hits.bam > mappedPairs.bam
samtools view -u -f 8 -F 260 map.bam  > oneEndMapped.bam

and this will output all the unmapped end entries:

samtools view -u  -f 4 -F 264 map.bam  > oneEndUnmapped.bam

Count number of mapped/unmapped reads from BAM - samtools idxstats

# count the number of mapped reads
samtools view -c -F0x4 accepted_hits.bam
# count the number of unmapped reads
samtools view -c -f0x4 accepted_hits.bam

The result can be checked with samtools idxstats command. See

$ samtools sort accepted_hits.bam -o accepted_hits_sorted.bam
$ samtools index accepted_hits_sorted.bam accepted_hits_sorted.bai # BAI file is binary
$ ls -t
accepted_hits_sorted.bai  accepted_hits_sorted.bam  accepted_hits.bam
$ samtools idxstats accepted_hits_sorted.bam 
1	249250621	949	1
2	243199373	807	0
3	198022430	764	0
4	191154276	371	1
5	180915260	527	0
6	171115067	411	3
7	159138663	888	5
8	146364022	434	0
9	141213431	409	2
10	135534747	408	1
11	135006516	490	2
12	133851895	326	1
13	115169878	149	1
14	107349540	399	3
15	102531392	249	1
16	90354753	401	4
17	81195210	466	1
18	78077248	99	0
19	59128983	503	1
20	63025520	275	1
21	48129895	87	0
22	51304566	228	2
X	155270560	315	1
Y	59373566	6	0
MT	16569	10	0
*	0	0	4
$ samtools idxstats accepted_hits_sorted.bam | awk '{s+=$3+$4} END {print s}'
$ samtools idxstats accepted_hits_sorted.bam | awk '{s+=$3} END {print s}'

Find unmapped reads

Find multi-mapped reads

samtools view -F 4 file.bam | awk '{printf $1"\n"}' | sort | uniq -d | wc -l
# uniq -d will only print duplicate lines

Realignment and recalibration

  1. Realignment and recalibration won't change these metrics output from samtools flagstat.
  2. Recalibration is typically not needed anymore (the same goes for realignment if you're using something like GATK's haplotype caller).
  3. Realignment just locally realigns things and typically the assigned MAPQ values don't change (unmapped mates etc. also won't change).
  4. Recalibration typically affects only base qualities.


Suppose we download a vcf.gz from NCBI ftp site. We want to subset chromosome 1 and index the new file.

  • tabix - subset a vcf.gz file or index a vcf.gz file
tabix -h common_all_20160601.vcf.gz 1: > common_all_20160601_1.vcf

$ tabix -h

Version: 1.3
Usage:   tabix [OPTIONS] [FILE] [REGION [...]]

Indexing Options:
   -0, --zero-based           coordinates are zero-based
   -b, --begin INT            column number for region start [4]
   -c, --comment CHAR         skip comment lines starting with CHAR [null]
   -C, --csi                  generate CSI index for VCF (default is TBI)
   -e, --end INT              column number for region end (if no end, set INT to -b) [5]
   -f, --force                overwrite existing index without asking
   -m, --min-shift INT        set minimal interval size for CSI indices to 2^INT [14]
   -p, --preset STR           gff, bed, sam, vcf
   -s, --sequence INT         column number for sequence names (suppressed by -p) [1]
   -S, --skip-lines INT       skip first INT lines [0]

Querying and other options:
   -h, --print-header         print also the header lines
   -H, --only-header          print only the header lines
   -l, --list-chroms          list chromosome names
   -r, --reheader FILE        replace the header with the content of FILE
   -R, --regions FILE         restrict to regions listed in the file
   -T, --targets FILE         similar to -R but streams rather than index-jumps
  • bgzip - create a vcf.gz file from a vcf file
bgzip -c common_all_20160601_1.vcf > common_all_20160601_1.vcf.gz

# Indexing. Output is <common_all_20160601_1.vcf.gz.tbi>
tabix -f -p vcf common_all_20160601_1.vcf.gz

$ bgzip -h

Version: 1.3
Usage:   bgzip [OPTIONS] [FILE] ...
   -b, --offset INT        decompress at virtual file pointer (0-based uncompressed offset)
   -c, --stdout            write on standard output, keep original files unchanged
   -d, --decompress        decompress
   -f, --force             overwrite files without asking
   -h, --help              give this help
   -i, --index             compress and create BGZF index
   -I, --index-name FILE   name of BGZF index file [file.gz.gzi]
   -r, --reindex           (re)index compressed file
   -s, --size INT          decompress INT bytes (uncompressed size)
   [email protected], --threads INT       number of compression threads to use [1]


hts-nim: scripting high-performance genomic analyses and source in github.

Examples include bam filtering (bam files), read counts in regions (bam & bed files) and quality control variant call files (vcf files).


Displaying sequence statistics for next generation sequencing. SAMStat reports nucleotide composition, length distribution, base quality distribution, mapping statistics, mismatch, insertion and deletion error profiles, di-nucleotide and 10-mer over-representation. The output is a single html5 page which can be interpreted by a non-specialist.


samstat <file.sam>  <file.bam>  <file.fa>  <file.fq> ....

For each input file SAMStat will create a single html page named after the input file name plus a dot html suffix.


A set of tools (in Java) for working with next generation sequencing data in the BAM ( format.

Note that As of version 2.0.1 (Nov. 2015) Picard requires Java 1.8 (jdk8u66). The last version to support Java 1.7 was release 1.141. Use the following to check your Java version

[email protected] ~/github/picard $ java -version
java version "1.7.0_101"
OpenJDK Runtime Environment (IcedTea 2.6.6) (7u101-2.6.6-0ubuntu0.14.04.1)
OpenJDK 64-Bit Server VM (build 24.95-b01, mixed mode)

See my Linux -> JRE and JDK page.

Some people reported errors or complain about memory usage. See this post from And HTSeq python program is another option.

sudo apt-get install ant
git clone
cd picard
git clone
ant -lib lib/ant package-commands # We will see 'BUILD SUCCESSFUL'
                                  # It will create <dist/picard.jar>


java jvm-args -jar picard.jar PicardCommandName OPTION1=value1 OPTION2=value2...
# For example
cd ~/github/freebayes/test/tiny
java -Xmx2g -jar ~/github/picard/dist/picard.jar SamToFastq \
   INPUT=NA12878.chr22.tiny.bam \
   FASTQ=tmp_1.fq \
   SECOND_END_FASTQ=tmp_2.fq \
wc -l tmp_1.fq
# 6532 tmp_1.fq
wc -l tmp_2.fq
# 6532 tmp_2.fq

SAM validation error; Mate Alignment start should be 0 because reference name = *

Errors in SAM/BAM files can be diagnosed with ValidateSamFile

When I run picard with MarkDuplicates, AddOrReplaceReadGroups or ReorderSam parameter, I will get an error on the bam file aligned to human+mouse genomes but excluding mouse mappings:

Ignoring SAM validation error: ERROR: Record 3953, Read name D00748:53:C8KPMANXX:7:1109:11369:5479, Mate Alignment start should be 0 because reference name = *.' & Many of the validation errors reported by Picard are "technically" errors, but do not have any impact for the vast majority of downstream processing.

For example,

java -Xmx10g -jar picard.jar MarkDuplicates VALIDATION_STRINGENCY=LENIENT METRICS_FILE=MarkDudup.metrics INPUT=input.bam OUTPUT=output.bam
# OR to avoid messages
java -Xmx10g -jar picard.jar MarkDuplicates VALIDATION_STRINGENCY=SILENT METRICS_FILE=MarkDudup.metrics INPUT=input.bam OUTPUT=output.bam

Another (different) error message is:

SAM validation error: ERROR: Record 16642, Read name SRR925751.3835, First of pair flag should not be set for unpaired read.


The bed format is similar to gtf format but with more compact representation. We can use the apt-get method to install it on Linux environment.

bedtools intersect
bedtools bamtobed
bedtools bedtobam
bedtools getfasta
bedtools bamtofastq [OPTIONS] -i <BAM> -fq <FASTQ>

For example,

bedtools intersect -wo -a RefSeq.gtf -b XXX.bed | wc -l   # in this case every line is one exon
bedtools intersect -wo -a RefSeq.gtf -b XXX.bed | cut -f9 | cut -d ' ' -f2 | more
bedtools intersect -wo -a RefSeq.gtf -b XXX.bed | cut -f9 | cut -d ' ' -f2 | sort -u | wc -l 

bedtools intersect -wo -a RefSeq.bed -b XXX.bed | more    # one gene is one line with multiple intervals

One good use of bedtools is to find the intersection of bam and bed files (to double check the insertion/deletion/junction reads in bed files are in accepted_hits.bam). See this page

$ bedtools --version
bedtools v2.17.0
$ bedtools intersect -abam accepted_hits.bam -b junctions.bed | samtools view - | head -n 3

We can use bedtool to reverse bam files to fastq files. See


git clone
cd bedtools2


cd ~/github/freebayes/test/tiny # Working directory

~/github/samtools/samtools sort -n NA12878.chr22.tiny.bam NA12878.chr22.tiny.qsort

~/github/bedtools2/bin/bamToFastq -i NA12878.chr22.tiny.qsort.bam \
                      -fq aln.end1.fq \
                      -fq2 aln.end2.fq  2> bamToFastq.warning.out

## 60 warnings; see <bamToFastq.warning.out>
*****WARNING: Query ST-E00118:53:H02GVALXX:1:1102:1487:23724 is marked as paired, but its mate does not occur next to it in your BAM file.  Skipping. 

wc -l tmp.out  # 60
wc -l aln.end1.fq  # 6548 
wc -l aln.end2.fq  # 6548 
                   # recall tmp_1.fq is 6532

Cufflinks package

Transcriptome assembly and differential expression analysis for RNA-Seq.

Both Cufflinks and Cuffdiff accept SAM and BAM files as input. It is not uncommon for a single lane of Illumina HiSeq sequencing to produce FASTQ and BAM files with a combined size of 20 GB or larger. Laboratories planning to perform more than a small number of RNA-seq experiments should consider investing in robust storage infrastructure, either by purchasing their own hardware or through cloud storage services.

Tuxedo protocol

  1. bowtie2 - fast alignment
  2. tophat2 - splice alignment (rna-seq reads, rna are spliced, introns are removed, some reads may span over 2 exons)
  3. cufflinks - transcript assembly & quantitation
  4. cuffdiff2 - differential expression

Cufflinks - assemble reads into transcript


# about 47MB on ver 2.2.1 for Linux binary version
sudo tar xzvf ~/Downloads/cufflinks-2.2.1.Linux_x86_64.tar.gz  -C /opt/RNA-Seq/bin/
export PATH=$PATH:/opt/RNA-Seq/bin/cufflinks-2.2.1.Linux_x86_64/

# test
cufflinks -h

Cufflinks uses this map (done from Tophat) against the genome to assemble the reads into transcripts.

# Quantifying Known Transcripts using Cufflinks
cufflinks -o OutputDirectory/ -G refseq.gtf mappedReads.bam

# De novo Transcript Discovery using Cufflinks
cufflinks -o OutputDirectory/  mappedReads.bam

The output files are genes.fpkm_tracking, isoforms.fpkm_tracking, skipped.gtf and transcripts.gtf.

It can be used to calculate FPKM.

Cuffcompare - compares transcript assemblies to annotation

Cuffmerge - merges two or more transcript assemblies

First create a text file <assemblies.txt>


Then run

cd GSE11209
cuffmerge -g genes.gtf -s genome.fa assemblies.txt


Finds differentially expressed genes and transcripts/Detect differential splicing and promoter use.

Cuffdiff takes the aligned reads from two or more conditions and reports genes and transcripts that are differentially expressed using a rigorous statistical analysis.

Follow the tutorial, we can quickly test the cuffdiff program.

$ wget
$ cufflinks ./test_data.sam
$ ls -l
total 56
-rw-rw-r-- 1 mli mli   221 2013-03-05 15:51 genes.fpkm_tracking
-rw-rw-r-- 1 mli mli   231 2013-03-05 15:51 isoforms.fpkm_tracking
-rw-rw-r-- 1 mli mli     0 2013-03-05 15:51 skipped.gtf
-rw-rw-r-- 1 mli mli 41526 2009-09-26 19:15 test_data.sam
-rw-rw-r-- 1 mli mli   887 2013-03-05 15:51 transcripts.gtf

In real data,

cd GSE11209
cuffdiff -p 5 -o cuffdiff_out -b genome.fa -L dT,RH \
         -u merged_asm/merged.gtf \
        ./dT_bio/accepted_hits.bam,./dT_ori/accepted_hits.bam,./dT_tech/accepted_hits.bam \

N.B.: the FPKM value (<genes.fpkm_tracking>) is generated per condition not per sample. If we want FPKM per sample, we want to specify one condition for each sample.

The method of constructing test statistics can be found online.



Plots abundance and differential expression results from Cuffdiff. CummeRbund also handles the details of parsing Cufflinks output file formats to connect Cufflinks and the R statistical computing environment. CummeRbund transforms Cufflinks output files into R objects suitable for analysis with a wide variety of other packages available within the R environment and can also now be accessed through the Bioconductor website

The tool appears on the paper Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks by Trapnell, et al 2012.

Finding differentially expressed genes

Big pictures

   BWA/Bowtie     samtools        
fa ---------> sam ------> sam/bam (sorted indexed, short reads), vcf
   or tophat

Rsamtools    GenomeFeatures                  edgeR (normalization)
--------->   --------------> table of counts --------->


Technical replicate vs biological replicate

We sequence more DNA from the same DNA "library", which is called a "technical replicate". If we perform a new experiment with a new organism/tissue/population of cells, which is called a "biological replicate".

Youtube videos

Download the raw fastq data GSE19602 from GEO and uncompress fastq.bz2 to fastq (~700MB) file. NOTE: the data downloaded from ncbi is actually sra file format. We can use fastq_dump program in SRA_toolkit to convert sra to fastq.

~/Downloads/sratoolkit.2.3.2-ubuntu64/bin/fastq-dump ~/Downloads/SRR034580.sra 

If we want to run Galaxy locally, we can install it simply by 2 command lines

hg clone
cd galaxy-dist
hg update stable

To run Galaxy locally, we do

cd galaxy-dist
sh --reload

The command line will show Starting server in PID XXXX. serving on We can use Ctrl + C to stop the Galaxy process.

Note: One problem with this simple instruction is we have not created a user yet.

  1. Upload one fastq data. Click 'refresh' icon on the history panel to ensure the data is uploaded. Don't use 'refresh' button on the browser; it will cause an error for the current process.
  2. FASTQ Groomer. Convert the data to Galaxy needs. NGS: QC and manipulation => Illumina FASTQ. FASTQ quality scores type: Sanger. (~10 minutes). This part uses CPU not memory.
  3. Open a new browser tab and go to Right click the file all.cDNA and copy link location. In Galaxy click 'Upload File from your computer' paste URL to URL/Text entry.
  4. Scroll down Galaxy and select NGS:Mapping -> Map with BWA. PS. for some reason, BWA is not available. So I use Bowtie2 instead. The output of Bowtie2 is bam file.
    1. For reference genome, choose 'Use one from the history'. Galaxy automatically find the reference file 'ftp://ftp.plantbiology....' from history.
    2. Library mate-paired => Single-end.
    3. FASTQ file => 2: FASTQ Groomer on data 1.
    4. BWA settings to use => Commonly Used.
    5. Execute (~ 15 minutes)
  5. We can view the alignment file (sam format) created from BWA by using UCSV or IGV (input is bam or bai format). We now use NGS: SAM Tools to convert sam file to bam file. Click 'SAM-to-BAM converts SAM format to BAM format' tool.
    1. Choose the source for the reference list => History
    2. Converts SAM file => 4: Map with BWA on data 2 and data 3.
    3. Using reference file => 3:ftp://ftp.plantbiology.....
    4. Execute (~5 minutes)
  6. We want to create bai file which is a shortcut to IGV. It breaks the data into smaller accessible chunks. So when you pull up a certain cDNA, it goes straight to the subset. Go to the history, click the pencil icon (Edit Attributes) on the file SAM-to-BAM on data 3 and data 4.
    1. Look at 'Convert to new format' section. Go ahead and click 'Convert'. (< 1 minute). This will create another file.
    2. Use browser and go to ftp website to download all.cDNA file to desktop. The desktop should contain 3 files - all.cDNA, rice.bam and rice.bai files for IGV.
  7. Goto to download IGV which is a java-based application. I need to install java machine first by install openjdk-7-jdk. IGV by default will launch 'Human hg18' genome. Launch IGV by cd IGV_2.2.13;java -Xmx750m -jar igv.jar. I found the IGV input requires sam+bai OR bam+bai. So we need to click the pencil icon to create bai file first before we want to upload sam or bam file to IGV.
    1. Goto File => Import Genome. Call it 'rice' and select 'all.cDNA' sequence file. Click 'Save' button.
    2. Goto File => Upload from File => rice.bam.
    3. Top right panel is cDNA
    4. Middle right panel has a lot of 'boxes' which is a read. If we zoom in, we can see some read points to left (backward) while some points to right (forward). On the top is a histogram. For example, a base may be covered by a lot of reads then the histogram will show the high frequence.
    5. If we keep zoom in, we can see color at the Bottom right panel. Keeping zoom in, we can see the base G, C, T, A themselves.
    6. Using IGV, we can 1. examine coverage.
    7. We can 2. check 'alternative splicing'. (not for this cDNA)
    8. We can 3. examine SNPs for base change. If we see gray color (dark gray is hight quality read, light gray means low quality read), it means they are perfect match. If we see color, it means there is a change. For example, a read is 'C' but in fact it should be 'A'. If a case has many high quality reads, and half of them are 'G' but the reference genome shows 'A'. This is most likely a SNP. This is heterogeisity.
  8. Tophat - align RNA seq data to genomic DNA
    1. Suppose we have use Galaxy to upload 2 data. One is SRR034580 and we have run FASTQ Groomer on data 1. The second data is SRR034584 and we also have run FASTQ Groomer on data 2. We also have uploaded reference genome sequence.
    2. Goto Galaxy and find NGS: RNA Analysis => Tophat.
    3. reference genome => Use one from the history
    4. RNA-Seq FASTQ file => 2; FASTQ Groomer on data 1.
    5. Execute. This will create 2 files. One is splice junctions and the other is accepted_hits. We queue the job and run another Tophat with the 2nd 'groomer'ed data file. We are going to work on accepted_hits file.
    6. While the queue are running, we can click on 'pencil' icon on 'accepted_hits' job and run the utlity 'Convert to new format' (Bam to Bai). We should do this for both 'accepted_hits' files.
    7. For some reason, the execution failed: An error occurred with this dataset: TopHat v2.0.7 TopHat v2.0.7 Error indexing reference sequence /bin/sh: 1: bowtie-build: not found.
  9. Cufflinks. We will estimate transcript abundance by using FPKM (RPKM).
    1. SAM or BAM file of alignmed RNA-Seq reads => tophat on data 2.. accepted_hits
    2. Use Reference Annotation - No (choose Yes if we want annotation. This requires GTF format. See We don't have it for rice.)
    3. Execute. This will create 3 files. Gene expression, transcript expression and assembled transcripts.
    4. We also run Cufflinks for 2nd accepted_hits file. (~ 25 minutes)
  10. Cuffcompare. Compare one to each other.
    1. GTF file produced by Cufflinks => assembled transcript from the 1st data
    2. Use another GTF file produced by Cufflinks => Yes. It automatically find the other one.
    3. Execute. (< 10 minutes). This will create 7 files. Transcript accuracy, tmap file & refmap flie from each assembled transcripts, combined transcripts and transcript tracking.
    4. We are interested in combined transcripts file (to use in Cuffdiff).
  11. Cuffdiff.
    1. Transcripts => combined transcripts.
    2. SAM or BAM file of aligned RNA-Seq reads => 1st accepted_hits
    3. SAM or BAM file or aligned RNA-Seq reads => 2nd accepted_hits
    4. Execute. This will generate 11 files. Isoform expression, gene expression, TSS groups expression, CDS Expression FPKM Tracking, isoform FPKM tracking, gene FPKM tracking, TSS groups FPKM tracking, CDS FPKM tracking, splicing diff, promoters diff, CDS diff. We are interested in 'gene expression' file. We can save it and open it in Excel.
  12. IGV - 2 RNA-Seq datasets aligned to genomic DNA using Tophat
    1. Load the reference genome rice (see above)
    2. Upload from file => rice4.bam. Upload from file => rice5.bam.
    3. Alternative RNA splicing.

