The data is used in the paper "Count-based differential ....." by Anders et al 2013.

Required tools

The version number indicated below is the one I use. It may be updated when you are ready to download that.

bowtie (bowtie2-2.1.0-linux-x86_64.zip): Mac, Linux, Windows binaries
samtools (samtools-0.1.19.tar.bz2): require GCC to compile (build-essential) & zlib1g-dev & libncurses5-dev packages. wikipedia.
tophat (tophat-2.0.10.Linux_x86_64.tar.gz): Linux and Mac binaries. Mailing list is on Tuxedo Tools Users Google Group.
sra toolkit (sratoolkit.2.3.4-2-ubuntu64.tar.gz): Mac, Ubuntu & CentOS binaries
HTseq (HTSeq-0.6.1.tar.gz): require Python 2
IGV (IGV_2.3.26.zip): require Java and registration to download the program
FastQC (fastqc_v0.10.1.zip): require Java (jdk is needed, jre is not enough)
FastX (fastx_toolkit_0.0.13_binaries_Linux_2.6_amd64.tar.bz2): require libgtextutils-0.6

Note

For alignment, STAR is another choice.
For trimmer, trimmomatic is another choice.

Installation

If binary executables are available, we don't need to do anything special except adding the path containing the binary to the PATH variable.

If only source code is available, make sure the required tool GCC is installed (Under Ubuntu, we can use sudo apt-get install build-essential). Then we can compile and then install the program by using ./configure, make and make install.

In this exercise, all source code or binary programs were extracted to $HOME/binary/ directory (Use mkdir ~/binary to create this new directory) where each program has its own directory.

To use a shell script to install every thing, try the following.

cd; wget -N https://www.dropbox.com/s/ip1jiarxhq0dq91/install.sh; chmod +x install.sh; sudo ./install.sh; rm install.sh

Change PATH variable

Open ~/.basrhc file using any text editor (such as nano or vi) and add the path containing the binary of each program to PATH variable.

tail ~/.bashrc
export PATH=$PATH:~/binary/bowtie2-2.1.0/
export PATH=$PATH:~/binary/tophat-2.0.10.Linux_x86_64/
export PATH=$PATH:~/binary/samtools-0.1.19/
export PATH=$PATH:~/binary/samtools-0.1.19/bcftools/
export PATH=$PATH:~/binary/samtools-0.1.19/misc/
export PATH=$PATH:~/binary/sratoolkit.2.3.4-2-ubuntu64/bin
export PATH=$PATH:~/binary/HTSeq-0.6.1/build/scripts-2.7
export PATH=$PATH:~/binary/IGV_2.3.26/
export PATH=$PATH:~/binary/FastQC/
export PATH=$PATH:~/binary/fastx/bin

If we want to make these tools available for ALL users (ie system wide environment), we need to put these lines on /etc/profile file.

Data directory

Download <nprot.2013.099-S1.zip> from the paper's web site and extract it to ~/Anders2013 directory.

brb@brbweb4:~$ mkdir Anders2013
brb@brbweb4:~$ cd Anders2013
brb@brbweb4:~/Anders2013$ wget http://www.nature.com/nprot/journal/v8/n9/extref/nprot.2013.099-S1.zip
brb@brbweb4:~/Anders2013$ unzip nprot.2013.099-S1.zip
brb@brbweb4:~/Anders2013$ ls
CG8144_RNAi-1.count  counts.csv             README.txt      Untreated-1.count  Untreated-6.count
CG8144_RNAi-3.count  __MACOSX               samples.csv     Untreated-3.count
CG8144_RNAi-4.count  nprot.2013.099-S1.zip  SraRunInfo.csv  Untreated-4.count

The raw data is from GSE18508 'modENCODE Drosophila RNA Binding Protein RNAi RNA-Seq Studies'.

Subdirectory

During running tophat program, several subdirectories will be generated. Each subdirectory will contain accepted_hits.bam, junctions.bed, insertions.bed, deletions.bed and other files/sub-subdirectories.

Once samtools program was run, .bam (and .bai) files will be created under Anders2013 directory.

Pipeline

The following summary is extracted from http://www.bioconductor.org/help/course-materials/2013/CSAMA2013/tuesday/afternoon/DESeq_protocol.pdf. It is the same as Anders 2013 paper but the paper requires an access permission.

It would be interesting to monitor the disk space usage before, during and after the execution.

Download example data (22 files in SRA format)

Download Supplement file and read <SraRunInfo.csv> in R. We will choose a subset of data based on "LibraryName" column.

sri = read.csv("SraRunInfo.csv", stringsAsFactors=FALSE)
keep = grep("CG8144|Untreated-",sri$LibraryName)
sri = sri[keep,]
fs = basename(sri$download_path)
for(i in 1:nrow(sri))
 download.file(sri$download_path[i], fs[i])
names(sri)
[1] "Run"              "ReleaseDate"      "spots"            "bases"           
[5] "avgLength"        "size_MB"          "AssemblyName"     "download_path"   
[9] "Experiment"       "LibraryName"      "LibraryStrategy"  "LibrarySelection"
[13] "LibrarySource"    "LibraryLayout"    "InsertSize"       "Platform"        
[17] "Model"            "SRAStudy"         "BioProject"       "Study_Pubmed_id"
[21] "ProjectID"        "Sample"           "BioSample"        "TaxID"           
[25] "ScientificName"   "SampleName"       "Submission"       "Consent"        
apply(sri,2, function(x) length(unique(x)))
            Run      ReleaseDate            spots            bases
             22               22               22               22
      avgLength          size_MB     AssemblyName    download_path
              5               22                1               22
     Experiment      LibraryName  LibraryStrategy LibrarySelection
              7                7                1                1
  LibrarySource    LibraryLayout       InsertSize         Platform
              1                2                2                1
          Model         SRAStudy       BioProject  Study_Pubmed_id
              1                1                1                1
      ProjectID           Sample        BioSample            TaxID
              1                7                1                1
 ScientificName       SampleName       Submission          Consent
              1                1                1                1
sri[1:2,]
       Run         ReleaseDate   spots     bases avgLength size_MB
1 SRR031714 2010-01-15 10:42:00 5327425 394229450        74     396
2 SRR031715 2010-01-21 14:24:18 5248396 388381304        74     390
 AssemblyName
1           NA
2           NA
                                                                                         download_path
1 ftp://ftp-private.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR031/SRR031714/SRR031714.sra
2 ftp://ftp-private.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR031/SRR031715/SRR031715.sra
 Experiment LibraryName LibraryStrategy LibrarySelection  LibrarySource
1  SRX014459 Untreated-3         RNA-Seq             cDNA TRANSCRIPTOMIC
2  SRX014459 Untreated-3         RNA-Seq             cDNA TRANSCRIPTOMIC
 LibraryLayout InsertSize Platform                       Model  SRAStudy
1        PAIRED        200 ILLUMINA Illumina Genome Analyzer II SRP001537
2        PAIRED        200 ILLUMINA Illumina Genome Analyzer II SRP001537
                     BioProject Study_Pubmed_id ProjectID    Sample BioSample
1 GEO Series accession: GSE18508        20921232    168994 SRS008447        NA
2 GEO Series accession: GSE18508        20921232    168994 SRS008447        NA
 TaxID          ScientificName              SampleName Submission Consent
1  7227 Drosophila melanogaster Drosophila melanogaster  SRA010243  public
2  7227 Drosophila melanogaster Drosophila melanogaster  SRA010243  public