edX course

PH525.5x Case Study: RNA-seq data analysis. The course notes are forming a book. Check out and

Variant calling



Step 1: Mapping

bwa index <ref.fa>

bwa mem -R '@RG\tID:foo\tSM:bar\tLB:library1' <ref.fa> <read1.fa> <read1.fa> > lane.sam
samtools fixmate -O bam <lane.sam> <lane_fixmate.bam>
samtools sort -O bam -o <lane_sorted.bam> -T </tmp/lane_temp> <lane_fixmate.sam>

Step 2: Improvement

java -Xmx2g -jar GenomeAnalysisTK.jar -T RealignerTargetCreator -R <ref.fa> -I <lane.bam> \
    -o <lane.intervals> --known <bundle/b38/Mills1000G.b38.vcf>
java -Xmx4g -jar GenomeAnalysisTK.jar -T IndelRealigner -R <ref.fa> -I <lane.bam> \
    -targetIntervals <lane.intervals> --known <bundle/b38/Mills1000G.b38.vcf> -o <lane_realigned.bam>

java -Xmx4g -jar GenomeAnalysisTK.jar -T BaseRecalibrator -R <ref.fa> \
    -knownSites >bundle/b38/dbsnp_142.b38.vcf> -I <lane.bam> -o <lane_recal.table>
java -Xmx2g -jar GenomeAnalysisTK.jar -T PrintReads -R <ref.fa> -I <lane.bam> \
    --BSQR <lane_recal.table> -o <lane_recal.bam>

java -Xmx2g -jar MarkDuplicates.jar VALIDATION_STRINGENCY=LENIENT \
    INPUT=<lane_1.bam> INPUT=<lane_2.bam> INPUT=<lane_3.bam> OUTPUT=<library.bam>

samtools merge <sample.bam> <library1.bam> <library2.bam> <library3.bam>
samtools index <sample.bam>

Step 3: Variant calling

samtools mpileup -ugf <ref.fa> <sample1.bam> <sample2.bam> <sample3.bam> | \
    bcftools call -vmO z -o <study.vcf.gz>

Non-R Software

Variant detector/discovery, genotyping

Variant Identification

GUI software

Variant Annotation

See Variant Annotation

Biocoductor and R packages

fundamental - VariantAnnotation

readVcf() can be used to read a *.vcf or *.vcf.gz file.

dbSNP - SNPlocs.Hsapiens.dbSNP.20101109

Compare the rs numbers of the data and dbSNP.

variant wrt genes - TxDb.Hsapiens.UCSC.hg19.knownGene

Look for the column LOCATION which has values coding, fiveUTR, threeUTR, intron, intergenic, spliceSite, and promoter.

amino acid change for the non-synonymous variants - BSgenome.Hsapiens.UCSC.hg19

Look for the column CONSEQUENCE which has values synonymous or nonsynonymous.

SIFT & Polyphen for predict the impact of amino acid substitution on a human protein - PolyPhen.Hsapiens.dbSNP131

Look for the column PREDICTION which has values possibly damaging or benign.

rentrez tutorial

Accessing and Manipulating Biological Databases Exercises utm_source=rss&utm_medium=rss&utm_campaign=accessing-and-manipulating-biological-databases-exercises-part-2


vcf format

Below is an example of each record/row

1 #CHROM 2
2 POS 14370
3 ID rs6054257 or .
6 QUAL 29
8 INFO NS=3;DP=14;AF=0.5;DB;H2
opt NA00001 0/1:3,2:5:34:34,0,65

where the meanings of GT(genotype), AD(Allelic depths for the ref and alt alleles in the order listed), HQ (haplotype quality), GQ (genotype quality)... can be found in the header of the VCF file.


See the samtools and GATK output from GSE48215subset data included in BRB-SeqTools.

Count number of rows

grep -cv "#" vcffile


samtools view -b -q 30 input.bam > filtered.bam
  • To filter vcf based on MQ, use for example,
bcftools filter -i"QUAL >= 20 && DP >= 5 && MQ >= 30" INPUTVCF > OUTPUTVCF

Open with LibreOffice Calc

Use delimiter 'Tab' only and uncheck the others.

It will then get the correct header #CHROM, POS, ID, REF, ALT, QUAL, FILTER, INFO (separated by ;), FORMAT (separated by :), SampleID (separated by :) where INFO field contains site-level annotations and FORMAT&SampleID fields contain sample-level annotations.

Count number of records, snps and indels

  • Summary statistics
bcftools stats XXX.vcf

# SN, Summary numbers:
# SN    [2]id   [3]key  [4]value
SN      0       number of samples:      0
SN      0       number of records:      71712
SN      0       number of no-ALTs:      0
SN      0       number of SNPs: 65950
SN      0       number of MNPs: 0
SN      0       number of indels:       5762
SN      0       number of others:       0
SN      0       number of multiallelic sites:   0
SN      0       number of multiallelic SNP sites:       0
  • Remove the header
grep -v "^#" input.vcf > output.vcf
  • Count number of records
grep -c -v "^#" XXX.vcf   # -c means count, -v is invert search

VariantsToTable tool from Broad GATK

VariantAnnotation package

To install the R package, first we need to install required software in Linux

sudo apt-get update
sudo apt-get install libxml2-dev
sudo apt-get install libcurl4-openssl-dev

and then


vcf <- readVcf("filename.vcf", "hg19")
# Header

# Sample names

# Geno

# Genomic positions
head(rowRanges(vcf), 3)
# Variant quality

info(vcf)[1:3, ]
# Get the Depth (DP)
hist(info(vcf)$DP, xlab="DP")
summary(info(vcf)$DP, xlab="DP")
# Get the Mapping quality (MQ/MapQ)
hist(info(vcf)$MQ, xlab="MQ")


We first show how to use linux command line to explore the VCF file. Then we show how to use the VariantAnnotation package.

# The vcf file is created from running samtools on SRR1656687.fastq

nskip=$(grep "^#" ~/Downloads/BMBC2_liver3_IMPACT_raw.vcf | wc -l)
echo $nskip  # 53
awk 'NR==53, NR==53' ~/Downloads/BMBC2_liver3_IMPACT_raw.vcf

nall=$(cat ~/Downloads/BMBC2_liver3_IMPACT_raw.vcf | wc -l)
echo $nall   # 302174

# Generate <variantsOnly.vcf> which contains only the variant body
awk 'NR==54, NR==302174' ~/Downloads/BMBC2_liver3_IMPACT_raw.vcf > variantsOnly.vcf

# Generate <infoField.txt> which contains only the INFO field
cut -f 8 variantsOnly.vcf > infoField.txt

# Generate the final product
head -n 1 infoField.txt 
head -n 1 infoField.txt | cut -f1- -d ";" --output-delimiter " " 
## Method 1: use cut
### Problem: the result is not a table since not all fields appear in all variants.
cut -f1- -d ";" --output-delimiter=$'\t' infoField.txt > infoFieldTable.txt
## Method 2: use sed
### Problem: same as Method 1. 
sed 's/;/\t/g' infoField.txt > infoFieldTable2.txt
### OR if we want to replace the original file
sed -i 's/;/\t/g' infoField.txt

$ head -n 3 infoField.txt 

$ grep "^##INFO=<ID=" BMBC2_liver3_IMPACT_raw.vcf 
##INFO=<ID=INDEL,Number=0,Type=Flag,Description="Indicates that the variant is an INDEL.">
##INFO=<ID=IDV,Number=1,Type=Integer,Description="Maximum number of reads supporting an indel">
##INFO=<ID=IMF,Number=1,Type=Float,Description="Maximum fraction of reads supporting an indel">
##INFO=<ID=DP,Number=1,Type=Integer,Description="Raw read depth">
##INFO=<ID=VDB,Number=1,Type=Float,Description="Variant Distance Bias for filtering splice-site artefacts in RNA-seq data (bigger is better)",Version="3">
##INFO=<ID=RPB,Number=1,Type=Float,Description="Mann-Whitney U test of Read Position Bias (bigger is better)">
##INFO=<ID=MQB,Number=1,Type=Float,Description="Mann-Whitney U test of Mapping Quality Bias (bigger is better)">
##INFO=<ID=BQB,Number=1,Type=Float,Description="Mann-Whitney U test of Base Quality Bias (bigger is better)">
##INFO=<ID=MQSB,Number=1,Type=Float,Description="Mann-Whitney U test of Mapping Quality vs Strand Bias (bigger is better)">
##INFO=<ID=SGB,Number=1,Type=Float,Description="Segregation based metric.">
##INFO=<ID=MQ0F,Number=1,Type=Float,Description="Fraction of MQ0 reads (smaller is better)">
##INFO=<ID=ICB,Number=1,Type=Float,Description="Inbreeding Coefficient Binomial test (bigger is better)">
##INFO=<ID=HOB,Number=1,Type=Float,Description="Bias in the number of HOMs number (smaller is better)">
##INFO=<ID=AC,Number=A,Type=Integer,Description="Allele count in genotypes for each ALT allele, in the same order as listed">
##INFO=<ID=AN,Number=1,Type=Integer,Description="Total number of alleles in called genotypes">
##INFO=<ID=DP4,Number=4,Type=Integer,Description="Number of high-quality ref-forward , ref-reverse, alt-forward and alt-reverse bases">
##INFO=<ID=MQ,Number=1,Type=Integer,Description="Average mapping quality">
[email protected]:~/Downloads$ 

$ head -n2 variantsOnly.vcf 
1	10285	.	T	C	19.3175	.	DP=4;VDB=0.64;SGB=-0.453602;RPB=1;MQB=1;BQB=1;MQ0F=0;AC=2;AN=2;DP4=0,1,0,2;MQ=22	GT:PL	1/1:46,2,0
1	10333	.	C	T	14.9851	.	DP=6;VDB=0.325692;SGB=-0.556411;MQ0F=0.333333;AC=2;AN=2;DP4=0,0,0,4;MQ=11	GT:PL	1/1:42,12,0

$ awk 'NR==53, NR==58' ~/Downloads/BMBC2_liver3_IMPACT_raw.vcf
1	10285	.	T	C	19.3175	.	DP=4;VDB=0.64;SGB=-0.453602;RPB=1;MQB=1;BQB=1;MQ0F=0;AC=2;AN=2;DP4=0,1,0,2;MQ=22	GT:PL	1/1:46,2,0
1	10333	.	C	T	14.9851	.	DP=6;VDB=0.325692;SGB=-0.556411;MQ0F=0.333333;AC=2;AN=2;DP4=0,0,0,4;MQ=11	GT:PL	1/1:42,12,0
1	16257	.	G	C	24.566	.	DP=5;VDB=0.0481133;SGB=-0.511536;RPB=1;MQB=1;MQSB=1;BQB=1;MQ0F=0;ICB=1;HOB=0.5;AC=1;AN=2;DP4=1,0,2,1;MQ=26	GT:PL	0/1:57,0,10
1	16534	.	C	T	13.9287	.	DP=5;VDB=0.87268;SGB=-0.556411;MQ0F=0.4;AC=2;AN=2;DP4=0,0,4,0;MQ=11	GT:PL	1/1:41,12,0
1	16571	.	G	A	12.4197	.	DP=7;VDB=0.560696;SGB=-0.556411;RPB=1;MQB=1;MQSB=0;BQB=1;MQ0F=0.714286;AC=2;AN=2;DP4=1,0,2,2;MQ=8	GT:PL	1/1:39,8,0
[email protected]:~/Downloads$

As you can see it is not easy to create a table from the INFO field.

Now we use the VariantAnnotation package.

> library("VariantAnnotation")
> v=readVcf("BMBC2_liver3_IMPACT_raw.vcf", "hg19")
> v
class: CollapsedVCF 
dim: 302121 1 
  GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
  DataFrame with 17 columns: INDEL, IDV, IMF, DP, VDB, RPB, MQB, BQB, MQSB, ...
         Number Type    Description                                            
   INDEL 0      Flag    Indicates that the variant is an INDEL.                
   IDV   1      Integer Maximum number of reads supporting an indel            
   IMF   1      Float   Maximum fraction of reads supporting an indel          
   DP    1      Integer Raw read depth                                         
   VDB   1      Float   Variant Distance Bias for filtering splice-site arte...
   RPB   1      Float   Mann-Whitney U test of Read Position Bias (bigger is...
   MQB   1      Float   Mann-Whitney U test of Mapping Quality Bias (bigger ...
   BQB   1      Float   Mann-Whitney U test of Base Quality Bias (bigger is ...
   MQSB  1      Float   Mann-Whitney U test of Mapping Quality vs Strand Bia...
   SGB   1      Float   Segregation based metric.                              
   MQ0F  1      Float   Fraction of MQ0 reads (smaller is better)              
   ICB   1      Float   Inbreeding Coefficient Binomial test (bigger is better)
   HOB   1      Float   Bias in the number of HOMs number (smaller is better)  
   AC    A      Integer Allele count in genotypes for each ALT allele, in th...
   AN    1      Integer Total number of alleles in called genotypes            
   DP4   4      Integer Number of high-quality ref-forward , ref-reverse, al...
   MQ    1      Integer Average mapping quality                                
  SimpleList of length 2: GT, PL
      Number Type    Description                              
   GT 1      String  Genotype                                 
   PL G      Integer List of Phred-scaled genotype likelihoods
> header(v)
class: VCFHeader 
samples(1): dedup.bam
meta(2): META contig
fixed(2): FILTER ALT
info(17): INDEL IDV ... DP4 MQ
geno(2): GT PL
> dim(info(v))
[1] 302121     17
> info(v)[1:4, ]
DataFrame with 4 rows and 17 columns
                INDEL       IDV       IMF        DP       VDB       RPB
            <logical> <integer> <numeric> <integer> <numeric> <numeric>
1:10285_T/C     FALSE        NA        NA         4 0.6400000         1
1:10333_C/T     FALSE        NA        NA         6 0.3256920        NA
1:16257_G/C     FALSE        NA        NA         5 0.0481133         1
1:16534_C/T     FALSE        NA        NA         5 0.8726800        NA
                  MQB       BQB      MQSB       SGB      MQ0F       ICB
            <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
1:10285_T/C         1         1        NA -0.453602  0.000000        NA
1:10333_C/T        NA        NA        NA -0.556411  0.333333        NA
1:16257_G/C         1         1         1 -0.511536  0.000000         1
1:16534_C/T        NA        NA        NA -0.556411  0.400000        NA
                  HOB            AC        AN           DP4        MQ
            <numeric> <IntegerList> <integer> <IntegerList> <integer>
1:10285_T/C        NA             2         2     0,1,0,...        22
1:10333_C/T        NA             2         2     0,0,0,...        11
1:16257_G/C       0.5             1         2     1,0,2,...        26
1:16534_C/T        NA             2         2     0,0,4,...        11
> write.table(info(v)[1:4,], file="infoFieldTable3.txt", sep="\t", quote=F)
> head(rowRanges(v), 3)
GRanges object with 3 ranges and 5 metadata columns:
              seqnames         ranges strand | paramRangeID            REF
                 <Rle>      <IRanges>  <Rle> |     <factor> <DNAStringSet>
  1:10285_T/C        1 [10285, 10285]      * |         <NA>              T
  1:10333_C/T        1 [10333, 10333]      * |         <NA>              C
  1:16257_G/C        1 [16257, 16257]      * |         <NA>              G
                             ALT      QUAL      FILTER
              <DNAStringSetList> <numeric> <character>
  1:10285_T/C                  C   19.3175           .
  1:10333_C/T                  T   14.9851           .
  1:16257_G/C                  C   24.5660           .
  seqinfo: 25 sequences from hg19 genome
> qual(v)[1:5]
[1] 19.3175 14.9851 24.5660 13.9287 12.4197
> alt(v)[1:5]
DNAStringSetList of length 5
[[1]] C
[[2]] T
[[3]] C
[[4]] T
[[5]] A

> q('no')

vcfR package

Filtering strategy

Note that the QUAL (variant quality score) values can go very large. For example in GSE48215, samtools gives QUAL a range of [3,228] but gatk gives a range of [3, 10042]. This post says QUAL is not often a very useful property for evaluating the quality of a variant call.