Convert SRA to FASTQ (sra -> fastq)

This takes a long time and it is not to run in parallel by default. At the end of execution, there are 30 fastq files because 8 SRA file are pair end.

stopifnot( all(file.exists(fs)) ) # assure FTP download was successful
for(f in fs) {
cmd = paste("fastq-dump --split-3", f)
cat(cmd,"\n")
system(cmd) # invoke command
}

fastq-dump --split-3 SRR031714.sra
Read 5327425 spots for SRR031714.sra
Written 5327425 spots for SRR031714.sra
fastq-dump --split-3 SRR031715.sra
Read 5248396 spots for SRR031715.sra
Written 5248396 spots for SRR031715.sra
...

Note that '--split-3' option will splits mate-pair reads into separate files (First biological reads satisfying dumping conditions are placed in files *_1.fastq and *_2.fastq If only one biological read is present it is placed in *.fastq Biological reads and above are ignored). See this post.

brb@brbweb4:~/Anders2013$ ls -lh *.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 12:47 SRR031708.fastq
-rw-r--r-- 1 brb brb 812M Feb 10 12:50 SRR031709.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 12:56 SRR031710.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 13:02 SRR031711.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 13:08 SRR031712.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 13:14 SRR031713.fastq
-rw-r--r-- 1 brb brb 1.1G Feb 10 11:48 SRR031714_1.fastq
-rw-r--r-- 1 brb brb 1.1G Feb 10 11:48 SRR031714_2.fastq
-rw-r--r-- 1 brb brb 1.1G Feb 10 11:54 SRR031715_1.fastq
-rw-r--r-- 1 brb brb 1.1G Feb 10 11:54 SRR031715_2.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 12:02 SRR031716_1.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 12:02 SRR031716_2.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 12:10 SRR031717_1.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 12:10 SRR031717_2.fastq
-rw-r--r-- 1 brb brb 1.4G Feb 10 13:21 SRR031718.fastq
-rw-r--r-- 1 brb brb 927M Feb 10 13:25 SRR031719.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 13:32 SRR031720.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 13:39 SRR031721.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 13:45 SRR031722.fastq
-rw-r--r-- 1 brb brb 842M Feb 10 13:49 SRR031723.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 12:17 SRR031724_1.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 12:17 SRR031724_2.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 12:25 SRR031725_1.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 12:25 SRR031725_2.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 12:33 SRR031726_1.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 12:33 SRR031726_2.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 12:41 SRR031727_1.fastq
-rw-r--r-- 1 brb brb 1.2G Feb 10 12:41 SRR031727_2.fastq
-rw-r--r-- 1 brb brb 1.3G Feb 10 13:54 SRR031728.fastq
-rw-r--r-- 1 brb brb 3.2G Feb 10 14:07 SRR031729.fastq

Access quality control through ShortRead or FastQC

library("ShortRead")
fqQC = qa(dirPath=".", pattern=".fastq$", type="fastq")
report(fqQC, type="html", dest="fastqQAreport")

Use a web browser to inspect the generated HTML file (here, stored in the \fastqQAreport" directory) with the quality-assessment report.

FastQC can be run either interactively or non-interactive. For example

/opt/RNA-Seq/bin/FastQC/fastqc ~/GSE11209/SRR002058.fastq --outdir=/home/brb/GSE11209/fastQC

will create a zip file and an unzipped directory under /home/brb/GSE11209/fastQC.

FastX_trimmer is a better choice. The syntax is

fastx_trimmer -f N -l N -i INPUT -o OUTPUT -v -Q33

The last parameter -Q33 is necessary for Illumina encoding quality score. If we omit it, we will get an error fastx_trimmer: Invalid quality score value (char '#' ord 35 quality value -29) on line 4

Read quality issues

http://training.bioinformatics.ucdavis.edu/docs/2012/05/RNA/qa-and-i.html

Per base sequence quality (by position)
Per sequence quality scores: see if a subset of sequences have universally low quality values.
Per base sequence content
Per sequence GC content
Per base N content
Sequence Length Distribution
Sequence Duplication Levels
Overpresented sequences
Kmer Content

Download reference genome (for Tophat)

wget ftp://ftp.ensembl.org/pub/release-70/fasta/drosophila_melanogaster/dna/Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa.gz
gunzip Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa.gz

Note that bowtie2/tophat2 indices for many commonly used reference genomes can be downloaded directly from https://ccb.jhu.edu/software/tophat/igenomes.shtml. Actually the link https://support.illumina.com/sequencing/sequencing_software/igenome.html from illumina is more complete.