# vcfs is vcf file ran by samtools using GSE48215subset
# vcfg is vcf file ran by gatk using GSE48215subset
> summary(vcfs)
      Length        Class         Mode 
        1604 CollapsedVCF           S4 
> summary(vcfg)
      Length        Class         Mode 
         868 CollapsedVCF           S4 

> summary(qual(vcfs))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.013  10.580  23.650  45.510  45.400 228.000 
> summary(qual(vcfg))
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
    3.98    21.77    47.74   293.90   116.10 10040.00 

> range(info(vcfs)$DP)
[1]   1 336
> range(info(vcfg)$DP)
[1]   1 317

> range(info(vcfs)$MQ)
[1]  4 60
> range(info(vcfg)$MQ)
[1] 21.00 65.19

# vcfg2 is vcf file ran by gatk using GSE48215
> vcfg2 <- readVcf("~/GSE48215/outputgcli/bt20_raw.vcf", "hg19")
> summary(vcfg2)
      Length        Class         Mode 
      506851 CollapsedVCF           S4 
> range(qual(vcfg2))
[1]     0.00 73662.77
> summary(qual(vcfg2))
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
    0.00    21.77    21.77   253.70    62.74 73660.00 

> range(info(vcfg2)$DP)
[1]    0 3315
> summary(info(vcfg2)$DP)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    2.00    2.00   14.04    4.00 3315.00 
> range(info(vcfg2)$MQ)
[1] NaN NaN
> range(info(vcfg2)$MQ, na.rm=T)
[1] 20 70
  • vaf and AD field (stands for allele depth/number of reads, 2 values for each genotype per sample, reference:alternative).

These two values will usually, but not always sum to the DP value. Reads that are not used for calling are not counted in the DP measure, but are included in AD. See Question: Understanding Vcf File Format

An example

GT:AD:DP:GQ:PL 0/1:1,2:3:30:67,0,30

R code to compute VAF by using AD information only (AD = 1,2 in this case)

vcf <- readVcf(file, refg)
vaf <- sapply(geno(vcf)$AD, function(x) x[2]/sum(x))

extract allele frequency

Calculate allele frequency from vcf files.

readvcf2 <- function(file, refg="hg19") {
  vcf <- readVcf(file, refg)
  if (!is.null(geno(vcf)$DP) && ncol(geno(vcf)$DP) > 1)  stop("More than one sample were found!")
  if ("AD" %in% names(geno(vcf))) {
    # note it is possible DP does not exist but AD exists (mutect2 output)
    # vaf <- sapply(geno(vcf)$AD, function(x) x[2]) / geno(vcf)$DP
    # vaf <- vaf[, 1]
    vaf <- sapply(geno(vcf)$AD, function(x) x[2]/sum(x))
  } else {
    vaf <- NULL
  if (is.null(geno(vcf)$DP) || 
      ($DP[1]) && is.null(info(vcf)$DP)) ||
      ($DP[1]) &&$DP[1]))) {
    cat("Warning: no DP information can be retrieved!\n")
    dp <- NULL
  } else {
    if (!is.null(geno(vcf)$DP) && !$DP[1])) {
      dp <- geno(vcf)$DP
      if (is.matrix(dp))  dp <- dp[, 1]
    } else {
      dp <- info(vcf)$DP
  if (! "chr" %in% substr(unique(seqnames(vcf)), 1, 3)) {
    chr <- paste('chr', as.character(seqnames(vcf)), sep='')
  } else {
    chr <- as.character(seqnames(vcf))
  strt <- paste(chr, start(vcf)) # ?BiocGenerics::start
  return(list(dp=dp, vaf=vaf, start=strt, chr=unique(as.character(seqnames(vcf)))))
vcftools --vcf file.vcf --freq --out output # No DP, No AD. No useful information
vcftools --vcf file.vcf --freq -c > output  # same as above

How can I extract only insertions from a VCF file?

subset vcf file

Using tabix.

Adding/removing 'chr' to/from vcf files

awk '{if($0 !~ /^#/) print "chr"$0; else print $0}' no_chr.vcf > with_chr.vcf # Not enough

awk '{ if($0 !~ /^#/) print "chr"$0; else if(match($0,/(##contig=<ID=)(.*)/,m)) print m[1]"chr"m[2]; else print $0 }' no_chr.vcf > with_chr.vcf

SAMtools (samtools, bcftools, htslib)

export seqtools_samtools_PATH=/opt/SeqTools/bin/samtools-1.3:/opt/SeqTools/bin/samtools-1.3/misc
export PATH=$seqtools_samtools_PATH:$PATH

samtools sort TNBC1/accepted_hits.bam TNBC1/accepted_hits-sorted
samtools index TNBC1/accepted_hits-sorted.bam TNBC1/accepted_hits-sorted.bai
samtools mpileup -uf ~/igenome/human/NCBI/build37.2/genome.fa \
            TNBC1/accepted_hits-sorted.bam | bcftools view -vcg - > TNBC1/var.raw.vcf

where '-u' in mpileup means uncompressed and '-f' means faidx indexed reference sequence file. If we do not use pipe command, the output from samtools mpileup is a bcf file which can be viewed by using 'bcftools view XXX.bcf | more' command.

Note that the samtools mpileup can be used in different ways. For example,

samtools mpileup -f XXX.fa XXX.bam > XXX.mpileup
samtools mpileup -v -u -f XXX.fa XXX.bam > XXX.vcf
samtools mpileup -g -f XXX.fa XXX.bam > XXX.bcf


bcftools — utilities for variant calling and manipulating VCFs and their binary counterparts BCFs. bcftools was one of components in SAMtools software (not anymore, see


sudo tar jxf bcftools-1.2.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/bcftools-1.2/
sudo make # create bcftools, plot-vcfstats, commands

Example: Add or remove or update annotations.

# Remove three fields
bcftools annotate -x ID,INFO/DP,FORMAT/DP file.vcf.gz

# Remove all INFO fields and all FORMAT fields except for GT and PL
bcftools annotate -x INFO,^FORMAT/GT,FORMAT/PL file.vcf

# Add ID, QUAL and INFO/TAG, not replacing TAG if already present
bcftools annotate -a src.bcf -c ID,QUAL,+TAG dst.bcf

# Update 'ID' column in VCF file,
# Note that the column header CHROM,FROM,.. only needs to appear in input.vcf.gz;
#       they may not appear in the annotation file 
# For vcf files, there is a comment sign '#' on the header line containing CHROM,FROM,... 
bcftools annotate -c CHROM,FROM,TO,ID,INFO/MLEAC,INFO/MLEAF -a annotation.vcf.gz -o output.vcf input.vcf.gz

# Carry over all INFO and FORMAT annotations except FORMAT/GT
bcftools annotate -a src.bcf -c INFO,^FORMAT/GT dst.bcf

# Annotate from a tab-delimited file with six columns (the fifth is ignored),
# first indexing with tabix. The coordinates are 1-based.
tabix -s1 -b2 -e2
bcftools annotate -a -h annots.hdr -c CHROM,POS,REF,ALT,-,TAG file.vcf

# Annotate from a tab-delimited file with regions (1-based coordinates, inclusive)
tabix -s1 -b2 -e3
bcftools annotate -a -h annots.hdr -c CHROM,FROM,TO,TAG inut.vcf

Example: variant call

samtools sort TNBC1/accepted_hits.bam TNBC1/accepted_hits-sorted
samtools index TNBC1/accepted_hits-sorted.bam TNBC1/accepted_hits-sorted.bai
samtools mpileup -uf ~/igenome/human/NCBI/build37.2/genome.fa \
                     TNBC1/accepted_hits-sorted.bam | bcftools view -vcg - > TNBC1/var.raw.vcf
# Or
samtools mpileup -g -f XXX.fa XXX.bam > sample.bcf
bcftools call -v -m -O z -o var.raw.vcf.gz sample.bcf 
zcat var.raw.vcf.gz | more
zcat var.raw.vcf.gz | grep -v "^#" | wc -l
samtools tview -p 17:1234567 XXX.bam XXX.fa  | more    # IGV alternative

where '-v' means exports variants only, '-m' for multiallelic-caller and '-O z' means compressed vcf format.

Example: count the number of snps, indels, et al in the vcf file, use

bcftools stats xxx.vcf | more

Example: filter based on variant quality, depth, mapping quality

bcftools filter -i"QUAL >= 20 && DP >= 5 && MQ >= 60" inputVCF > output.VCF

where '-i' include only sites for which EXPRESSION is true.

Example: change the header (dedup.bam -> dedup), use

$ grep dedup.bam dT_ori_raw.vcf                                                  
##samtoolsCommand=samtools mpileup -go temp.bcf -uf /home/brb/GSE11209-master/annotation/genome.fa dedup.bam
#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  dedup.bam
$ echo dedup > sampleName
$ bcftools reheader -s sampleName dT_ori_raw.vcf -o dT_ori_raw2.vcf
$ grep dedup.bam dT_ori_raw2.vcf
##samtoolsCommand=samtools mpileup -go temp.bcf -uf /home/brb/GSE11209-master/annotation/genome.fa dedup.bam
$ diff dT_ori_raw.vcf dT_ori_raw2.vcf
< #CHROM        POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  dedup.bam
> #CHROM        POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  dedup

What bcftools commands are used in BRB-SeqTools?

  • bcftools filter: apply fixed-threshold filters.
bcftools filter -i "QUAL >= 1 && DP >= 60 && MQ >= 1" INPUT.vcf > filtered.vcf
bcftools norm -m-both -o splitted.vcf filtered.vcf
bcftools norm -f genomeRef -o leftnormalized.vcf splitted.vcf
  • bcftools annotate: add/remove or update annotations
bcftools annotate -c ID -a dbSNPVCF leftnormalized.vcf.gz > dbsnp_anno.vcf
bcftools annotate -c ID,+GENE -a cosmicVCF dbsnp_anno.vcf.gz > cosmic_dbsnp.vcf
# +GENE: add annotations without overwriting existing values
# In this case, it is likely GENE does not appear in dbsnp_anno.vcf.gz
# It is better +INFO/GENE instead of GENE in '-c' parameter.
  • bcftools query: Extracts fields from VCF or BCF files and outputs them in user-defined format.
bcftools query -f '%INFO/AC\n' input.vcf > AC.txt
bcftools query -f '%INFO/MLEAC\n' input.vcf > MLEAC.txt

htslib: bgzip and tabix

  • bgzip – Block compression/decompression utility. The output file .gz is in a binary format.
  • tabix – Generic indexer for TAB-delimited genome position files. The output file tbi is in a binary format.


sudo tar jxf htslib-1.2.1.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/htslib-1.2.1/
sudo make  # create tabix, htsfile, bgzip commands


export PATH=/opt/SeqTools/bin/samtools-1.3/htslib-1.3:$PATH
export PATH=/opt/RNA-Seq/bin/bcftools-1.2/:$PATH
# zip and index
bgzip -c var.raw.vcf > var.raw.vcf.gz # var.raw.vcf will not be kept
tabix var.raw.vcf.gz  # create, index vcf files (very fast in this step)
bcftools annotate -c ID -a common_all_20150603.vcf.gz var.raw.vcf.gz > var_annot.vcf # 2 min

# subset based on chromosome 1, include 'chr' and position range if necessary
tabix -h var.raw.vcf.gz 1: > chr1.vcf
tabix -h var.raw.vcf.gz chr1:10,000,000-20,000,000

# subset a vcf file using a bed file
bgzip -c raw.vcf > raw.vcf.gz # without "-c", the original vcf file will not be kept
tabix -p vcf raw.vcf.gz  # create tbi file
tabix -R test.bed raw.vcf.gz > testout.vcf

GATK (Java)

# Download GenomeAnalysisTK-3.4-46.tar.bz2 from gatk website
sudo mkdir /opt/RNA-Seq/bin/gatk
sudo tar jxvf ~/Downloads/GenomeAnalysisTK-3.4-46.tar.bz2 -C /opt/RNA-Seq/bin/gatk
ls /opt/RNA-Seq/bin/gatk
# GenomeAnalysisTK.jar  resources

Citing papers

  1. McKenna et al. 2010 "The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data"
  2. DePristo et al. 2011 "A framework for variation discovery and genotyping using next-generation DNA sequencing data"
  3. Van der Auwera et al. 2013 "rom FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline"

Container version and Java

For some reason, running GATK 3.8 on Biowulf (CentOS + Sun Java) does not give any variants. But in my Ubuntu (OpenJDK), it does.

It is strange the website recommends Sun Java, but the container version uses OpenJDK.

$ docker run -it --rm broadinstitute/gatk
(gatk) [email protected]:/gatk# java -version
openjdk version "1.8.0_131"
OpenJDK Runtime Environment (build 1.8.0_131-8u131-b11-2ubuntu1.16.04.3-b11)
OpenJDK 64-Bit Server VM (build 25.131-b11, mixed mode)

$ docker run -it --rm broadinstitute/gatk3:3.8-0
[email protected]:/usr# java -version
openjdk version "1.8.0_102"
OpenJDK Runtime Environment (build 1.8.0_102-8u102-b14.1-1~bpo8+1-b14)
OpenJDK 64-Bit Server VM (build 25.102-b14, mixed mode)

See the example 'how to run a shell script on host' on how to run GATK from the host command line (the container will be deleted after the job is done; similar to what 'singularity' does).



dbSNP file

For running GATK best practices, dbsnp file has to be downloaded using the GATK version (with 'chr') for Ensembl but non-GATK (without 'chr') for UCSC. See Anders -> GATK.

MarkDuplicates by Picard

Sequencing error propagated in duplicates. See p14 on Broad Presentation -> Pipeline Talks -> MPG_Primer_2016-Seq_and_Variant_Discovery.

Reads have to be sorted by coordinates (using eg picard.jar SortSam OR samtools sort) first.

java"./tmpJava" \
  -Xmx10g -jar $PICARDJARPATH/picard.jar  \
  MarkDuplicates \
  METRICS_FILE=MarkDudup.metrics \
  INPUT=sorted.bam \
Check if sam is sorted

Read group assignment by Picard

java"/home/brb/SRP049647/outputvc/tmpJava" -Xmx10g \
  -jar /opt/SeqTools/bin/picard-tools-2.1.1/picard.jar AddOrReplaceReadGroups \
  INPUT=BMBC2_liver3_IMPACT.bam \
  OUTPUT=rg_added_sorted.bam \
  RGID=1 \
  RGLB=rna \
  RGPL=illumina \

Split reads into exon (RNA-seq only)

samtools index reorder.bam

java"/home/brb/SRP049647/outputvc/tmpJava" -Xmx10g \
  -jar /opt/SeqTools/bin/gatk/GenomeAnalysisTK.jar \
  -T SplitNCigarReads \
  -R /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/../WholeGenomeFasta/genome.fa \
  -I reorder.bam \
  -o split.bam \
  -fixNDN \

samtools index split.bam

Indel realignment

Local alignment around indels corrects mapping errors. See p15 on Broad Presentation -> Pipeline Talks -> MPG_Primer_2016-Seq_and_Variant_Discovery.


  • (from its documentation) indel realignment is no longer necessary for variant discovery if you plan to use a variant caller that performs a haplotype assembly step, such as HaplotypeCaller or MuTect2. However it is still required when using legacy callers such as UnifiedGenotyper or the original MuTect.
  • It require the following two steps before running indel realignment
    1. Generate an intervals (see next subsection) file
    2. sorted bam file (necessary?)
  • The '-known' option is not necessary. In this GATK workflow with HaplotypeCaller, it does not use '-known' or '-knownSites' in the pipeline.
Create realignment targets (*.intervals) using '-known' option

The following code follows Local Realignment around Indels. That is, we don't need to use the '-I' parameter as in example from the RealignerTargetCreator documentation.

java -Xmx10g -jar /opt/SeqTools/bin/gatk/GenomeAnalysisTK.jar \
      -T RealignerTargetCreator \
      -R /home/brb/igenomes/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa \
      -o  /home/brb/SeqTestdata/usefulvcf/hg19/gatk/BRB-SeqTools_indels_only.intervals \
      -known  /home/brb/SeqTestdata/usefulvcf/hg19/gatk/common_all_20160601.vcf  \
      -nt 11

Indel realignment requires an intervals file.

ReorderSam by Picard

Question: is this step necessary? Local Realignment around Indels documentation does not have emphasized this step.

java"/home/brb/SRP049647/outputvc/tmpJava" -Xmx10g \
  -jar /opt/SeqTools/bin/picard-tools-2.1.1/picard.jar  \
  ReorderSam \
  INPUT=rg_added_sorted.bam \
  OUTPUT=reorder.bam \
Indel realignment using '-known' option
java"/home/brb/SRP049647/outputvc/tmpJava" -Xmx10g \
  -jar /opt/SeqTools/bin/gatk/GenomeAnalysisTK.jar \
  -T IndelRealigner \
  -R /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/../WholeGenomeFasta/genome.fa \
  -I split.bam \
  -targetIntervals /home/brb/SeqTestdata/usefulvcf/hg38/gatk/BRB-SeqTools_indels_only.intervals \
  -known /home/brb/SeqTestdata/usefulvcf/hg38/gatk/common_all_20170710.vcf.gz \
  -o realigned_reads.bam \

Base quality score recalibration using '-knownSites' option

Base recalibrartion corrects for machine errors. See p16 on Broad Presentation -> Pipeline Talks -> MPG_Primer_2016-Seq_and_Variant_Discovery.

The base recalibration process involves two key steps

  1. builds a model of covariation based on the data and produces the recalibration table. It operates only at sites that are not in dbSNP; we assume that all reference mismatches we see are therefore errors and indicative of poor base quality.
  2. Assuming we are working with a large amount of data, we can then calculate an empirical probability of error given the particular covariates seen at this site, where p(error) = num mismatches / num observations. The output file is a table (of the several covariate values, number of observations, number of mismatches, empirical quality score).

Afterwards, it (I guess it is in the PrintReads command) adjusts the base quality scores in the data based on the model

(from the documentation) -knownSites parameter: This algorithm treats every reference mismatch as an indication of error. However, real genetic variation is expected to mismatch the reference, so it is critical that a database of known polymorphic sites (e.g. dbSNP) is given to the tool in order to mask out those sites.

In terms of the software implementation, this workflow actually includes two commands: BaseRecalibrator and PrintReads.

In my experience running the PrintReads command is very very slow even I have used the multi-threaded mode option (-nct). See a discussion parallelizing PrintReads.

java"/home/brb/SRP049647/outputvc/tmpJava" -Xmx10g \
  -jar /opt/SeqTools/bin/gatk/GenomeAnalysisTK.jar \
  -T BaseRecalibrator \
  -R /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/../WholeGenomeFasta/genome.fa \
  -I realigned_reads.bam \
  -nct 11 \
  -knownSites /home/brb/SeqTestdata/usefulvcf/hg38/gatk/common_all_20170710.vcf.gz \
  -o recal_data.table 

java"/home/brb/SRP049647/outputvc/tmpJava" -Xmx10g 
  -jar /opt/SeqTools/bin/gatk/GenomeAnalysisTK.jar 
  -T PrintReads 
  -R /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/../WholeGenomeFasta/genome.fa 
  -I realigned_reads.bam 
  -nct 11 
  -BQSR recal_data.table 
  -o recal.bam

Variant call by HaplotypeCaller (germline) and MuTec2 (somatic)

  • Germline vs. somatic mutations
  • HaplotypeCaller
    • The (HaplotypeCaller) algorithms used to calculate variant likelihoods is not well suited to extreme allele frequencies (relative to ploidy) so its use is not recommended for somatic (cancer) variant discovery. For that purpose, use MuTect2 instead.
    • HaplotypeCaller theory/properties
    • Local denovo assembly (De novo transcriptome assembly) based variant caller. Whenever the program encounters a region showing signs of variation, it discards the existing mapping information and completely reassembles the reads in that region. This allows the HaplotypeCaller to be more accurate when calling regions that are traditionally difficult to call, for example when they contain different types of variants close to each other.
    • Calls SNP, INDEL, MNP and small SV simultaneously
    • Removes mapping artifacts
    • More sensitive and accurate than the Unified Genotyper (UG)
  • How the HaplotypeCaller works? (Broad Presentation -> Pipeline Talks -> MPG_Primer_2015-Seq_and_Variant_Discovery)
    1. Define active regions (substantial evidence of variation relative to the reference)
    2. Determine haplotypes by re-assembly of the active region
    3. Determine likelihoods of the haplotypes given the read data
    4. Assign sample genotypes
java"/home/brb/SRP049647/outputvc/tmpJava" -Xmx10g 
  -jar /opt/SeqTools/bin/gatk/GenomeAnalysisTK.jar 
  -T HaplotypeCaller --genotyping_mode DISCOVERY 
  -R /home/brb/igenomes/Homo_sapiens/UCSC/hg38/Sequence/BWAIndex/../WholeGenomeFasta/genome.fa 
  -I recal.bam  
  -stand_call_conf 30 
  -o /home/brb/SRP049647/outputvc/BMBC2_liver3_IMPACT_raw.vcf 
  -nct 11

Variant call by VarDict

Random results and downsampling

Why expected variants are not called

Why is HaplotypeCaller dropping half of my reads?

Missing mapping quality score

On GSE48215 case, I got 34 missing MQ from bwa + gatk but no missing MQ from bwa + samtools???


idx file

It is a binary file. It is one of two ouptut from the GATK's Haplotype calling.

Unfortunately there is no documentation about its spec. It can be generated if VCF files can be validated by ValidateVariants

how to deal with errors

$ grep -n ERROR swarm_58155698_0.e --color
513:ERROR StatusLogger Unable to create class org.apache.logging.log4j.core.impl.Log4jContextFactory specified in jar:file:/usr/local/apps/GATK/3.8-0/GenomeAnalysisTK.jar!/META-INF/
514:ERROR StatusLogger Log4j2 could not find a logging implementation. Please add log4j-core to the classpath. Using SimpleLogger to log to the console...
535:##### ERROR ------------------------------------------------------------------------------------------
536:##### ERROR A USER ERROR has occurred (version 3.8-0-ge9d806836): 
537:##### ERROR
538:##### ERROR This means that one or more arguments or inputs in your command are incorrect.
539:##### ERROR The error message below tells you what is the problem.
540:##### ERROR
541:##### ERROR If the problem is an invalid argument, please check the online documentation guide
542:##### ERROR (or rerun your command with --help) to view allowable command-line arguments for this tool.
543:##### ERROR
544:##### ERROR Visit our website and forum for extensive documentation and answers to 
545:##### ERROR commonly asked questions
546:##### ERROR
547:##### ERROR Please do NOT post this error to the GATK forum unless you have really tried to fix it yourself.
548:##### ERROR
549:##### ERROR MESSAGE: SAM/BAM/CRAM file [email protected]a7427f9 is malformed. Please see more information. Error details: BAM file has a read with mismatching number of bases and base qualities. Offender: homo_simulated_Error0_Mu0-j1-chr2-r10408427 [55 bases] [0 quals]. You can use --defaultBaseQualities to assign a default base quality for all reads, but this can be dangerous in you don't know what you are doing.
550:##### ERROR -----------------------------------------------------------------------------
##### ERROR A USER ERROR has occurred (version 3.8-0-ge9d806836):
##### ERROR
##### ERROR This means that one or more arguments or inputs in your command are incorrect.
##### ERROR The error message below tells you what is the problem.
##### ERROR
##### ERROR If the problem is an invalid argument, please check the online documentation guide
##### ERROR (or rerun your command with --help) to view allowable command-line arguments for this tool.
##### ERROR
##### ERROR Visit our website and forum for extensive documentation and answers to
##### ERROR commonly asked questions
##### ERROR
##### ERROR Please do NOT post this error to the GATK forum unless you have really tried to fix it yourself.
##### ERROR
##### ERROR MESSAGE: SAM/BAM/CRAM file [email protected]c0bf8f4 appears to be using the wrong encoding for quality scores: we encountered an extremely high quality score (68) with BAQ correction factor of 4. Please see for more details and options related to this error.

After adding '--fix_misencoded_quality_scores' does not help.

#### ERROR MESSAGE: Bad input: while fixing mis-encoded base qualities we encountered a read that was correctly encoded; we cannot handle such a mixture of reads so unfortunately the BAM must be fixed with some other tool


Running index is very quick (2 minutes for hg19). The following commands will generate a file <hg19index> which is about 7.8GB in size.

$ sinteractive --mem=32g -c 16
$ module load novocraft
$ novoindex hg19index $HOME/igenomes/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa

# novoindex (3.8) - Universal k-mer index constructor.
# (C) 2008 - 2011 NovoCraft Technologies Sdn Bhd
# novoindex hg19index /home/limingc/igenomes/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa 
# Creating 55 indexing threads.
# Building with 14-mer and step of 2 bp.
tcmalloc: large alloc 1073750016 bytes == 0x13f8000 @  0x4008e4 0x56c989 0x40408c 0x40127b 0x4d20bb 0x402845
tcmalloc: large alloc 8344305664 bytes == 0x41402000 @  0x4008e4 0x56d6d3 0x40423a 0x40127b 0x4d20bb 0x402845
# novoindex construction dT = 116.1s
# Index memory size   7.771Gbyte.
# Done.


The program gave different errors for some real datasets downloaded from GEO.

A small test data included in the software works.

[email protected]:~/github$ sudo apt-get update
[email protected]:~/github$ sudo apt-get install cmake
[email protected]:~/github$ git clone --recursive git://
[email protected]:~/github$ cd freebayes/
[email protected]:~/github/freebayes$ make
[email protected]:~/github/freebayes$ make test   # Got some errors. So don't worry to 'make test'
[email protected]:~/github/freebayes/test/tiny$ ../../bin/freebayes -f q.fa NA12878.chr22.tiny.bam > tmp.vcf
[email protected]:~/github/freebayes/test/tiny$ ls -lt
total 360
-rw-rw-r-- 1 brb brb  15812 Oct  9 10:52 tmp.vcf
-rw-rw-r-- 1 brb brb 287213 Oct  9 09:33 NA12878.chr22.tiny.bam
-rw-rw-r-- 1 brb brb     96 Oct  9 09:33 NA12878.chr22.tiny.bam.bai
-rw-rw-r-- 1 brb brb  16307 Oct  9 09:33 NA12878.chr22.tiny.giab.vcf
-rw-rw-r-- 1 brb brb  12565 Oct  9 09:33 q.fa
-rw-rw-r-- 1 brb brb     16 Oct  9 09:33 q.fa.fai
-rw-rw-r-- 1 brb brb   2378 Oct  9 09:33 q_spiked.vcf.gz
-rw-rw-r-- 1 brb brb     91 Oct  9 09:33 q_spiked.vcf.gz.tbi
-rw-rw-r-- 1 brb brb   4305 Oct  9 09:33 q.vcf.gz
-rw-rw-r-- 1 brb brb    102 Oct  9 09:33 q.vcf.gz.tbi
[email protected]:~/github/freebayes/test/tiny$ ~/github/samtools/samtools view NA12878.chr22.tiny.bam | wc -l
[email protected]:~/github/freebayes/test/tiny$ wc -l tmp.vcf
76 tmp.vcf
[email protected]:~/github/freebayes/test/tiny$ wc -l q.fa
207 q.fa

To debug the code, we can

../../bin/freebayes -f q.fa NA12878.chr22.tiny.bam > tmpFull.out 2>&1

Then compare tmp.vcf and tmpFull.out files. We see

  • total sites: 12280
  • the first variant call happens at position 186. Look at tmpFull.out file, we see most of positions just show 3 lines in the output but variants like position 186 show a lot of output (line 643 to 736).
  • the output 'processing position XXX' was generated from AlleleParser:toNextPosition() which was called by AlleleParser::getNextAlleles() which was called by freebayes.cpp::main() line 126, the while() loop.

freeBayes vs HaplotypeCaller



This toolset can be used to perform the following operations on VCF files:

  • Filter out specific variants
  • Compare files
  • Summarize variants
  • Convert to different file types
  • Validate and merge files
  • Create intersections and subsets of variants
wget \
     -o vcftools_0.1.12b.tar.gz
tar -xzvf vcftools_0.1.12b.tar.gz
sudo mv vcftools_0.1.12b /opt/RNA-Seq/bin/
export PERL5LIB=/opt/RNA-Seq/bin/vcftools_0.1.12b/perl/
export PATH=$PATH:/opt/RNA-Seq/bin/vcftools_0.1.12b/bin
ls bin
# fill-aa       vcf-annotate   vcf-convert      vcf-phased-join   vcf-subset
# fill-an-ac    vcf-compare    vcf-fix-ploidy   vcf-query         vcftools
# fill-fs       vcf-concat     vcf-indel-stats  vcf-shuffle-cols  vcf-to-tab
# fill-ref-md5  vcf-consensus  vcf-isec         vcf-sort          vcf-tstv
# man1          vcf-contrast   vcf-merge        vcf-stats         vcf-validator

Some example

$ cd ~/SRP032789
$ vcftools --vcf GSM1261016_IP2-50_var.flt.vcf

VCFtools - v0.1.12b
(C) Adam Auton and Anthony Marcketta 2009

Parameters as interpreted:
	--vcf GSM1261016_IP2-50_var.flt.vcf

After filtering, kept 1 out of 1 Individuals
After filtering, kept 193609 out of a possible 193609 Sites
Run Time = 1.00 seconds

$ wc -l GSM1261016_IP2-50_var.flt.vcf
193636 GSM1261016_IP2-50_var.flt.vcf

$ vcf-indel-stats < GSM1261016_IP2-50_var.flt.vcf > out.txt
Use of uninitialized value in pattern match (m//) at /opt/RNA-Seq/bin/vcftools_0.1.12b/bin/vcf-indel-stats line 49.
Use of uninitialized value in concatenation (.) or string at /opt/RNA-Seq/bin/vcftools_0.1.12b/bin/vcf-indel-stats line 49.
<: No such file or directory at /opt/RNA-Seq/bin/vcftools_0.1.12b/bin/vcf-indel-stats line 18.
	main::error('<: No such file or directory') called at /opt/RNA-Seq/bin/vcftools_0.1.12b/bin/vcf-indel-stats line 50
	main::init_regions('HASH(0xd77cb8)') called at /opt/RNA-Seq/bin/vcftools_0.1.12b/bin/vcf-indel-stats line 71
	main::do_stats('HASH(0xd77cb8)') called at /opt/RNA-Seq/bin/vcftools_0.1.12b/bin/vcf-indel-stats line 9

To compare two vcf files, see

./vcftools --vcf input_data.vcf --diff other_data.vcf --out compare

Online course on Variant calling

  • edX: HarvardX: PH525.6x Case Study: Variant Discovery and Genotyping. Course notes is at their Github page.


a cloud platform for genomic variant discovery and interpretation


PCA of genomic variant data across one chromosome from 2,504 people from the 1000 genomes project

Variant Annotation

See also the paper A survey of tools for variant analysis of next-generation genome sequencing data.

Awesome-cancer-variant-databases - A community-maintained repository of cancer clinical knowledge bases and databases focused on cancer variants.


SNPlocs data R package for Human. Some clarification about SNPlocs.Hsapiens.dbSNP.20120608 package.