For human species, it has different assemblies

Ensembl - GRCh37 (equivalent to hg19)
NCBI - GRCh38, build37.1, build37.2, build36.3 (equivalent to hg18)
UCSC - hg38, hg19, hg18

Source
Ensembl		GRCh37
NCBI	GRCh38	build37	build36
UCSC	hg38	hg19	hg18

Structure of downloaded reference genome from Tophat

Downloaded genome from tophat takes a long time (20GB for human). Using bowtie2-build program which can takes an enormous time to run.

$ tar xzvf Homo_sapiens_Ensembl_GRCh37.tar.gz
$ tree -L 4 Homo_sapiens
Homo_sapiens
└── Ensembl
    └── GRCh37
        ├── Annotation
        │   ├── Archives             <==== gtf file
        │   ├── Genes -> Archives/archive-current/Genes
        │   ├── README.txt -> Archives/archive-current/README.txt
        │   ├── SmallRNA -> Archives/archive-current/SmallRNA
        │   └── Variation -> Archives/archive-current/Variation
        ├── GenomeStudio
        │   ├── Archives
        │   ├── Homo_sapiens -> Archives/archive-2012-03-09-04-49-46/Homo_sapiens
        │   └── README.txt
        └── Sequence
            ├── AbundantSequences
            ├── Bowtie2Index         <==== bt2 files 
            ├── BowtieIndex
            ├── BWAIndex
            ├── Chromosomes
            ├── Squashed-Homo_sapiens-Ensembl-GRCh37
            └── WholeGenomeFasta     <==== fa file

18 directories, 2 files
$ ls -l Homo_sapiens/Ensembl/GRCh37/Annotation/Archives/archive-2013-03-06-14-23-04/Genes
total 639136
-rwxrwxr-x 1 brb brb       302 Mar 18  2013 ChromInfo.txt
-rwxrwxr-x 1 brb brb 603096976 Mar 18  2013 genes.gtf
-rwxrwxr-x 1 brb brb   8236175 Mar 18  2013 refFlat.txt.gz
-rwxrwxr-x 1 brb brb  43130092 Mar 18  2013 refGene.txt

A list of files in <Homo_sapiens_UCSC_hg18.tar.gz> can be found on (File:Ucsc hg18.txt) or <Homo_sapiens_UCSC_hg19.tar.gz> can be found on github.

We don't need to extract the whole tarball. We just need to extract some files we need. But how do we know the locations of files we need? Use tar -tzvf XXX.tar.gz to find out.

$ tar -xzvf Homo_sapiens_UCSC_hg18.tar.gz Homo_sapiens/UCSC/hg18/Annotation/Archives/archive-2014-06-02-13-47-56/Genes/genes.gtf
Homo_sapiens/UCSC/hg18/Annotation/Archives/archive-2014-06-02-13-47-56/Genes/genes.gtf
$ tar -xzvf Homo_sapiens_UCSC_hg18.tar.gz Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/genome.3.bt2
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/genome.1.bt2
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/genome.rev.2.bt2
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/genome.rev.1.bt2
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/genome.4.bt2
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/genome.2.bt2
Homo_sapiens/UCSC/hg18/Sequence/Bowtie2Index/genome.fa
$ tar -xzvf Homo_sapiens_UCSC_hg18.tar.gz Homo_sapiens/UCSC/hg18/Sequence/WholeGenomeFasta/genome.fa
Homo_sapiens/UCSC/hg18/Sequence/WholeGenomeFasta/genome.fa
$ tree -L 7 Homo_sapiens
Homo_sapiens
    └── hg18
        ├── Annotation
        │   └── Archives
        │       └── archive-2014-06-02-13-47-56
        │           └── Genes
        │               └── genes.gtf                             <= gtf file
        └── Sequence
            ├── Bowtie2Index
            │   ├── genome.1.bt2                                  <= bt2 file
            │   ├── genome.2.bt2
            │   ├── genome.3.bt2
            │   ├── genome.4.bt2
            │   ├── genome.fa -> ../WholeGenomeFasta/genome.fa    <= fake fa file (symbolic link)
            │   ├── genome.rev.1.bt2
            │   └── genome.rev.2.bt2
            └── WholeGenomeFasta
                └── genome.fa                                     <= fa file

Download GTF (gene transfer format) file (for Tophat)

The compressed file is 77.6 MB size.

wget ftp://ftp.ensembl.org/pub/release-70/gtf/drosophila_melanogaster/Drosophila_melanogaster.BDGP5.70.gtf.gz
gunzip Drosophila_melanogaster.BDGP5.70.gtf.gz

CRITICAL: Make sure that the gene annotation uses the same coordinate system as the reference FASTA le. Here, both les use BDGP5 (i. e., release 5 of the assembly provided by the Berkeley Drosophila Genome Project), as is apparent from the le names. To be on the safe side here, we recommend to always download the FASTA reference sequence and the GTF annotation data from the same resource provider.

Ensembl.org provides both fasta and gtf files together. It's useful when we want to use Galaxy.
Broad also provides a link to download gtf and fasta file.

Preprocess reference FASTA files into an index (fasta -> bt2)

Alternatively, pre-built indices for many commonly-used genomes are available from http://tophat.cbcb.umd.edu/igenomes.html <Drosophila_melanogaster_Ensembl_BDGP5.tar.gz> contains genome.1.bt2, genome.2.bt2, genome.3.bt2, genome.fa, genome.rev.1.bt2, and genome.rev.2.bt2 under Drosophila_melanogaster/Ensembl/BDGP5/Sequence/Bowtie2Index/ directory. This takes about 4 minutes 37 seconds on my Core-i7 Laptop computer running xubuntu 12.04.

When I try to run bowtie2-build on mouse genome, it is extremely slow. Unfortunately bowtie2-build does not have "-p" option like bowtie2 to speed it up.

bowtie2-build -f Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa Dme1_BDGP5_70
brb@brbweb4:~/Anders2013$ ls -lt *.bt2
-rw-r--r-- 1 brb brb 58770281 Feb 10 12:06 Dme1_BDGP5_70.rev.1.bt2
-rw-r--r-- 1 brb brb 40591960 Feb 10 12:06 Dme1_BDGP5_70.rev.2.bt2
-rw-r--r-- 1 brb brb 58770281 Feb 10 12:04 Dme1_BDGP5_70.1.bt2
-rw-r--r-- 1 brb brb 40591960 Feb 10 12:04 Dme1_BDGP5_70.2.bt2
-rw-r--r-- 1 brb brb   339200 Feb 10 12:01 Dme1_BDGP5_70.3.bt2
-rw-r--r-- 1 brb brb 40591953 Feb 10 12:01 Dme1_BDGP5_70.4.bt2

Create a metadata table called "samples"

sri$LibraryName = gsub("S2_DRSC_","",sri$LibraryName) # trim label
samples = unique(sri[,c("LibraryName","LibraryLayout")])
for(i in seq_len(nrow(samples))) {
 rw = (sri$LibraryName==samples$LibraryName[i])
 if(samples$LibraryLayout[i]=="PAIRED") {
   samples$fastq1[i] = paste0(sri$Run[rw],"_1.fastq",collapse=",")
   samples$fastq2[i] = paste0(sri$Run[rw],"_2.fastq",collapse=",")
 } else {
   samples$fastq1[i] = paste0(sri$Run[rw],".fastq",collapse=",")
   samples$fastq2[i] = ""
 }
}

samples$condition = "CTL"
samples$condition[grep("RNAi",samples$LibraryName)] = "KD"
samples$shortname = paste( substr(samples$condition,1,2),
                                         substr(samples$LibraryLayout,1,2), seq_len(nrow(samples)), sep=".")
samples
     LibraryName LibraryLayout
1     Untreated-3        PAIRED
3     Untreated-4        PAIRED
5   CG8144_RNAi-3        PAIRED
7   CG8144_RNAi-4        PAIRED
144   Untreated-1        SINGLE
150 CG8144_RNAi-1        SINGLE
156   Untreated-6        SINGLE
                                                                                            fastq1
1                                                               SRR031714_1.fastq,SRR031715_1.fastq
3                                                               SRR031716_1.fastq,SRR031717_1.fastq
5                                                               SRR031724_1.fastq,SRR031725_1.fastq
7                                                               SRR031726_1.fastq,SRR031727_1.fastq
144 SRR031708.fastq,SRR031709.fastq,SRR031710.fastq,SRR031711.fastq,SRR031712.fastq,SRR031713.fastq
150 SRR031718.fastq,SRR031719.fastq,SRR031720.fastq,SRR031721.fastq,SRR031722.fastq,SRR031723.fastq
156                                                                 SRR031728.fastq,SRR031729.fastq
                                fastq2 condition shortname
1   SRR031714_2.fastq,SRR031715_2.fastq       CTL   CT.PA.1
3   SRR031716_2.fastq,SRR031717_2.fastq       CTL   CT.PA.2
5   SRR031724_2.fastq,SRR031725_2.fastq        KD   KD.PA.3
7   SRR031726_2.fastq,SRR031727_2.fastq        KD   KD.PA.4
144                                           CTL   CT.SI.5
150                                            KD   KD.SI.6
156                                           CTL   CT.SI.7

On the other hand, the <samples.txt> file used in BRB-DGE looks like

$ cat samples.txt
LibraryName     LibraryLayout   fastq1  fastq2
Untreated-3     PAIRED  SRR031714_1.fastq,SRR031715_1.fastq     SRR031714_2.fastq,SRR031715_2.fastq
Untreated-4     PAIRED  SRR031716_1.fastq,SRR031717_1.fastq     SRR031716_2.fastq,SRR031717_2.fastq
CG8144_RNAi-3   PAIRED  SRR031724_1.fastq,SRR031725_1.fastq     SRR031724_2.fastq,SRR031725_2.fastq
CG8144_RNAi-4   PAIRED  SRR031726_1.fastq,SRR031727_1.fastq     SRR031726_2.fastq,SRR031727_2.fastq
Untreated-1     SINGLE  SRR031708.fastq,SRR031709.fastq,SRR031710.fastq,SRR031711.fastq,SRR031712.fastq,SRR031713.fastq
CG8144_RNAi-1   SINGLE  SRR031718.fastq,SRR031719.fastq,SRR031720.fastq,SRR031721.fastq,SRR031722.fastq,SRR031723.fastq
Untreated-6     SINGLE  SRR031728.fastq,SRR031729.fastq

Align the reads to the reference genome using tophat2 (fastq -> bam, bed)

Note:

'-p' specifies the number of threads to use, -o specifies the output directory. The first name (bowind) is the name of the index (built in advance).
It seems even the program cannot find the reference genome, it still runs.
The run time is about 3 to 4 hours per sample when 7 jobs were running at the same time (5 threads for each sample).
If only one sample was run it took 45 minutes as the paper said (5 threads). If I specify 1 thread, the running time is 1 hour 44 minutes.
If I only run 2 jobs using batch, it takes 1 hour and 1 hour 2 mintues to finish these two jobs.
The input is fastq files. The output is a directory contains 7 files (including *.bam) and 1 subdirectory of logs.

gf = "Drosophila_melanogaster.BDGP5.70.gtf"
bowind = "Dme1_BDGP5_70"  # bt2 files
cmd = with(samples, paste("tophat2 -G", gf, "-p 5 -o", LibraryName,
          bowind, fastq1, fastq2))