> library(BSgenome)
> available.SNPs()
[1] "SNPlocs.Hsapiens.dbSNP141.GRCh38"    
[2] "SNPlocs.Hsapiens.dbSNP142.GRCh37"    
[3] "SNPlocs.Hsapiens.dbSNP.20090506"     
[4] "SNPlocs.Hsapiens.dbSNP.20100427"     
[5] "SNPlocs.Hsapiens.dbSNP.20101109"     
[6] "SNPlocs.Hsapiens.dbSNP.20110815"     
[7] "SNPlocs.Hsapiens.dbSNP.20111119"     
[8] "SNPlocs.Hsapiens.dbSNP.20120608"     
[9] "XtraSNPlocs.Hsapiens.dbSNP141.GRCh38"

Query dbSNP

An example:

sudo tar jxf bcftools-1.2.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/bcftools-1.2/
sudo make

sudo tar jxf htslib-1.2.1.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/htslib-1.2.1/
sudo make  # create tabix, htsfile, bgzip commands

export bdge_bowtie_PATH=/opt/RNA-Seq/bin/bowtie2-2.2.1
export bdge_tophat_PATH=/opt/RNA-Seq/bin/tophat-2.0.11.Linux_x86_64
export bdge_samtools_PATH=/opt/RNA-Seq/bin/samtools-0.1.19
export PATH=$bdge_bowtie_PATH:$bdge_samtools_PATH:$bdge_tophat_PATH:$PATH
export PATH=/opt/RNA-Seq/bin/bcftools-1.2/:$PATH
export PATH=/opt/RNA-Seq/bin/htslib-1.2.1/:$PATH

cd TNBC1
mv ~/Downloads/common_all_20150603.vcf.gz* .
bgzip -c var.raw.vcf > var.raw.vcf.gz
tabix var.raw.vcf.gz
bcftools annotate -c ID -a common_all_20150603.vcf.gz var.raw.vcf.gz > var_annot.vcf

Any found in dbSNP?

grep -c 'rs[0-9]' raw_snps.vcf


ANNOVAR and the web based interface to ANNOVAR wANNOVAR. ANNOVAR can annotate genetic variants using

  • Gene-based annotation
  • Region-based annotation
  • Filter-based annotation
  • Other functionalities

Note that

  • annovarR R package. The wrapper functions of annovarR unified the interface of many published annotation tools, such as VEP, ANNOVAR, vcfanno and AnnotationDbi.
  • It is correct to use the original download link from the email to download the latest version.
  • annovar folder needs to be placed under a directory with write permission
  • If we run annovar with a new reference genome, the code will need to download some database. When annovar is downloading the database, the cpu is resting so the whole process looks idling.
  • Annovar will print out a warning with information about changes if there is a new version available.
  • BRB-SeqTools (grep "\.pl" preprocessgui/*.*) uses (5), (2), (4), (1).
  • In nih/biowulf, it creates $ANNOVAR_HOME and $ANNOVAR_DATA environment variables.
  • To check the version of my local copy
  • Contents of annovar
[email protected] ~ $ ls -l ~/annovar/
total 476
-rwxr-xr-x 1 brb brb 212090 Mar  7 14:59
-rwxr-xr-x 1 brb brb  13589 Mar  7 14:59
-rwxr-xr-x 1 brb brb 166582 Mar  7 14:59
drwxr-xr-x 2 brb brb   4096 Jun 18  2015 example
drwxr-xr-x 3 brb brb   4096 Mar 21 09:59 humandb
-rwxr-xr-x 1 brb brb  19419 Mar  7 14:59
-rwxr-xr-x 1 brb brb  34682 Mar  7 14:59
-rwxr-xr-x 1 brb brb  21774 Mar  7 14:59
[email protected] ~ $ ls -lh ~/annovar/humandb | head
total 36G
-rw-r--r-- 1 brb brb  927 Mar 21 09:59 annovar_downdb.log
drwxr-xr-x 2 brb brb 4.0K Jun 18  2015 genometrax-sample-files-gff
-rw-r--r-- 1 brb brb  20K Mar  7 14:59 GRCh37_MT_ensGeneMrna.fa
-rw-r--r-- 1 brb brb 3.1K Mar  7 14:59 GRCh37_MT_ensGene.txt
-rw-r--r-- 1 brb brb 1.4G Dec 15  2014 hg19_AFR.sites.2014_10.txt
-rw-r--r-- 1 brb brb  87M Dec 15  2014 hg19_AFR.sites.2014_10.txt.idx
-rw-r--r-- 1 brb brb 2.8G Dec 15  2014 hg19_ALL.sites.2014_10.txt
-rw-r--r-- 1 brb brb  89M Dec 15  2014 hg19_ALL.sites.2014_10.txt.idx
-rw-r--r-- 1 brb brb 978M Dec 15  2014 hg19_AMR.sites.2014_10.txt

SnpEff & SnpSift

SnpEff: Genetic variant annotation and effect prediction toolbox

  1. Input: vcf & reference genome database (eg GRCh38.79).
  2. Output: vcf & <snpEff_summary.html> & < snpEff_genes.txt> files.
sudo unzip -d /opt/RNA-Seq/bin
export PATH=/opt/RNA-Seq/bin/snpEff/:$PATH

# Next we want to download snpEff database.
# 1. Need to pay attention the database is snpEff version dependent
# 2. Instead of using the command line (very slow < 1MB/s),
#   java -jar /opt/RNA-Seq/bin/snpEff/snpEff.jar databases | grep GRCh
#   java -jar /opt/RNA-Seq/bin/snpEff/snpEff.jar download GRCh38.79
# we just go to the file using the the web browser
#  # 525MB
# 3. the top folder of the zip file is called 'data'. We will need to unzip it to the snpEff directory.
#   That is, snpEff +- data
#                   |- examples
#                   |- galaxy
#                   +- scripts
mv ~/Downloads/ .
sudo java -Xmx4G -jar /opt/RNA-Seq/bin/snpEff/snpEff.jar \
                      -i vcf -o vcf GRCh38.79 var_annot.vcf > var_annot_snpEff.vcf

[email protected] ~ $ ls -l /opt/SeqTools/bin/snpEff/
total 44504
drwxrwxr-x 5 brb brb     4096 May  5 11:24 data
drwxr-xr-x 2 brb brb     4096 Feb 17 16:37 examples
drwxr-xr-x 3 brb brb     4096 Feb 17 16:37 galaxy
drwxr-xr-x 3 brb brb     4096 Feb 17 16:37 scripts
-rw-r--r-- 1 brb brb  6138594 Dec  5 10:49 snpEff.config
-rw-r--r-- 1 brb brb 20698856 Dec  5 10:49 snpEff.jar
-rw-r--r-- 1 brb brb 18712032 Dec  5 10:49 SnpSift.jar

Output file (compare cosmic_dbsnp_rem.vcf and snpeff_anno.vcf at here):

##SnpEffVersion="4.2 (build 2015-12-05), by Pablo Cingolani"
##SnpEffCmd="SnpEff  -no-downstream -no-upstream ....
##INFO=<ID=ANN,Number=.,Type=String,Description="Functional annotations: 'Allele | Annotation | Annotation_Impact | Gene_Name | Gene_ID | Feature_Type | Feature_ID |...
##INFO=<ID=LOF,Number=.,Type=String,Description="Predicted loss of function effects for this variant. ...
##INFO=<ID=NMD,Number=.,Type=String,Description="Predicted nonsense mediated decay effects for this variant. ...

SnpSift: SnpSift helps filtering and manipulating genomic annotated files

Once you annotated your files using SnpEff, you can use SnpSift to help you filter large genomic datasets in order to find the most significant variants

SnpSift filter

cat "$outputDir/tmp/$tmpfd/snpeff_anno.vcf" | \
    java -jar "$seqtools_snpeff/SnpSift.jar" \
      filter "(ANN[*].BIOTYPE = 'protein_coding') | (ANN[*].EFFECT has 'splice')"  \
    > "$outputDir/tmp/$tmpfd/snpeff_proteincoding.vcf"

the output file (compare snpeff_anno.vcf and snpeff_proteincoding.vcf at here]

  • the header will add 3 lines
##SnpSiftVersion="SnpSift 4.2 (build 2015-12-05), by Pablo Cingolani"
##SnpSiftCmd="SnpSift filter '(ANN[*].BIOTYPE = 'protein_coding') | (ANN[*].EFFECT has 'splice')'"
##FILTER=<ID=SnpSift,Description="SnpSift 4.2 ...
  • KEEP variants satisfying the filter criterion (In the ANN field, BIOTYPE=protein_coding and EFFECT=splice). So it will reduce the variants size. No more fields are added.

SnpSift dbNSFP

java -jar "$seqtools_snpeff/SnpSift.jar" dbNSFP -f $dbnsfpField \
    -v -db "$dbnsfpFile" "$outputDir/tmp/$tmpfd/nonsyn_splicing2.vcf" \
    > "$outputDir/tmp/$tmpfd/nonsyn_splicing_dbnsfp.vcf"

Compare nonsyn_splicing2.vcf and nonsyn_splicing_dbnsfp.vcf at github output file

  • the header adds 29 lines.
##SnpSiftCmd="SnpSift dbnsfp -f SIFT_score,SIFT_pred, ...
##INFO=<ID=dbNSFP_GERP___RS,Number=A,Type=Float,Description="Field 'GERP++_RS' from dbNSFP">
##INFO=<ID=dbNSFP_CADD_phred,Number=A,Type=Float,Description="Field 'CADD_phred' from dbNSFP">
  • the INFO field will add
dbNSFP_CADD_phred=2.276;dbNSFP_CADD_raw=-0.033441;dbNSFP_FATHMM_pred=.,.,T; ...

SnpSift extractFields

java -jar "$seqtools_snpeff/SnpSift.jar" extractFields \
    -s "," -e "." "$outputDir/tmp/$tmpfd/nonsyn_splicing_dbnsfp.vcf" CHROM POS ID REF ALT "ANN[0].EFFECT" "ANN[0].IMPACT" "ANN[0].GENE" "ANN[0].GENEID" "ANN[0].FEATURE" "ANN[0].FEATUREID" "ANN[0].BIOTYPE" "ANN[0].HGVS_C" "ANN[0].HGVS_P" dbNSFP_SIFT_score dbNSFP_SIFT_pred dbNSFP_Polyphen2_HDIV_score dbNSFP_Polyphen2_HDIV_pred dbNSFP_Polyphen2_HVAR_score dbNSFP_Polyphen2_HVAR_pred dbNSFP_LRT_score dbNSFP_LRT_pred dbNSFP_MutationTaster_score dbNSFP_MutationTaster_pred dbNSFP_MutationAssessor_score dbNSFP_MutationAssessor_pred dbNSFP_FATHMM_score dbNSFP_FATHMM_pred dbNSFP_PROVEAN_score dbNSFP_PROVEAN_pred dbNSFP_VEST3_score dbNSFP_CADD_raw dbNSFP_CADD_phred dbNSFP_MetaSVM_score dbNSFP_MetaSVM_pred dbNSFP_MetaLR_score dbNSFP_MetaLR_pred dbNSFP_GERP___NR dbNSFP_GERP___RS dbNSFP_phyloP100way_vertebrate dbNSFP_phastCons100way_vertebrate dbNSFP_SiPhy_29way_logOdds \
    > "$outputDir/tmp/$tmpfd/annoTable.txt"

The output file will

  • remove header from the input VCF file
  • break the INFO field into several columns; Only the columns we specify in the command will be kept.


$ ls -l /opt/SeqTools/bin/snpEff/
total 44504
drwxrwxr-x 5 brb brb     4096 May  5 11:24 data
drwxr-xr-x 2 brb brb     4096 Feb 17 16:37 examples
drwxr-xr-x 3 brb brb     4096 Feb 17 16:37 galaxy
drwxr-xr-x 3 brb brb     4096 Feb 17 16:37 scripts
-rw-r--r-- 1 brb brb  6138594 Dec  5 10:49 snpEff.config
-rw-r--r-- 1 brb brb 20698856 Dec  5 10:49 snpEff.jar
-rw-r--r-- 1 brb brb 18712032 Dec  5 10:49 SnpSift.jar

$ ls -l /opt/SeqTools/bin/snpEff/data
total 12
drwxr-xr-x 2 brb brb 4096 May  5 11:24 GRCh38.82
drwxrwxr-x 2 brb brb 4096 Feb  5 09:28 hg19
drwxr-xr-x 2 brb brb 4096 Mar  8 09:44 hg38


The following code fixed some typos on biowulf website.

# Make sure the database (GRCH37.75 in this case) exists 
ls /usr/local/apps/snpEff/4.2/data
# CanFam3.1.75  Felis_catus_6.2.75  GRCh37.75    GRCh38.82	 GRCm38.81  GRCz10.82  hg38
# CanFam3.1.81  Felis_catus_6.2.81  GRCh37.GTEX  GRCh38.p2.RefSeq  GRCm38.82  hg19       hg38kg
# CanFam3.1.82  Felis_catus_6.2.82  GRCh38.81    GRCm38.75	 GRCz10.81  hg19kg     Zv9.75
# Use snpEff to annotate against GRCh37.75
snpEff -v -lof -motif -hgvs -nextProt GRCh37.75 protocols/ex1.vcf > ex1.eff.vcf # 25 minutes
          # create ex1.eff.vcf (475MB), snpEff_genes.txt (2.5MB) and snpEff_summary.html (22MB)
# Use SnpSift to pull out 'HIGH IMPACT' or 'MODERATE IMPACT' variants
cat ex1.eff.vcf | \
  java -jar $SNPSIFT_JAR filter "((EFF[*].IMPACT = 'HIGH') | (EFF[*].IMPACT = 'MODERATE'))"  \
  > ex1.filtered.vcf
          # ex1.filtered.vcf (8.2MB), 2 minutes
# Use SnpSift to annotate against the dbNSFP database
java -jar $SNPSIFT_JAR dbnsfp -v -db /fdb/dbNSFP2/dbNSFP2.9.txt.gz ex1.eff.vcf \
  > file.annotated.vcf
          # file.annotated.vcf (479 MB), 11 minutes

and the output

$ ls -lth | head
total 994M
-rw-r----- 1  479M May 15 11:47 file.annotated.vcf
-rw-r----- 1  8.2M May 15 11:31 ex1.filtered.vcf
-rw-r----- 1  476M May 15 10:31 ex1.eff.vcf
-rw-r----- 1  2.5M May 15 10:30 snpEff_genes.txt
-rw-r----- 1   22M May 15 10:30 snpEff_summary.html
lrwxrwxrwx 1    39 May 15 10:01 protocols -> /usr/local/apps/snpEff/4.2/../protocols

Strange error

  • Run 1: Error
$ java -Xmx4G -jar "$seqtools_snpeff/snpEff.jar" -canon -no-downstream -no-upstream -no-intergenic -no-intron -no-utr \
    -noNextProt -noMotif $genomeVer -s "$outputDir/tmp/$tmpfd/annodbsnpRemove.html" "$outputDir/tmp/$tmpfd/cosmic_dbsnp_rem.vcf"

java.lang.RuntimeException: 	ERROR: Cannot read file '/opt/SeqTools/bin/snpEff/./data/hg38/snpEffectPredictor.bin'.
	You can try to download the database by running the following command:
		java -jar snpEff.jar download hg38

	at ca.mcgill.mcb.pcingola.snpEffect.SnpEffectPredictor.load(
	at ca.mcgill.mcb.pcingola.snpEffect.Config.loadSnpEffectPredictor(
	at ca.mcgill.mcb.pcingola.snpEffect.commandLine.SnpEff.loadDb(
	at ca.mcgill.mcb.pcingola.snpEffect.commandLine.SnpEff.main(

	There is a new SnpEff version available: 
		Version      : 4.3P
		Release date : 2017-06-06
		Download URL :
  • Run 2: OK
$ java -Xmx4G -jar "$seqtools_snpeff/snpEff.jar" -canon -no-downstream -no-upstream -no-intergenic -no-intron -no-utr \
    -noNextProt -noMotif $genomeVer -s "$outputDir/tmp/$tmpfd/annodbsnpRemove.html" "$outputDir/tmp/$tmpfd/cosmic_dbsnp_rem.vcf"

##FILTER=<ID=PASS,Description="All filters passed">

ANNOVAR and SnpEff examples








DNA Sequencing analysis on Artemis Mapping and Variant Calling Tracy Chew et al

Web tools



SPDI: Data Model for Variants and Applications at NCBI

De novo genome assembly

Single Cell RNA-Seq

How many cells are in the human body?

30-40 trillion (1012) cells

scone package: normalization

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq

monocle package

sincell package


SCell – integrated analysis of single-cell RNA-seq data


Detection of high variability in gene expression from single-cell RNA-seq profiling. Two mouse scRNA-seq data sets were obtained from Gene Expression Omnibus (GSE65525 and GSE60361).


Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization


Splatter: Simulation Of Single-Cell RNA Sequencing Data


Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R


DEsingle – detecting three types of differential expression in single-cell RNA-seq data

RNA-Seq analysis interface

Co-expression in RNA-Seq

Monitor Software Version Change

Circos Plot

Circos is a popular tool for summarizing genomic events in a tumor genome.

Cancer & Mutation

My Cancer Genome

CIViC - Clinical Interpretations of Variants in Cancer


R and Bioconductor packages




Bioinstaller: A comprehensive R package to construct interactive and reproducible biological data analysis applications based on the R platform. Package on CRAN.

Some workflows

RNA-Seq workflow

Gene-level exploratory analysis and differential expression. A non stranded-specific and paired-end rna-seq experiment was used for the tutorial.

       STAR       Samtools         Rsamtools
fastq -----> sam ----------> bam  ----------> bamfiles  -|
                                                          \  GenomicAlignments       DESeq2 
                                                           --------------------> se --------> dds
      GenomicFeatures         GenomicFeatures             /        (SummarizedExperiment) (DESeqDataSet)
  gtf ----------------> txdb ---------------> genes -----|

Sequence analysis

library(ShortRead) or library(Biostrings) (QA)
gtf + library(GenomicFeatures) or directly library(TxDb.Scerevisiae.UCSC.sacCer2.sgdGene) (gene information)
GenomicRanges::summarizeOverlaps or GenomicRanges::countOverlaps(count)
edgeR or DESeq2 (gene expression analysis)
library(org.Sc.sgd.db) or library(biomaRt)

Accessing Annotation Data

Use microarray probe, gene, pathway, gene ontology, homology and other annotations. Access GO, KEGG, NCBI, Biomart, UCSC, vendor, and other sources.

library(  # Sample OrgDb Workflow
library("hgu95av2.db") # Sample ChipDb Workflow
library(TxDb.Hsapiens.UCSC.hg19.knownGene) # Sample TxDb Workflow
library(Homo.sapiens)  # Sample OrganismDb Workflow
library(AnnotationHub) # Sample AnnotationHub Workflow
library("biomaRt")     # Using biomaRt
library(BSgenome.Hsapiens.UCSC.hg19) # BSgenome packages
Object type example package name contents
OrgDb gene based information for Homo sapiens
TxDb TxDb.Hsapiens.UCSC.hg19.knownGene transcriptome ranges for Homo sapiens
OrganismDb Homo.sapiens composite information for Homo sapiens
BSgenome BSgenome.Hsapiens.UCSC.hg19 genome sequence for Homo sapiens

RNA-Seq Data Analysis using R/Bioconductor


GenomicDataCommons package


  1. The TCGA data such as TCGA-LUAD are not part of clinical trials (described here).
  2. Each patient has 4 categories data and the 'case_id' is common to them:
    • demographic: gender, race, year_of_birth, year_of_death
    • diagnoses: tumor_stage, age_at_diagnosis, tumor_grade
    • exposures: cigarettes_per_day, alcohol_history, years_smoked, bmi, alcohol_intensity, weight, height
    • main: disease_type, primary_site
  3. The original download (clinical.tsv file) data contains a column 'treatment_or_therapy' but it has missing values for all patients.