cmd # Actually we want to use system() to run each line of cmd.
[1] "tophat2 -G Drosophila_melanogaster.BDGP5.70.gtf -p 5 -o Untreated-3 Dme1_BDGP5_70 SRR031714_1.fastq,SRR031715_1.fastq SRR031714_2.fastq,SRR031715_2.fastq"                           
[2] "tophat2 -G Drosophila_melanogaster.BDGP5.70.gtf -p 5 -o Untreated-4 Dme1_BDGP5_70 SRR031716_1.fastq,SRR031717_1.fastq SRR031716_2.fastq,SRR031717_2.fastq"                           
[3] "tophat2 -G Drosophila_melanogaster.BDGP5.70.gtf -p 5 -o CG8144_RNAi-3 Dme1_BDGP5_70 SRR031724_1.fastq,SRR031725_1.fastq SRR031724_2.fastq,SRR031725_2.fastq"                         
[4] "tophat2 -G Drosophila_melanogaster.BDGP5.70.gtf -p 5 -o CG8144_RNAi-4 Dme1_BDGP5_70 SRR031726_1.fastq,SRR031727_1.fastq SRR031726_2.fastq,SRR031727_2.fastq"                         
[5] "tophat2 -G Drosophila_melanogaster.BDGP5.70.gtf -p 5 -o Untreated-1 Dme1_BDGP5_70 SRR031708.fastq,SRR031709.fastq,SRR031710.fastq,SRR031711.fastq,SRR031712.fastq,SRR031713.fastq "  
[6] "tophat2 -G Drosophila_melanogaster.BDGP5.70.gtf -p 5 -o CG8144_RNAi-1 Dme1_BDGP5_70 SRR031718.fastq,SRR031719.fastq,SRR031720.fastq,SRR031721.fastq,SRR031722.fastq,SRR031723.fastq "
[7] "tophat2 -G Drosophila_melanogaster.BDGP5.70.gtf -p 5 -o Untreated-6 Dme1_BDGP5_70 SRR031728.fastq,SRR031729.fastq "

Tophat options

--coverage-search/--no-coverage-search: According to this, it is a step to define possible junctions between exons. If you only want the expression profile of the annotated genes, you can skip this step for speed. The identification of new regions with the coverage is relevant only if you want to detect new splice sites in alternate transcripts or even new genes.
-r/--mate-inner-dist <int>: This is the expected (mean) inner distance between mate pairs. For, example, for paired end runs with fragments selected at 300bp, where each end is 50bp, you should set -r to be 200. The default is 50bp. Check out this post about fragment size & inner size. This post discusses about the fragment size.
-g/--max-multihits <int>: Instructs TopHat to allow up to this many alignments to the reference for a given read, and choose the alignments based on their alignment scores if there are more than this number. The default is 20 for read mapping.
--library-type: The default is unstranded (fr-unstranded). If either fr-firststrand or fr-secondstrand is specified, every read alignment will have an XS attribute tag.
--GTF option: Tophat now takes the approach of mapping the reads on the transcriptome first, with only the unmapped reads being further aligned to the whole genome and going through the novel junction discovery process like before. Please note that the values in the first column of the provided GTF/GFF file (column which indicates the chromosome or contig on which the feature is located), must match the name of the reference sequence in the Bowtie index you are using with TopHat.
-T/--transcriptome-only: only align the reads to the transcriptome and report only those mappings as genomic mappings. Note that we still get junctions map.
-x/--transcriptome-max-hits: Maximum number of mappings allowed for a read, when aligned to the transcriptome (any reads found with more then this number of mappings will be discarded).

Some helpful information:

http://vallandingham.me/RNA_seq_differential_expression.html

Output files

$ ls -lh Untreated-6
total 2.2G
-rw-rw-r-- 1 brb brb 2.0G May  6 18:11 accepted_hits.bam
-rw-rw-r-- 1 brb brb  202 May  6 18:03 align_summary.txt
-rw-rw-r-- 1 brb brb 2.0M May  6 18:03 deletions.bed
-rw-rw-r-- 1 brb brb 612K May  6 18:03 insertions.bed
-rw-rw-r-- 1 brb brb 2.7M May  6 18:03 junctions.bed
drwxrwxr-x 2 brb brb 4.0K May  6 18:11 logs
-rw-rw-r-- 1 brb brb   70 May  6 17:43 prep_reads.info
-rw-rw-r-- 1 brb brb 197M May  6 18:12 unmapped.bam

$ ls -lh Untreated-6/logs
total 108K
-rw-rw-r-- 1 brb brb    0 May  6 18:11 bam_merge_um.log
-rw-rw-r-- 1 brb brb  12K May  6 17:59 bowtie_build.log
-rw-rw-r-- 1 brb brb    0 May  6 17:39 bowtie_inspect_recons.log
-rw-rw-r-- 1 brb brb  221 May  6 17:53 bowtie.left_kept_reads.log
-rw-rw-r-- 1 brb brb  216 May  6 17:59 bowtie.left_kept_reads_seg1.log
-rw-rw-r-- 1 brb brb  216 May  6 17:59 bowtie.left_kept_reads_seg2.log
-rw-rw-r-- 1 brb brb  215 May  6 18:00 bowtie.left_kept_reads_seg3.log
-rw-rw-r-- 1 brb brb  394 May  6 17:59 juncs_db.log
-rw-rw-r-- 1 brb brb  300 May  6 18:00 long_spanning_reads.segs.log
-rw-rw-r-- 1 brb brb  105 May  6 17:43 prep_reads.log
-rw-rw-r-- 1 brb brb  617 May  6 18:03 reports.log
-rw-rw-r-- 1 brb brb    0 May  6 18:07 reports.merge_bam.log
-rw-rw-r-- 1 brb brb   40 May  6 18:05 reports.samtools_sort.log0
-rw-rw-r-- 1 brb brb   40 May  6 18:05 reports.samtools_sort.log1
-rw-rw-r-- 1 brb brb   40 May  6 18:04 reports.samtools_sort.log10
-rw-rw-r-- 1 brb brb   40 May  6 18:05 reports.samtools_sort.log2
-rw-rw-r-- 1 brb brb   40 May  6 18:04 reports.samtools_sort.log3
-rw-rw-r-- 1 brb brb   40 May  6 18:06 reports.samtools_sort.log4
-rw-rw-r-- 1 brb brb   40 May  6 18:06 reports.samtools_sort.log5
-rw-rw-r-- 1 brb brb   40 May  6 18:05 reports.samtools_sort.log6
-rw-rw-r-- 1 brb brb   40 May  6 18:05 reports.samtools_sort.log7
-rw-rw-r-- 1 brb brb   40 May  6 18:04 reports.samtools_sort.log8
-rw-rw-r-- 1 brb brb   40 May  6 18:04 reports.samtools_sort.log9
-rw-rw-r-- 1 brb brb  12K May  6 18:12 run.log
-rw-rw-r-- 1 brb brb  813 May  6 17:58 segment_juncs.log
-rw-rw-r-- 1 brb brb 2.0K May  6 18:12 tophat.log