  • Differential expression analyses for RNA-sequencing and microarray studies
  • Case Study using a Bioconductor R pipeline to analyze RNA-seq data (this is linked from limma package user guide). Here we illustrate how to use two Bioconductor packages - Rsubread' and limma - to perform a complete RNA-seq analysis, including Subread'Bold text read mapping, featureCounts read summarization, voom normalization and limma differential expresssion analysis.
  • Unbalanced data, non-normal data, Bartlett's test for equal variance across groups and SAM tests (assumes equal variances just like limma). See this post.


Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package.


Base classes, functions, and methods for representation of high-throughput, short-read sequencing data.


The Rsamtools package provides an interface to BAM files.

The main purpose of the Rsamtools package is to import BAM files into R. Rsamtools also provides some facility for file access such as record counting, index file creation, and filtering to create new files containing subsets of the original. An important use case for Rsamtools is as a starting point for creating R objects suitable for a diversity of work flows, e.g., AlignedRead objects in the ShortRead package (for quality assessment and read manipulation), or GAlignments objects in GenomicAlignments package (for RNA-seq and other applications). Those desiring more functionality are encouraged to explore samtools and related software efforts

This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities (see 'LICENCE') for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files.


IRanges is a fundamental package (see how many packages depend on it) to other packages like GenomicRanges, GenomicFeatures and GenomicAlignments. The package defines the IRanges class.

The plotRanges() function given in the 'An Introduction to IRanges' vignette shows how to draw an IRanges object.

If we want to make the same plot using the ggplot2 package, we can follow the example in this post. Note that disjointBins() returns a vector the bin number for each bins counting on the y-axis.


The example is obtained from ?IRanges::flank.

ir3 <- IRanges(c(2,5,1), c(3,7,3))
# IRanges of length 3
#     start end width
# [1]     2   3     2
# [2]     5   7     3
# [3]     1   3     3

flank(ir3, 2)
#     start end width
# [1]     0   1     2
# [2]     3   4     2
# [3]    -1   0     2
# Note: by default flank(ir3, 2) = flank(ir3, 2, start = TRUE, both=FALSE)
# For example, [2,3] => [2,X] => (..., 0, 1, 2) => [0, 1]
#                                     == ==

flank(ir3, 2, start=FALSE)
#     start end width
# [1]     4   5     2
# [2]     8   9     2
# [3]     4   5     2
# For example, [2,3] => [X,3] => (..., 3, 4, 5) => [4,5]
#                                        == == 

flank(ir3, 2, start=c(FALSE, TRUE, FALSE))
#     start end width
# [1]     4   5     2
# [2]     3   4     2
# [3]     4   5     2
# Combine the ideas of the previous 2 cases.

flank(ir3, c(2, -2, 2))
#     start end width
# [1]     0   1     2
# [2]     5   6     2
# [3]    -1   0     2
# The original statement is the same as flank(ir3, c(2, -2, 2), start=T, both=F)
# For example, [5, 7] => [5, X] => ( 5, 6) => [5, 6]
#                                   == ==

flank(ir3, -2, start=F)
#     start end width
# [1]     2   3     2
# [2]     6   7     2
# [3]     2   3     2
# For example, [5, 7] => [X, 7] => (..., 6, 7) => [6, 7]
#                                       == ==

flank(ir3, 2, both = TRUE)
#     start end width
# [1]     0   3     4
# [2]     3   6     4
# [3]    -1   2     4
# The original statement is equivalent to flank(ir3, 2, start=T, both=T)
# (From the manual) If both = TRUE, extends the flanking region width positions into the range. 
#        The resulting range thus straddles the end point, with width positions on either side.
# For example, [2, 3] => [2, X] => (..., 0, 1, 2, 3) => [0, 3]
#                                             ==
#                                       == == == ==

flank(ir3, 2, start=FALSE, both=TRUE)
#     start end width
# [1]     2   5     4
# [2]     6   9     4
# [3]     2   5     4
# For example, [2, 3] => [X, 3] => (..., 2, 3, 4, 5) => [4, 5]
#                                          ==
#                                       == == == ==

Both IRanges and GenomicRanges packages provide the flank function.

Flanking region is also a common term in High-throughput sequencing. The IGV user guide also has some option related to flanking.

  • General tab: Feature flanking regions (base pairs). IGV adds the flank before and after a feature locus when you zoom to a feature, or when you view gene/loci lists in multiple panels.
  • Alignments tab: Splice junction track options. The minimum amount of nucleotide coverage required on both sides of a junction for a read to be associated with the junction. This affects the coverage of displayed junctions, and the display of junctions covered only by reads with small flanking regions.



GenomicRanges depends on IRanges package. See the dependency diagram below.

GenomicFeatues ------- GenomicRanges -+- IRanges -- BioGenomics
                         |            +
                   +-----+            +- GenomeInfoDb
                   |                      |
GenomicAlignments  +--- Rsamtools --+-----+
                                    +--- Biostrings

The package defines some classes

  • GRanges
  • GRangesList
  • GAlignments
  • SummarizedExperiment: it has the following slots - expData, rowData, colData, and assays. Accessors include assays(), assay(), colData(), expData(), mcols(), ... The mcols() method is defined in the S4Vectors package.

(As of Jan 6, 2015) The introduction in GenomicRanges vignette mentions the GAlignments object created from a 'BAM' file discarding some information such as SEQ field, QNAME field, QUAL, MAPQ and any other information that is not needed in its document. This means that multi-reads don't receive any special treatment. Also pair-end reads will be treated as single-end reads and the pairing information will be lost. This might change in the future.


Counting reads with summarizeOverlaps vignette


fls <- list.files(system.file("extdata", package="GenomicAlignments"),
    recursive=TRUE, pattern="*bam$", full=TRUE)

features <- GRanges(
    seqnames = c(rep("chr2L", 4), rep("chr2R", 5), rep("chr3L", 2)),
    ranges = IRanges(c(1000, 3000, 4000, 7000, 2000, 3000, 3600, 4000, 
        7500, 5000, 5400), width=c(rep(500, 3), 600, 900, 500, 300, 900, 
        300, 500, 500)), "-",
    group_id=c(rep("A", 4), rep("B", 5), rep("C", 2)))

# GRanges object with 11 ranges and 1 metadata column:
#       seqnames       ranges strand   |    group_id
#          <Rle>    <IRanges>  <Rle>   | <character>
#   [1]    chr2L [1000, 1499]      -   |           A
#   [2]    chr2L [3000, 3499]      -   |           A
#   [3]    chr2L [4000, 4499]      -   |           A
#   [4]    chr2L [7000, 7599]      -   |           A
#   [5]    chr2R [2000, 2899]      -   |           B
#   ...      ...          ...    ... ...         ...
#   [7]    chr2R [3600, 3899]      -   |           B
#   [8]    chr2R [4000, 4899]      -   |           B
#   [9]    chr2R [7500, 7799]      -   |           B
#  [10]    chr3L [5000, 5499]      -   |           C
#  [11]    chr3L [5400, 5899]      -   |           C
#  -------
#  seqinfo: 3 sequences from an unspecified genome; no seqlengths
# class: SummarizedExperiment 
# dim: 11 2 
# exptData(0):
# assays(1): counts
# rownames: NULL
# rowData metadata column names(1): group_id
# colnames(2): sm_treated1.bam sm_untreated1.bam
# colData names(0):

#       sm_treated1.bam sm_untreated1.bam
#  [1,]               0                 0
#  [2,]               0                 0
#  [3,]               0                 0
#  [4,]               0                 0
#  [5,]               5                 1
#  [6,]               5                 0
#  [7,]               2                 0
#  [8,]             376               104
#  [9,]               0                 0
# [10,]               0                 0
# [11,]               0                 0

Pasilla data. Note that the bam files are not clear where to find them. According to the message, we can download SAM files first and then convert them to BAM files by samtools (Not verify yet).

samtools view -h -o outputFile.bam inputFile.sam

A modified R code that works is

### code chunk number 11: gff (eval = FALSE)
fl <- paste0("",
gffFile <- file.path(tempdir(), basename(fl))
download.file(fl, gffFile)
gff0 <- import(gffFile, asRangedData=FALSE)

### code chunk number 12: gff_parse (eval = FALSE)
idx <- mcols(gff0)$source == "protein_coding" & 
           mcols(gff0)$type == "exon" & 
           seqnames(gff0) == "4"
gff <- gff0[idx]
## adjust seqnames to match Bam files
seqlevels(gff) <- paste("chr", seqlevels(gff), sep="")
chr4genes <- split(gff, mcols(gff)$gene_id)

### code chunk number 12: gff_parse (eval = FALSE)

# fls <- c("untreated1_chr4.bam", "untreated3_chr4.bam")
fls <- list.files(system.file("extdata", package="pasillaBamSubset"),
     recursive=TRUE, pattern="*bam$", full=TRUE)
path <- system.file("extdata", package="pasillaBamSubset")
bamlst <- BamFileList(fls)
genehits <- summarizeOverlaps(chr4genes, bamlst, mode="Union") # SummarizedExperiment object

### code chunk number 15: pasilla_exoncountset (eval = FALSE)

expdata = MIAME(
              name="pasilla knockdown",
              lab="Genetics and Developmental Biology, University of 
                  Connecticut Health Center",
              contact="Dr. Brenton Graveley",
              title="modENCODE Drosophila pasilla RNA Binding Protein RNAi 
                  knockdown RNA-Seq Studies",
              abstract="RNA-seq of 3 biological replicates of from the Drosophila
                  melanogaster S2-DRSC cells that have been RNAi depleted of mRNAs 
                  encoding pasilla, a mRNA binding protein and 4 biological replicates 
                  of the the untreated cell line.")

design <- data.frame(
              condition=c("untreated", "untreated"),
              type=rep("single-read", 2), stringsAsFactors=TRUE)
geneCDS <- newCountDataSet(

experimentData(geneCDS) <- expdata
sampleNames(geneCDS) = colnames(genehits)

### code chunk number 16: pasilla_genes (eval = FALSE)
chr4tx <- split(gff, mcols(gff)$transcript_id)
txhits <- summarizeOverlaps(chr4tx, bamlst)
txCDS <- newCountDataSet(assay(txhits), design) 
experimentData(txCDS) <- expdata

We can also check out ?summarizeOverlaps to find some fake examples.



See this post for about C version of the featureCounts program.

featureCounts vs HTSeq-count


DESeq or edgeR


An R package for gene and isoform differential expression analysis of RNA-seq data


Probe region expression estimation for RNA-seq data for improved microarray comparability


Inference of differential exon usage in RNA-Seq


A non-parametric approach for detecting differential expression and splicing from RNA-Seq data

voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data

Pathway analysis

fgsea: Fast Gene Set Enrichment Analysis

GSEABenchmarkeR: Reproducible GSEA Benchmarking

Towards a gold standard for benchmarking gene set enrichment analysis


GSEPD: a Bioconductor package for RNA-seq gene set enrichment and projection display




SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package

pasilla and pasillaBamSubset Data

pasilla - Data package with per-exon and per-gene read counts of RNA-seq samples of Pasilla knock-down by Brooks et al., Genome Research 2011.

pasillaBamSubset - Subset of BAM files untreated1.bam (single-end reads) and untreated3.bam (paired-end reads) from "Pasilla" experiment (Pasilla knock-down by Brooks et al., Genome Research 2011).


Transcript expression inference and differential expression analysis for RNA-seq data. The homepage of Antti Honkela.


The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data.


More or less an educational package. It has 2 c and c++ source code. It is used in Advanced R programming and package development.


Bioinformatics paper

CRAN packages


Sample Size Calculation for RNA-Seq Experimental Design


Shiny app


Provides an interface to functions of the 'SAMtools' C-Library by Heng Li


The packge contains functionality for import and managing of downloaded genome annotation Data from Ensembl genome browser (European Bioinformatics Institute) and from UCSC genome browser (University of California, Santa Cruz) and annotation routines for genomic positions and splice site positions.


Provides very fast access to whole genome, population scale variation data from VCF files and sequence data from FASTA-formatted files. It also reads in alignments from FASTA, Phylip, MAF and other file formats. Provides easy-to-use interfaces to genome annotation from UCSC and Bioconductor and gene ontology data from AmiGO and is capable to read, modify and write PLINK .PED-format pedigree files.


Simple TCGA Data Access for Integrated Statistical Analysis in R

TCGA2STAT depends on Bioconductor package CNTools which cannot be installed automatically.



The getTCGA() function allows to download various kind of data:

  • gene expression which includes mRNA-microarray gene expression data (data.type="mRNA_Array") & RNA-Seq gene expression data (data.type="RNASeq")
  • miRNA expression which includes miRNA-array data (data.type="miRNA_Array") & miRNA-Seq data (data.type="miRNASeq")
  • mutation data (data.type="Mutation")
  • methylation expression (data.type="Methylation")
  • copy number changes (data.type="CNA_SNP")


caOmicsV Data from TCGA ws used

Visualize multi-dimentional cancer genomics data including of patient information, gene expressions, DNA methylations, DNA copy number variations, and SNP/mutations in matrix layout or network layout.


The GetGeneList() function is useful to download Genomic Features (including gene features/symbols) from NCBI (

> library(Map2NCBI)
> GeneList = GetGeneList("Homo sapiens", build="ANNOTATION_RELEASE.107", savefiles=TRUE, destfile=path.expand("~/"))
  # choose [2], [n], and [1] to filter the build and feature information.
  # The destination folder will contain seq_gene.txt, and GeneList.txt files.
> str(GeneList)
'data.frame':	52157 obs. of  15 variables:
 $ tax_id       : chr  "9606" "9606" "9606" "9606" ...
 $ chromosome   : chr  "1" "1" "1" "1" ...
 $ chr_start    : num  11874 14362 17369 30366 34611 ...
 $ chr_stop     : num  14409 29370 17436 30503 36081 ...
 $ chr_orient   : chr  "+" "-" "-" "+" ...
 $ contig       : chr  "NT_077402.3" "NT_077402.3" "NT_077402.3" "NT_077402.3" ...
 $ ctg_start    : num  1874 4362 7369 20366 24611 ...
 $ ctg_stop     : num  4409 19370 7436 20503 26081 ...
 $ ctg_orient   : chr  "+" "-" "-" "+" ...
 $ feature_name : chr  "DDX11L1" "WASH7P" "MIR6859-1" "MIR1302-2" ...
 $ feature_id   : chr  "GeneID:100287102" "GeneID:653635" "GeneID:102466751" "GeneID:100302278" ...
 $ feature_type : chr  "GENE" "GENE" "GENE" "GENE" ...
 $ group_label  : chr  "GRCh38.p2-Primary" "GRCh38.p2-Primary" "GRCh38.p2-Primary" "GRCh38.p2-Primary" ...
 $ transcript   : chr  "Assembly" "Assembly" "Assembly" "Assembly" ...
 $ evidence_code: chr  "-" "-" "-" "-" ...
> GeneList$feature_name[grep("^NAP", GeneList$feature_name)]

TCseq: Time course sequencing data analysis


See the internal link at R-GEO.

GREIN: An interactive web platform for re-analyzing GEO RNA-seq data


Biometrical Journal



Genome Analysis section

BMC Bioinformatics








CCBR Exome Pipeliner

MOFA: Multi-Omics Factor Analysis


Essential guidelines for computational method benchmarking


Simulate RNA-Seq


Used by TopHat: discovering splice junctions with RNA-Seq

BEERS/Grant G.R. 2011 The simulation method is called BEERS and it was used in the STAR software paper.

For the command line options of <> and more details about the config files that are needed/prepared by BEERS, see this gist.

This can generate paired end data but they are in one FASTA file.