Output from console

[2014-04-18 16:42:12] Beginning TopHat run (v2.0.10)
-----------------------------------------------
[2014-04-18 16:42:12] Checking for Bowtie
		  Bowtie version:	 2.1.0.0
[2014-04-18 16:42:12] Checking for Samtools
		Samtools version:	 0.1.19.0
[2014-04-18 16:42:12] Checking for Bowtie index files (genome)..
[2014-04-18 16:42:12] Checking for reference FASTA file
[2014-04-18 16:42:12] Generating SAM header for genome
[2014-04-18 16:42:58] Reading known junctions from GTF file
[2014-04-18 16:43:17] Preparing reads
	 left reads: min. length=49, max. length=49, 26721504 kept reads (2587 discarded)
[2014-04-18 16:47:40] Building transcriptome data files E2_Rep2/tmp/genes
[2014-04-18 16:48:22] Building Bowtie index from genes.fa
[2014-04-18 17:12:33] Mapping left_kept_reads to transcriptome genes with Bowtie2 
[2014-04-18 17:44:09] Resuming TopHat pipeline with unmapped reads
[2014-04-18 17:44:09] Mapping left_kept_reads.m2g_um to genome genome with Bowtie2 
[2014-04-18 17:49:50] Mapping left_kept_reads.m2g_um_seg1 to genome genome with Bowtie2 (1/2)
[2014-04-18 17:51:07] Mapping left_kept_reads.m2g_um_seg2 to genome genome with Bowtie2 (2/2)
[2014-04-18 17:51:54] Searching for junctions via segment mapping
	Coverage-search algorithm is turned on, making this step very slow
	Please try running TopHat again with the option (--no-coverage-search) if this step takes too much time or memory.
[2014-04-18 18:08:13] Retrieving sequences for splices
[2014-04-18 18:10:10] Indexing splices
[2014-04-18 18:19:15] Mapping left_kept_reads.m2g_um_seg1 to genome segment_juncs with Bowtie2 (1/2)
[2014-04-18 18:21:26] Mapping left_kept_reads.m2g_um_seg2 to genome segment_juncs with Bowtie2 (2/2)
[2014-04-18 18:23:38] Joining segment hits
[2014-04-18 18:25:52] Reporting output tracks
-----------------------------------------------
[2014-04-18 18:38:05] A summary of the alignment counts can be found in E2_Rep2/align_summary.txt
[2014-04-18 18:38:05] Run complete: 01:55:52 elapsed

Some lessons

The following table summarizes the result of using '-G' and '--transcriptome-index' options. In conclusion, 3 possible ways of using these 2 options:

Not using '-G'
Using '-G' but no '--transcriptome-index'
Run tophat2 one time to generate transcriptome index files and then run tophat2 again with '-G' and '--transcriptome-index' options.

Example 1 (fail)	Wrong spec --transcriptome-index
Example 2 (fail)	Using -G & --transcriptome-index but my files (genes.gtf & genome.*)
Example 3 (OK)	No -G option
Example 4 (OK)	Proper use of using -G & --transcriptome-index (2 steps)
Example 5 (fail)	Using -G & --transcriptome-index using Illumina iGenomes dir
Example 6 (fail)	Like Example 5 but cp genes.gtf to genome.gtf
Example 7 (OK)	Using -G but no --transcriptome-index

More details can be found on my github website.

This post discussed Tophat with and without GTF.

FAQs for Tophat

TopHat2 on multiple samples, avoid building Bowtie index from genes.fa each time?

Is it necessary to run the following code by Tophat? See this post on seqanswers.com.

[2014-09-18 10:38:45] Building transcriptome data files /tmp/genes
[2014-09-18 10:39:21] Building Bowtie index from genes.fa

For other users, which may encounter the same challenge - The trick is to run this command first:

tophat2 -G iGenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf \
       --transcriptome-index=transcriptome_data/known \
       iGenomes/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/genome

and then subsequently call tophat2 with this command:

tophat2 --num-threads 12 --transcriptome-index=transcriptome_data/known \
        iGenomes/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/genome 
        myfastq_R1.fastq.gz myfastq_R2.fastq.gz

After running the above command, you'll see the following 1 line (instead of 2 lines as shown above)

[2014-09-18 12:12:04] Using pre-built transcriptome data..

Which is significantly faster, when running multiple samples.

The UCSC/hg19 data can retrieved like so:

wget ftp://igenome:[email protected]/Homo_sapiens/UCSC/hg19/Homo_sapiens_UCSC_hg19.tar.gz

Organize, sort and index the BAM files and create SAM files (bam -> sam)

Note

Potentially we can run the commands in parallel. This step needs lots of disk I/O so it may be better not to run the command in parallel.
Four steps:
1. first step - Input: original aligned bam file. Output: <Untreated-3_sn.bam> (1.5 GB). sort alignment file by read name (-n option).
2. Second step - Input: sorted bam file from 1st step. Output: <Untreated-3_sn.sam> (11 GB). BAM<->SAM conversion. The SAM file generated from these 2 steps will be used in HTSEQ-count (For paired-end data, the alignment have to be sorted either by read name or by alignment position; see htseq-count documentation).
3. Third step - Input: original aligned bam file. Output: <Untreated-3_s.bam> (1.2 GB). sort alignment file by chromosomal coordinates.
4. Fourth step - Input: sorted bam file from 3rd step. Output: <Untreated-3_s.bam.bai> (375 KB). index alignment. The SAM and BAI files generated from these 2 steps together with GTF file will be used in IGV (IGV requires that both SAM and BAM files be sorted by position and indexed; see IGV documentation).
Examples how to use. See here

If we use samtools view to see the sorted bam files, we may find some fagments (first column) has only one read in this file and some fragments are not in this file.