$ sudo apt-get install cpanminus
$ sudo cpanm Math::Random
$ wget
$ tar -xvf beers.tar      # two perl files <> and <>
$ cd ~/Downloads/
$ mkdir beers_output  
$ mkdir beers_simulator_refseq && cd "$_"
$ wget
$ tar xzvf simulator_config_refseq.tar.gz
$ ls -lth 
total 1.4G
-rw-r--r-- 1 brb brb  44M Sep 16  2010 simulator_config_featurequantifications_refseq
-rw-r--r-- 1 brb brb 7.7M Sep 15  2010 simulator_config_geneinfo_refseq
-rw-r--r-- 1 brb brb 106M Sep 15  2010 simulator_config_geneseq_refseq
-rw-r--r-- 1 brb brb 1.3G Sep 15  2010 simulator_config_intronseq_refseq
$ cd ~/Downloads/
$ perl 100 testbeers \
   -configstem refseq \
   -customcfgdir ~/Downloads/beers_simulator_refseq \
   -outdir ~/Downloads/beers_output

$ ls -lh beers_output
total 3.9M
-rw-r--r-- 1 brb brb 1.8K Mar 16 15:25 simulated_reads2genes_testbeers.txt
-rw-r--r-- 1 brb brb 1.2M Mar 16 15:25 simulated_reads_indels_testbeers.txt
-rw-r--r-- 1 brb brb 1.6K Mar 16 15:25 simulated_reads_junctions-crossed_testbeers.txt
-rw-r--r-- 1 brb brb 2.7M Mar 16 15:25 simulated_reads_substitutions_testbeers.txt
-rw-r--r-- 1 brb brb 6.3K Mar 16 15:25 simulated_reads_testbeers.bed
-rw-r--r-- 1 brb brb  31K Mar 16 15:25 simulated_reads_testbeers.cig
-rw-r--r-- 1 brb brb  22K Mar 16 15:25 simulated_reads_testbeers.fa
-rw-r--r-- 1 brb brb  584 Mar 16 15:25 simulated_reads_testbeers.log

$ wc -l simulated_reads2genes_testbeers.txt
102 simulated_reads2genes_testbeers.txt
$ head -4 simulated_reads2genes_testbeers.txt
seq.1	GENE.5600
seq.2	GENE.35506
seq.3	GENE.506
seq.4	GENE.34922
$ tail -4 simulated_reads2genes_testbeers.txt
seq.97	GENE.4197
seq.98	GENE.8763
seq.99	GENE.19573
seq.100	GENE.18830
$ wc -l simulated_reads_indels_testbeers.txt
36131 simulated_reads_indels_testbeers.txt
$ head -2 simulated_reads_indels_testbeers.txt
chr1:6052304-6052531	25	1	G
chr2:73899436-73899622	141	3	ATA
$ tail -2 simulated_reads_indels_testbeers.txt
chr4:68619532-68621804	1298	-2	AA
chr21:32554738-32554962	174	1	T
$ wc -l simulated_reads_substitutions_testbeers.txt 
71678  simulated_reads_substitutions_testbeers.txt
$ head -2 simulated_reads_substitutions_testbeers.txt 
chr22:50902963-50903167	50903077	G->A
chr1:6052304-6052531	6052330	G->C
$ wc -l simulated_reads_junctions-crossed_testbeers.txt 
49   simulated_reads_junctions-crossed_testbeers.txt
$ head -2 simulated_reads_junctions-crossed_testbeers.txt 
seq.1a	chrX:49084601-49084713
seq.1b	chrX:49084909-49086682

$ cat beers_output/simulated_reads_testbeers.log
Simulator run: 'testbeers'
started: Thu Mar 16 15:25:39 EDT 2017
num reads: 100
readlength: 100
substitution frequency: 0.001
indel frequency: 0.0005
base error: 0.005
low quality tail length: 10
percent of tails that are low quality: 0
quality of low qulaity tails: 0.8
percent of alt splice forms: 0.2
number of alt splice forms per gene: 2
stem: refseq
sum of gene counts: 3,886,863,063
sum of intron counts = 1,304,815,198
sum of intron counts = 2,365,472,596
intron frequency: 0.355507598105262
padded intron frequency: 0.52453796437909
finished at Thu Mar 16 15:25:58 EDT 2017

$ wc -l simulated_reads_testbeers.fa
400 simulated_reads_testbeers.fa
$ head simulated_reads_testbeers.fa

# Take a look at the true coordinates
$ head -4 simulated_reads_testbeers.bed # one-based coords and contains both endpoints of each span
chrX	49084529	49084601	+
chrX	49084713	49084739	+
chrX	49084863	49084909	+
chrX	49086682	49086734	+
$ head -4 simulated_reads_testbeers.cig # has a cigar string representation of the mapping coordinates, and a more human readable representation of the coordinates
$ wc -l simulated_reads_testbeers.fa
400 simulated_reads_testbeers.fa
$ wc -l simulated_reads_testbeers.bed
247 simulated_reads_testbeers.bed
$ wc -l simulated_reads_testbeers.cig
200 simulated_reads_testbeers.cig

Flux Sammeth 2010




A data-based simulation algorithm for rna-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source rna-seq dataset provided by the user.


The key function is simulateCounts, which takes a fitted DESeq2 data object as an input and returns a simulated data object (DESeq2 class) with the same sample size factors, total counts and dispersions for each gene as in real data, but without the effect of predictor variables.

Functions fdrTable, fdrBiCurve and empiricalFDR compare the DESeq2 results obtained for the real and simulated data, compute the empirical false discovery rate (the ratio of the number of differentially expressed genes detected in the simulated data and their number in the real data) and plot the results.


Given a set of annotated transcripts, polyester will simulate the steps of an RNA-seq experiment (fragmentation, reverse-complementing, and sequencing) and produce files containing simulated RNA-seq reads.

Input: reference FASTA file (containing names and sequences of transcripts from which reads should be simulated) OR a GTF file denoting transcript structures, along with one FASTA file of the DNA sequence for each chromosome in the GTF file.

Output: FASTA files. Reads in the FASTA file will be labeled with the transcript from which they were simulated.

Too many dependencies. Got an error in installation.. It seems it has not considered splice junctions.


Simulate DNA-Seq



DNA aligner accuracy: BWA, Bowtie, Soap and SubRead tested with simulated reads

$ head simDNA_100bp_16del.fasta

Simulate Whole genome

Simulate whole exome

Variant simulator

sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs

Convert FASTA to FASTQ

It is interesting to note that the simulated/generated FASTA files can be used by alignment/mapping tools like BWA just like FASTQ files.

If we want to convert FASTA files to FASTQ files, use The quality score 'I' means 40 (the highest) by Sanger (range [0,40]). See The Wikipedia website also mentions FASTQ read simulation tools and a comparison of these tools.

$ cat test.fasta
$ perl ~/Downloads/ test.fasta

Alternatively we can use just one line of code by awk

$ awk 'BEGIN {RS = ">" ; FS = "\n"} NR > 1 {print "@"$1"\n"$2"\n+"; for(c=0;c<length($2);c++) printf "H"; printf "\n"}' \
   test.fasta > test.fq
$ cat test.fq

Change the 'H' to the quality score value that you need (Depending what phred score scale you are using).

Simulate genetic data

‘Simulating genetic data with R: an example with deleterious variants (and a pun)’


module load gossamer
xenome index -M 24 -T 16 -P idx \
  -H $HOME/igenomes/Mus_musculus/UCSC/mm9/Sequence/WholeGenomeFasta/genome.fa \
  -G $HOME/igenomes/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa



DNA Seq Data


  • Go to SRA/Sequence Read Archiveand type the keywords 'Whole Genome Sequencing human'. An example of the procedures to search whole genome sequencing data from human samples:
    1. Enter 'Whole Genome Sequencing human' in ncbi/sra search sra objects at
    2. The webpage will return the result in terms of SRA experiments, SRA studies, Biosamples, GEO datasets. I pick SRA studies from Public Access.
    3. The result is sorted by the Accession number (does not take the first 3 letters like DRP into account). The Accession number has a format SRPxxxx. So I just go to the Last page (page 98)
    4. I pick the first one Accession:SRP066837 from this page. The page shows the Study type is whole genome sequence.
    5. (Important trick) Click the number next to Run. It will show a summary (SRR #, library name, MBases, age, biomaterial provider, isolate and sex) about all samples.
    6. Download the raw data from any one of them (eg SRR2968056). For whole genome, the Strategy is WGS. For whole exome, the Strategy is called WXS.
  • Search the keywords 'nonsynonymous' and 'human' in PMC

Use SRAToolKit instead of wget to download

Don't use the wget command since it requires the specification of right http address.

Downloading SRA data using command line utilities

SRA2R - a package to import SRA data directly into R.

(Method 1) Use the fastq-dump command. For example, the following command (modified from the document will download the first 5 reads and save it to a file called <SRR390728.fastq> (NOT sra format) in the current directory.

/opt/RNA-Seq/bin/sratoolkit.2.3.5-2-ubuntu64/bin/fastq-dump -X 5 SRR390728 -O .
# OR 
/opt/RNA-Seq/bin/sratoolkit.2.3.5-2-ubuntu64/bin/fastq-dump --split-3 SRR390728 # no progress bar

This will download the files in FASTQ format.

(Method 2) If we need to downloading by wget or FTP (works for ‘SRR’, ‘ERR’, or ‘DRR’ series):


It will download the file in SRA format. In the case of SRR590795, the sra is 240M and fastq files are 615*2MB.

(Method 3) Download Ubuntu x86_64 tarball from

[email protected] ~/Downloads $ tar xzvf aspera-connect-
[email protected] ~/Downloads $ ./

Installing Aspera Connect

Deploying Aspera Connect (/home/brb/.aspera/connect) for the current user only.
Restart firefox manually to load the Aspera Connect plug-in

Install complete.

[email protected] ~/Downloads $ ~/.aspera/connect/bin/ascp -QT -l640M \
  -i ~/.aspera/connect/etc/asperaweb_id_dsa.openssh \
  [email protected]:/sra/sra-instant/reads/ByRun/sra/SRR/SRR590/SRR590795/SRR590795.sra .
SRR590795.sra                                                                           100%  239MB  535Mb/s    00:06
Completed: 245535K bytes transferred in 7 seconds
 (272848K bits/sec), in 1 file.
[email protected] ~/Downloads $

Aspera is typically 10 times faster than FTP according to the website. For this case, wget takes 12s while ascp uses 7s.

Note that the URL on the website's is wrong. I got the correct URL from emailing to ncbi help. Google: ascp "[email protected]"

SRAdb package

First we install some required package for XML and RCurl.

sudo apt-get update
sudo apt-get install libxml2-dev
sudo apt-get install libcurl4-openssl-dev

and then



Only the cancer types with expected cases > 10^5 in the US in 2015 are considered here.

SRA Explorer


SRP066363 - lung cancer

SRP015769 or SRP062882 - prostate cancer

SRP053134 - breast cancer

Look at the MBases value column. It determines the coverage for each run.

SRP050992 single cell RNA-Seq

Used in Design and computational analysis of single-cell RNA-sequencing experiments

Single cell RNA-Seq

Exploiting single-cell expression to characterize co-expression replicability

SRP040626 or SRP040540 - Colon and rectal cancer


OmicIDX on BigQuery


See the BWA section.

Whole Exome Seq

Whole Genome Seq


  2.[accn] and click SRP004077
  3. and click Runs from the RHS
  4. and click RunInfoTable

Note that (For this study, it has 2377 rows)

  • Column A (AssemblyName_s) eg GRCh37
  • Column I (library_name_s) eg
  • column N (header=Run_s) shows all SRR or ERR accession numbers.
  • Column P (Sample_Name)
  • Column Y (header=Assay_Type_s) shows WGS.
  • Column AB (LibraryLayout_s): PAIRED

Public Data

ISB Cancer Genomics Cloud (ISB-CGC) Leveraging Google Cloud Platform for TCGA Analysis

The ISB Cancer Genomics Cloud (ISB-CGC) is democratizing access to NCI Cancer Data (TCGA, TARGET, CCLE) and coupling it with unprecedented computational power to allow researchers to explore and analyze this vast data-space.

ISB-CGC Web Application


Next-generation characterization of the Cancer Cell Line Encyclopedia 2019

It has 1000+ cell lines profiled with different -omics including DNA methylation, RNA splicing, as well as some proteomics (and lots more!).ß

NCI's Genomic Data Commons (GDC)/TCGA

The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI).


Sharing data

Gene set analysis

Hypergeometric test

Next-generation sequencing data



Bioinformatics advice I wish I learned 10 years ago from NIH

High Performance

Cloud Computing

Merge different datasets (different genechips)



Ensembl is a genome browser for vertebrate genomes that supports research in comparative genomics, evolution, sequence variation and transcriptional regulation. Ensembl annotate genes, computes multiple alignments, predicts regulatory function and collects disease data. Ensembl tools include BLAST, BLAT, BioMart and the Variant Effect Predictor (VEP) for all supported species.

How to use UCSC Table Browser

File:Tablebrowser.png File:Tablebrowser2.png


  1. the UCSC browser will return the output on browser by default. Users need to use the browser to save the file with self-chosen file name.
  2. the output does not have a header
  3. The bed format is explained in

If I select "Whole Genome", I will get a file with 75,893 rows. If I choose "Coding Exons", I will get a file with 577,387 rows.

$ wc -l hg38Tables.bed 
75893 hg38Tables.bed
$ head -2 hg38Tables.bed 
chr1	67092175	67134971	NM_001276352	0	-	67093579	67127240	0	9	1429,70,145,68,113,158,92,86,42,	0,4076,11062,19401,23176,33576,34990,38966,42754,
chr1	201283451	201332993	NM_000299	0	+	201283702	201328836	0	15	453,104,395,145,208,178,63,115,156,177,154,187,85,107,2920,	0,10490,29714,33101,34120,35166,36364,36815,38526,39561,40976,41489,42302,45310,46622,
$ tail -2 hg38Tables.bed 
chr22_KI270734v1_random	131493	137393	NM_005675	0	+	131645	136994	0	5	262,161,101,141,549,	0,342,3949,4665,5351,
chr22_KI270734v1_random	138078	161852	NM_016335	0	-	138479	161586	0	15	589,89,99,176,147,93,82,80,117,65,150,35,209,313,164,	0,664,4115,5535,6670,6925,8561,9545,10037,10335,12271,12908,18210,23235,23610,

$ wc -l hg38CodingExon.bed 
577387 hg38CodingExon.bed
$ head -2 hg38CodingExon.bed 
chr1	67093579	67093604	NM_001276352_cds_0_0_chr1_67093580_r	0	-
chr1	67096251	67096321	NM_001276352_cds_1_0_chr1_67096252_r	0	-
$ tail -2 hg38CodingExon.bed 
chr22_KI270734v1_random	156288	156497	NM_016335_cds_12_0_chr22_KI270734v1_random_156289_r	0	-
chr22_KI270734v1_random	161313	161586	NM_016335_cds_13_0_chr22_KI270734v1_random_161314_r	0	-

# Focus on one NCBI refseq (
$ grep NM_001276352 hg38Tables.bed 
chr1	67092175	67134971	NM_001276352	0	-	67093579	67127240	0	9	1429,70,145,68,113,158,92,86,42,	0,4076,11062,19401,23176,33576,34990,38966,42754,
$ grep NM_001276352 hg38CodingExon.bed
chr1	67093579	67093604	NM_001276352_cds_0_0_chr1_67093580_r	0	-
chr1	67096251	67096321	NM_001276352_cds_1_0_chr1_67096252_r	0	-
chr1	67103237	67103382	NM_001276352_cds_2_0_chr1_67103238_r	0	-
chr1	67111576	67111644	NM_001276352_cds_3_0_chr1_67111577_r	0	-
chr1	67115351	67115464	NM_001276352_cds_4_0_chr1_67115352_r	0	-
chr1	67125751	67125909	NM_001276352_cds_5_0_chr1_67125752_r	0	-
chr1	67127165	67127240	NM_001276352_cds_6_0_chr1_67127166_r	0	-

This can be compared to refGene(?) directly downloaded via http

$ wget -c -O hg38.refGene.txt.gz
--2018-10-09 15:44:43--
Resolving (
Connecting to (||:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 7457957 (7.1M) [application/x-gzip]
Saving to: ‘hg38.refGene.txt.gz’

hg38.refGene.txt.gz                100%[===============================================================>]   7.11M   901KB/s    in 10s

2018-10-09 15:44:54 (708 KB/s) - ‘hg38.refGene.txt.gz’ saved [7457957/7457957]

$ zcat hg38.refGene.txt.gz | wc -l
15:45PM /tmp$ zcat hg38.refGene.txt.gz | head -2
1072	NM_003288	chr20	+	63865227	63891545	63865365	63889945	7	63865227,63869295,63873667,63875815,63882718,63889189,63889849,	63865384,63869441,63873816,63875875,63882820,63889238,63891545,	0	TPD52L2	cmpl	cmpl	0,1,0,2,2,2,0,
1815	NR_110164	chr2	+	161244738	161249050	161249050	161249050	2	161244738,161246874,	161244895,161249050,	0	LINC01806	unk	unk	-1,-1,

$ zcat hg38.refGene.txt.gz | tail -2
1006	NM_130467	chrX	+	55220345	55224108	55220599	55224003	5	55220345,55221374,55221766,55222620,55223986,	55220651,55221463,55221875,55222746,55224108,	0	PAGE5	cmpl	cmpl	0,1,0,1,1,
637	NM_001364814	chrY	-	6865917	6874027	6866072	6872608	7	6865917,6868036,6868731,6868867,6870005,6872554,6873971,	6866078,6868462,6868776,6868909,6870053,6872620,6874027,	0	AMELY	cmpl	cmpl	0,0,0,0,0,0,-1,

Where to download reference genome

Which human reference genome to use? (11/13/2017)

RefSeq categories

See Table 1 of Chapter 18The Reference Sequence (RefSeq) Database.

Category Description
NC Complete genomic molecules
NG Incomplete genomic region
NP Protein
XM predicted mRNA model
XR predicted ncRNA model
XP predicted Protein model (eukaryotic sequences)
WP predicted Protein model (prokaryotic sequences)

UCSC version & NCBI release corresponding

Gene Annotation

How many DNA strands are there in humans?

How many base pairs in human

  • 3 billion base pairs.
  • chromosome 22 has the smallest number of bps (~50 million).
  • chromosome 1 has the largest number of bps (245 million base pairs).
  • Illumina iGenome Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa file is 3.0GB (so is other genome.fa from human).

Gene, Transcript, Coding/Non-coding exon


Types of SNPs and number of SNPs in each chromosomes

NGS technology

DNA methylation

library(coloncancermeth) # 485512 x 26
data(coloncancermeth) # load meth (methylation data), pd (sample info ) and gr objects
table(pd$Status) # 9 normals, 17 cancers
normalIndex <- which(pd$Status=="normal")
cancerlIndex <- which(pd$Status=="cancer")

for(i in normalIndex){
### Add the cancer samples
for(i in cancerlIndex){

# finding regions of the genome that are different between cancer and normal samples
eb <- ebayes(fit)

# plot of the region surrounding the top hit
i <- which.min(eb$p.value[,2])
middle <- gr[i,]

plot(pos[Index],fit$coef[Index,2],type="b",xlab="genomic location",ylab="difference")
matplot(pos[Index],meth[Index,],col=cols,xlab="genomic location")

# within each chromosome we usually have big gaps creating subgroups of regions to be analyzed
chr1Index <- which(chr=="chr1")
hist(log10(diff(pos[chr1Index])),main="",xlab="log 10 method")

table(table(cl)) ##shows the number of regions with 1,2,3, ... points in them
#consider two example regions#

Whole Genome Sequencing, Whole Exome Sequencing, Transcriptome (RNA) Sequencing

Sequence + Expression

Integrate RNA-Seq and DNA-Seq

Integrate/combine Omics

Gene expression

Expression level is the amount of RNA in cell that was transcribed from that gene. Slides from Alyssa Frazee.

Quantile normalization

  • When to use Quantile Normalization? and its R package quantro
  • normalize.quantiles() from preprocessCore package. Note for ties, the average is used in normalize.quantiles(), ((4.666667 + 5.666667) / 2) = 5.166667.
    #load package
    #the function expects a matrix
    #create a matrix using the same example
    mat <- matrix(c(5,2,3,4,4,1,4,2,3,4,6,8),
    #     [,1] [,2] [,3]
    #[1,]    5    4    3
    #[2,]    2    1    4
    #[3,]    3    4    6
    #[4,]    4    2    8
    #quantile normalisation
    #         [,1]     [,2]     [,3]
    #[1,] 5.666667 5.166667 2.000000
    #[2,] 2.000000 2.000000 3.000000
    #[3,] 3.000000 5.166667 4.666667
    #[4,] 4.666667 3.000000 5.666667

Merging two gene expression studies

  • Alternative empirical Bayes models for adjusting for batch effects in genomic studies Zhang et al. BMC Bioinformatics 2018. The R package is BatchQC from Bioconductor.
  • Combat() function in sva package from Bioconductor.
    • It can remove both known batch effects and other potential latent sources of variation.
    • The tutorial includes information on (1) how to estimate the number of latent sources of variation, (2) how to apply the sva package to estimate latent variables such as batch effects, (3) how to directly remove known batch effects using the ComBat function, (4) how to perform differential expression analysis using surrogate variables either directly or with thelimma package, and (4) how to apply “frozen” sva to improve prediction and clustering.
    • Tutorial example to remove the batch effect
pheno = pData(bladderEset)
edata = exprs(bladderEset)
batch = pheno$batch
modcombat = model.matrix(~1, data=pheno)
combat_edata = ComBat(dat=edata, batch=batch, mod=modcombat, 
                      par.prior=TRUE, prior.plots=FALSE)
# This returns an expression matrix, with the same dimensions 
# as your original dataset.
# By default, it performs parametric empirical Bayesian adjustments. 
# If you would like to use nonparametric empirical Bayesian adjustments, 
# use the par.prior=FALSE option (this will take longer).

Fusion gene

Structural variation

LUMPY, DELLY, ForestSV, Pindel, breakdancer , SVDetect.

RNASeq + ChipSeq


Biowulf2 at NIH


Hash BAM and FASTQ files to verify data integrity. The C++ code is based on OpenSSL and seqan libraries.

Selected Papers





Staying current

Staying Current in Bioinformatics & Genomics: 2017 Edition


Common issues in algorithmic bioinformatics papers

What are some common issues I find when reviewing algorithmic bioinformatics conference papers?

Precision Medicine courses

Personalized medicine

Cancer and gene markers

  • Colorectal cancer patients without KRAS mutations have far better outcomes with EGFR treatment than those with KRAS mutations.
    • Two EGFR inhibitors, cetuximab and panitumumab are not recommended for the treatment of colorectal cancer in patients with KRAS mutations in codon 12 and 13.
  • Breast cancer.

The shocking truth about space travel

7 percent of DNA belonging to NASA astronaut Scott Kelly changed in the time he was aboard the International Space Station

bioSyntax: syntax highlighting for computational biology

Deep learning

Deep learning: new computational modelling techniques for genomics



RNA sequencing 101



strand-specific vs non-strand specific experiment

Understand this info is necessary when we want to use summarizeOverlaps() function (GenomicAlignments) or htseq-count python program to get count data.

This post mentioned to use script to check whether the rna-seq run is stranded or not.

The rna-seq experiment used in this tutorial is not stranded-specific.


  • FASTQ=FASTA + Qual. FASTQ format is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores.

Phred quality score

q = -10log10(p) where p = error probability for the base.

q error probability base call accuracy
10 0.1 90%
13 0.05 95%
20 0.01 99%
30 0.001 99.9%
40 0.0001 99.99%
50 0.00001 99.999%


fasta/fa files can be used as reference genome in IGV. But we cannot load these files in order to view them.

Download sequence files

Compute the sequence length of a FASTA file

awk '/^>/ {if (seqlen){print seqlen}; print ;seqlen=0;next; } { seqlen += length($0)}END{print seqlen}' file.fa

head -2 file.fa | \
    awk '/^>/ {if (seqlen){print seqlen}; print ;seqlen=0;next; } { seqlen += length($0)}END{print seqlen}'  | \
    tail -1

FASTA <=> FASTQ conversion

According to this post,

  • FastA are text files containing multiple DNA* seqs each with some text, some part of the text might be a name.
  • FastQ files are like fasta, but they also have quality scores for each base of each seq, making them appropriate for reads from an Illumina machine (or other brands)

Convert FASTA to FASTQ without quality scores

Biostars. For example, the bioawk by lh3 (Heng Li) worked.

Convert FASTA to FASTQ with quality score file

See the links on the above post.

Convert FASTQ to FASTA using Seqtk

Use the Seqtk program; see this post.

The Seqtk program by lh3 can be used to sample reads from a fastq file including paired-end; see this post.

RPKM (Mortazavi et al. 2008)

Reads per Kilobase of Exon per Million of Mapped reads.


  • The more we sequence, the more reads we expect from each gene. This is the most relevant correction of this method.
  • Longer transcript are expected to generate more reads. The latter is only relevant for comparisons among different genes which we rarely perform!. As such, the DESeq2 only creates a size factor for each library and normalize the counts by dividing counts by a size factor (scalar) for each library. Note that: H0: mu1=mu2 is equivalent to H0: c*mu1=c*mu2 where c is gene length.


  1. Count up the total reads in a sample and divide that number by 1,000,000 – this is our “per million” scaling factor.
  2. Divide the read counts by the “per million” scaling factor. This normalizes for sequencing depth, giving you reads per million (RPM)
  3. Divide the RPM values by the length of the gene, in kilobases. This gives you RPKM.


RPKM = (10^9 * C)/(N * L), with 

C = Number of reads mapped to a gene
N = Total mapped reads in the experiment
L = gene length in base-pairs for a gene

y <- matrix(rnbinom(20,size=1,mu=10),5,4)
     [,1] [,2] [,3] [,4]
[1,]    0    0    5    0
[2,]    6    2    7    3
[3,]    5   13    7    2
[4,]    3    3    9   11
[5,]    1    2    1   15

d <- DGEList(counts=y, lib.size=1001:1004)
# Note that lib.size is optional
# By default, lib.size = colSums(counts)
cpm(d) # counts per million
   Sample1   Sample2  Sample3   Sample4
1    0.000     0.000 4985.045     0.000
2 5994.006  1996.008 6979.063  2988.048
3 4995.005 12974.052 6979.063  1992.032
4 2997.003  2994.012 8973.081 10956.175
5  999.001  1996.008  997.009 14940.239
> cpm(d,log=TRUE)
    Sample1   Sample2  Sample3   Sample4
1  7.961463  7.961463 12.35309  7.961463
2 12.607393 11.132027 12.81875 11.659911
3 12.355838 13.690089 12.81875 11.129470
4 11.663897 11.662567 13.17022 13.451207
5 10.285119 11.132027 10.28282 13.890078

d$genes$Length <- c(1000,2000,500,1500,3000)
    Sample1   Sample2    Sample3  Sample4
1    0.0000     0.000  4985.0449    0.000
2 2997.0030   998.004  3489.5314 1494.024
3 9990.0100 25948.104 13958.1256 3984.064
4 1998.0020  1996.008  5982.0538 7304.117
5  333.0003   665.336   332.3363 4980.080

> cpm
function (x, ...)
<environment: namespace:edgeR>
> showMethods("cpm")

Function "cpm":
 <not an S4 generic function>
> cpm.default
function (x, lib.size = NULL, log = FALSE, prior.count = 0.25,
    x <- as.matrix(x)
    if (is.null(lib.size))
        lib.size <- colSums(x)
    if (log) {
        prior.count.scaled <- lib.size/mean(lib.size) * prior.count
        lib.size <- lib.size + 2 * prior.count.scaled
    lib.size <- 1e-06 * lib.size
    if (log)
        log2(t((t(x) + prior.count.scaled)/lib.size))
    else t(t(x)/lib.size)
<environment: namespace:edgeR>
> rpkm.default
function (x, gene.length, lib.size = NULL, log = FALSE, prior.count = 0.25,
    y <- cpm.default(x = x, lib.size = lib.size, log = log, prior.count = prior.count)
    gene.length.kb <- gene.length/1000
    if (log)
        y - log2(gene.length.kb)
    else y/gene.length.kb
<environment: namespace:edgeR>

Here for example the 1st sample and the 2nd gene, its rpkm value is calculated as

# step 1:
6/(1.0e-6 *1001) = 5994.006    # cpm, compute column-wise
# step 2:
5994.006/ (2000/1.0e3) = 2997.003 # rpkm, compute row-wise

# Another way
# step 1 (RPK) 
6/ (2000/1.0e3) = 3
# step 2 (RPKM)
3/ (1.0e-6 * 1001) = 2997.003


Consider the following example: in two libraries, each with one million reads, gene X may have 10 reads for treatment A and 5 reads for treatment B, while it is 100x as many after sequencing 100 millions reads from each library. In the latter case we can be much more confident that there is a true difference between the two treatments than in the first one. However, the RPKM values would be the same for both scenarios. Thus, RPKM/FPKM are useful for reporting expression values, but not for statistical testing!

(another critic) Union Exon Based Approach

In general, the methods for gene quantification can be largely divided into two categories: transcript-based approach and ‘union exon’-based approach.

It was found that the gene expression levels are significantly underestimated by ‘union exon’-based approach, and the average of RPKM from ‘union exons’-based method is less than 50% of the mean expression obtained from transcript-based approach.

FPKM (Trapnell et al. 2010)

Fragment per Kilobase of exon per Million of Mapped fragments (Cufflinks). FPKM is very similar to RPKM. RPKM was made for single-end RNA-seq, where every read corresponded to a single fragment that was sequenced. FPKM was made for paired-end RNA-seq. With paired-end RNA-seq, two reads can correspond to a single fragment, or, if one read in the pair did not map, one read can correspond to a single fragment. The only difference between RPKM and FPKM is that FPKM takes into account that two reads can map to one fragment (and so it doesn’t count this fragment twice).


> dds <- makeExampleDESeqDataSet(m=4)
> head(counts(dds))
      sample1 sample2 sample3 sample4
gene1       0       1       1       6
gene2       2       0       0       3
gene3      18       9      19      12
gene4      12      25      13      13
gene5      22      26      10       8
gene6       6       5       8       6
> dds <- estimateSizeFactors(dds)
> head(counts(dds))
      sample1 sample2 sample3 sample4
gene1       0       1       1       6
gene2       2       0       0       3
gene3      18       9      19      12
gene4      12      25      13      13
gene5      22      26      10       8
gene6       6       5       8       6
> head(counts(dds, normalized=TRUE))
       sample1    sample2    sample3   sample4
gene1  0.00000  0.9654796  0.9858756  5.732657
gene2  1.96066  0.0000000  0.0000000  2.866328
gene3 17.64594  8.6893164 18.7316365 11.465314
gene4 11.76396 24.1369899 12.8163829 12.420756
gene5 21.56726 25.1024695  9.8587560  7.643542
gene6  5.88198  4.8273980  7.8870048  5.732657
P -- per
K -- kilobase (related to gene length)
M -- million (related to sequencing depth)

TMM (Robinson and Oshlack, 2010)

Trimmed Means of M values (EdgeR).

Sample size


~20x coverage ----> reads per transcript = transcriptlength/readlength * 20
C = L N / G

where L=read length, N =number of reads and G=haploid genome length. So, if we take one lane of single read human sequence with v3 chemistry, we get C = (100 bp)*(189×10^6)/(3×10^9 bp) = 6.3. This tells us that each base in the genome will be sequenced between six and seven times on average.

# Assume the bam file is sorted by chromosome location
# took 40 min on 5.8G bam file. samtools depth has no threads option:(
# it is not right since it only account for regions that were covered with reads
samtools depth  *bamfile*  |  awk '{sum+=$3} END { print "Average = ",sum/NR}'    # maybe 42

# The following is the right way! The result matches with Qualimap program.
samtools depth -a *bamfile*  |  awk '{sum+=$3} END { print "Average = ",sum/NR}'  # maybe 8
# OR
LEN=`samtools view -H bamfile | grep -P '^@SQ' | cut -f 3 -d ':' | awk '{sum+=$1} END {print sum}'`   # 3095693981
SUM=`samtools depth bamfile | awk '{sum+=$3} END { print "Sum = ", sum}'`   # 24473867730
echo $(( $LEN/$SUM ))

SAM/Sequence Alignment Format and BAM format specification

Single-end, pair-end, fragment, insert size

Germline vs Somatic mutation

Germline: inherit from parents. See the Wikipedia page.

Driver vs passenger mutation

Nonsynonymous mutation

It is related to the genetic code, Wikipedia. There are 20 amino acids though there are 64 codes.


isma: analysis of mutations detected by multiple pipelines

isma: an R package for the integrative analysis of mutations detected by multiple pipelines

Missense variants

aminoacid changing variants

Alternative and differential splicing

Best practices and appropriate workflows to analyse alternative and differential splicing

Allele vs Gene

  • A gene is a stretch of DNA or RNA that determines a certain trait.
  • Genes mutate and can take two or more alternative forms; an allele is one of these forms of a gene. For example, the gene for eye color has several variations (alleles) such as an allele for blue eye color or an allele for brown eyes.
  • An allele is found at a fixed spot on a chromosome?
  • Chromosomes occur in pairs so organisms have two alleles for each gene — one allele in each chromosome in the pair. Since each chromosome in the pair comes from a different parent, organisms inherit one allele from each parent for each gene. The two alleles inherited from parents may be same (homozygous) or different (heterozygotes).



Base quality, Mapping quality, Variant quality

Mapping quality (MAPQ) vs Alignment score (AS) & SAM format specification

  • MAPQ (5th column): MAPping Quality. It equals −10 log10 Pr{mapping position is wrong} (defined by SAM documentation), rounded to the nearest integer. A value 255 indicates that the mapping quality is not available. MAPQ is a metric that tells you how confident you can be that the read comes from the reported position. So given 1000 reads, for example, read alignments with mapping quality being 30, one of them will be wrong in average (10^(30/-10)=.001). Another example, if MAPQ=70, then the probability mapping position is wrong is 10^(70/-10)=1e-7. We can use 'samtools view -q 30 input.bam' to keep reads with MAPQ at least 30. Users should refer to the alignment program for the 'MAPQ' value it uses.
  • AS (optional, 14th column in my case): Alignment score is a metric that tells you how similar the read is to the reference. AS increases with the number of matches and decreases with the number of mismatches and gaps (rewards and penalties for matches and mismatches depend on the scoring matrix you use)


  1. MAPQ scores produced by the aligners typically involves the alignment score and other information.
  2. You can have high AS and low MAPQ if the read aligns perfectly at multiple positions, and you can have low AS and high MAPQ if the read aligns with mismatches but still the reported position is still much more probable than any other.
  3. You probably want to filter for MAPQ, but "good" alignment may refer to AS if what you care is similarity between read and reference.
  4. MAPQ values are really useful but their implementation is a mess by Simon Andrews

Other software




MeV v4.8 (11/18/2011) allows annotation from Bioconductor

IPA from Ingenuity

Login: There are web started version and Java applet version We can double click the file <IpaApplication.jnlp> in my machine's download folder.


  • easily search the scientific literature/integrate diverse biological information.
  • build dynamic pathway models
  • quickly analyze experimental data/Functional discovery: assign function to genes
  • share research and collaborate. On the other hand, IPA is web based, so it takes time for running analyses. Once submitted analyses are done, an email will be sent to the user.

Start Here

Expression data -> New core analysis -> Functions/Diseases -> Network analysis
                                        Canonical pathways        |
                                              |                   |
Simple or advanced search --------------------+                   |
                                              |                   |
                                              v                   |
                                        My pathways, Lists <------+
Creating a custom pathway --------------------+



  • The input data file can be an Excel file with at least one gene ID and expression value at the end of columns (just what BRB-ArrayTools requires in general format importer).
  • The data to be uploaded (because IPA is web-based; the projects/analyses will not be saved locally) can be in different forms. See It uses the term Single/Multiple Observation. An Observation is a list of molecule identifiers and their corresponding expression values for a given experimental treatment. A dataset file may contain a single observation or multiple observations. A Single Observation dataset contains only one experimental condition (i.e. wild-type). A Multiple Observation dataset contains more than one experimental condition (i.e. a time course experiment, a dose response experiment, etc) and can be uploaded into IPA in a single file (e.g. Excel). A maximum of 20 observations in a single file may be uploaded into IPA.
  • The instruction shows what kind of gene identifier types IPA accepts.
  • In this prostate example data tutorial, the term 'fold change' was used to replace log2 gene expression. The tutorial also uses 1.5 as the fold change expression cutoff.
  • The gene table given on the analysis output contains columns 'Fold change', 'ID', 'Notes', 'Symbol' (with tooltip), 'Entrez Gene Name', 'Location', 'Types', 'Drugs'. See a screenshot below.



DAVID Bioinformatics Resource

It offers an integrated annotation combining gene ontology, pathways and protein annotations.

It can be used to identify the pathways associated with a set of genes; e.g. this paper.


GOTrapper: a tool to navigate through branches of gene ontology hierarchy


Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).



Genome-wide association studies in R