If we look into the (sorted by name) unmapped.bam file, we can see the unmapped fragments.

for(i in seq_len(nrow(samples))) {
 lib = samples$LibraryName[i]
 ob = file.path(lib, "accepted_hits.bam")
 # sort by name, convert to SAM for htseq-count
 cat(paste0("samtools sort -n ",ob," ",lib,"_sn"),"\n")
 cat(paste0("samtools view -o ",lib,"_sn.sam ",lib,"_sn.bam"),"\n")
 # sort by position and index for IGV
 cat(paste0("samtools sort ",ob," ",lib,"_s"),"\n")
 cat(paste0("samtools index ",lib,"_s.bam"),"\n\n")
}

samtools sort -n Untreated-3/accepted_hits.bam Untreated-3_sn
samtools view -o Untreated-3_sn.sam Untreated-3_sn.bam
samtools sort Untreated-3/accepted_hits.bam Untreated-3_s
samtools index Untreated-3_s.bam

samtools sort -n Untreated-4/accepted_hits.bam Untreated-4_sn
samtools view -o Untreated-4_sn.sam Untreated-4_sn.bam
samtools sort Untreated-4/accepted_hits.bam Untreated-4_s
samtools index Untreated-4_s.bam

samtools sort -n CG8144_RNAi-3/accepted_hits.bam CG8144_RNAi-3_sn
samtools view -o CG8144_RNAi-3_sn.sam CG8144_RNAi-3_sn.bam
samtools sort CG8144_RNAi-3/accepted_hits.bam CG8144_RNAi-3_s
samtools index CG8144_RNAi-3_s.bam 

samtools sort -n CG8144_RNAi-4/accepted_hits.bam CG8144_RNAi-4_sn
samtools view -o CG8144_RNAi-4_sn.sam CG8144_RNAi-4_sn.bam
samtools sort CG8144_RNAi-4/accepted_hits.bam CG8144_RNAi-4_s
samtools index CG8144_RNAi-4_s.bam

samtools sort -n Untreated-1/accepted_hits.bam Untreated-1_sn
samtools view -o Untreated-1_sn.sam Untreated-1_sn.bam
samtools sort Untreated-1/accepted_hits.bam Untreated-1_s
samtools index Untreated-1_s.bam

samtools sort -n CG8144_RNAi-1/accepted_hits.bam CG8144_RNAi-1_sn
samtools view -o CG8144_RNAi-1_sn.sam CG8144_RNAi-1_sn.bam
samtools sort CG8144_RNAi-1/accepted_hits.bam CG8144_RNAi-1_s
samtools index CG8144_RNAi-1_s.bam

samtools sort -n Untreated-6/accepted_hits.bam Untreated-6_sn
samtools view -o Untreated-6_sn.sam Untreated-6_sn.bam
samtools sort Untreated-6/accepted_hits.bam Untreated-6_s
samtools index Untreated-6_s.bam

Output from console

[bam_sort_core] merging from 15 files...

Check Coverage from bam file

Use samtools mpileup or Qualimap software as commented inbiostars.

The output of samtools mpileup has a pileup format. For example, in the GSE37918 dataset,

$ wc -l MDA-MB-231-control_mpileup.txt

$ head -n 2 MDA-MB-231-control_mpileup.txt
1  12060  N  1  ^!C  B
1  12061  N  1  T    C

where each line consists of chromosome, 1-based coordinate, reference base, the number of reads covering the site, read bases and base qualities.

Inspect alignments with IGV

The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.

Launch

I can launch IGV in a terminal (suppose the file IGV_2.3.52.zip is extracted to the directory called binary under the HOME directory):

 ~/binary/IGV_2.3.52/igv.sh

Command line options

bash igv.sh input.bam -g hg19 -b igv_batch_script.txt

And a sample <igv_batch_script.txt> file

new
genome hg19
load input.bam
snapshotDirectory /path/to/igv_screenshots
goto chr19:59418052-59418053
sort base
collapse
snapshot chr19_59418052-59418053.png
goto chr1:15680529-15680532 chr3:5680521-5680533 
sort base
collapse
snapshot chr1_15680529-15680532.chr3_5680521-5680533.png
exit

Genomes

IGV contains a lot of genomes. We can access them by using the drop-down list box and select 'More ...' entry. See Hosted Genomes for more information about the meaning of this list (some of them on the list do not appear on the website though).

To create .genome file, following the steps:

Download reference genome fasta file (bt2 won't work)

wget ftp://ftp.ensembl.org/pub/release-70/fasta/drosophila_melanogaster/dna/Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa.gz
gunzip Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa.gz

Make sure gene model annotation file is available (*.gtf)
If you have the cytoband file, IGV can display the chromosome ideogram. Otherwise, we won't see the ideogram. Unfortunately I don't see a download choice for the cytoband on the ensembl.org website. The UCSC website does provide the cytoband.txt file for downloading.
In IGV, click Genomes > Create .genom File. Enter 'Anders2013' for both Unique identifer & Descriptive name. Browse the right FASTA file and Gene file. Click OK. We can save the file <Anders2013.genome> under the data directory.

If the above steps work, we should be able to prepare a sorted bam file with its index file to be used in IGV. For example,

samtools sort Untreated-6/accepted_hits.bam Untreated-6_s
samtools index Untreated-6_s.bam

Splice junctions

The junctions track calls a splicing event when at least a single read splits across two exons in the alignment track.

IGV defines exons by your sample's read alignments.

Each splice junction is represented by an arc from the beginning to the end of the junction.

Junctions from the + strand are colored red and extend above the center line.
Junctions from the – strand are blue and extend below the center line.

The bed file format is specified in genome.ucsc.edu and a few lines of <junctions.bed> are given below.

brb@brb-T3500:~/Anders2013/Untreated-6$ head junctions.bed 
track name=junctions description="TopHat junctions"
2L	11095	11479	JUNC00000001	4	-	11095	11479	255,0,0	2	74,70	0,314
2L	11271	11479	JUNC00000002	24	-	11271	11479	255,0,0	2	73,70	0,138
2L	11447	11842	JUNC00000003	45	-	11447	11842	255,0,0	2	71,64	0,331

Small test data from Tophat (paired end)

The following screenshot was taken from another (small) dataset with two fq files (paired end). There are 100 sequences in the fq file. The reference genome has only 13 sequences. See also this post.

Before using IGV, we need to run Tophat and samtools (one is to get sorted by position in the reference in bam format, and the other is to get an index file in bai format). Both bam and bai files are binary. If we just use the bam file generated from Tophat in IGV, IGV will show a message 'An index file is required for SAM & BAM files'. See the BAM file requirement in IGV documentation.

wget http://ccb.jhu.edu/software/tophat/downloads/test_data.tar.gz
tar -xzf test_data.tar.gz

# Assume required software are installed; e.g. through BDGE.
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:/opt/RNA-Seq/bin/samtools-0.1.19/bcftools:/opt/RNA-Seq/bin/samtools-0.1.19/misc
export PATH=$bdge_bowtie_PATH:$bdge_samtools_PATH:$bdge_tophat_PATH:$PATH

tophat -r 20 test_ref reads_1.fq reads_2.fq 
samtools sort tophat_out/accepted_hits.bam tophat_out_s  # Output tophat_out_s.bam
samtools index tophat_out_s.bam                          # Output tophat_out_s.bam.bai

The "-r" option in tophat is related to mate-inner-dist. See the discussion here.

Genome -> Load genome from file -> test_ref.fa
File -> load from file -> tophat_out_s.bam (read_1.fq won't work)
We can change the color of alignment by read strand by using the right click menu on *.bam window.

When we launch IGV, it is better to increase the default memory size (2GB). To do that, open igv.sh file and change -Xmx2000m to -Xmx4000m, for example to increase the memory from 2GB to 4GB.

The reference genome (*.fa file) is shown on the bottom of IGV.
Mouse over a sequence will show "Read name" (we can go back to fastq file to double check the sequence), "Location" (the location from mouse over), "Alignment start" (the start position).
If there is a mismatch from a bp, it will be shown in a different color (T-red, C-blue, A-green, G-brown).
The histogram-like plot is called Coverage.
One sequence may be splitted after alignment.
The <align_summary.txt> file (see below for its content) shows the number of reads in input and number of mapped reads with percentage of mapped.
There are 142 reads shown on the IGV (verified by <align_summary.txt>).

The <test_ref.fa> file (reference genome) is shown below. The total length is 50 * 13 = 650 bp. We can verify this by looking at both the reference genome (bottom) and bp scale (top).

>test_chromosome
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
ACTACTATCTGACTAGACTGGAGGCGCTTGCGACTGAGCTAGGACGTGCC
ACTACGGGGATGACGACTAGGACTACGGACGGACTTAGAGCGTCAGATGC
AGCGACTGGACTATTTAGGACGATCGGACTGAGGAGGGCAGTAGGACGCT
ACGTATTTGGCGCGCGGCGCTACGGCTGAGCGTCGAGCTTGCGATACGCC
GTAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAG
ACTATTACTTTATTATCTTACTCGGACGTAGACGGATCGGCAACGGGACT
GTAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAG
TTTTCTACTTGAGACTGGGATCGAGGCGGACTTTTTAGGACGGGACTTGC
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

The <align_summary.txt> file is

Left reads:
          Input     :       100
           Mapped   :        72 (72.0% of input)
Right reads:
          Input     :       100
           Mapped   :        70 (70.0% of input)
71.0% overall read mapping rate.

Aligned pairs:        50
50.0% concordant pair alignment rate.

The first 2 reads (read length is 75) in <reads_1.fq> file looks like

@test_mRNA_150_290_0/1
TCCTAAAAAGTCCGCCTCGGTCTCAGTCTCAAGTAGAAAAAGTCCCGTTGGCGATCCGTCTACGTCCGAGTAAGA
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@test_mRNA_8_197_1/1
TCTGACTAGACTGGAGGCGCTTGCGACTGAGCTAGGACGTGACACTACGGGGATGGCGACTAGGACTACGGACGG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

The first 2 reads in <reads_2.fq> file looks like

@test_mRNA_150_290_0/2
TACGTATTTGTCGCGCGGCCCTACGGCTGAGCGTCGAGCTTGCGATCCGCCACTATTACTTTATTATCTTACTCG
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
@test_mRNA_8_197_1/2
GTATCGCAAGCTCGACGCTCAGCCGTAGGGCCGCGCGCCAAATACGTAGCGTCCTACTGCCCTCCTCAGTCCGAT
+
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

The IGV guide here shows how to interpret the colors for alignment data. For example, read bases that match are displayed in gray.

IGV Channel is a youtube channel where it contains some videos about the IGV.

Paire end sequencing

For paired end data, it is useful to use the right-click menu and select

'View as pairs' option. Pairs will be joined by a line.
Color alignments by read strand.

The alignment of the right read works by 1) reverse the order and 2) apply compliment operator. For example, the following shows the reference sequence on the location of @test_mRNA_6_182_59/2. As we can see if we flip & apply the compliment of the sequence of @test_mRNA_6_182_59/2, they are almost matched except one base pair.

Reference
GGACTATTTAGGACGATCGGACTGAGGAGGGCAGTAGGACGCTACGTATTTGGCGCGCGGCGCTACGGCTGAGCG

@test_mRNA_6_182_59/2
CGCTCAGCCGTAGGGCCGCGCGCCAAATACGTAGCGTCCTACTGCCCTCCTCAGTCCGATCGTCCTAAATAGTCC

SRA000299 (single end)

This data was used in this post.

There are 4 libraries and 6 fastq files. The samples.txt is given below.

LibraryName     LibraryLayout   fastq1  fastq2
Kidney_0007_1.5pM       SINGLE  SRR002324.fastq
Kidney_0007_3pM SINGLE  SRR002320.fastq,SRR002325.fastq
Liver_0007_1.5pM        SINGLE  SRR002322.fastq
Liver_0007_3pM  SINGLE  SRR002321.fastq,SRR002323.fastq

Anders2013 (mix of single & paired end)

This is the data used in this wiki page. The sample meta data can be found at here.

Here we focus on a single-end library Untreated-6 which contains 2 fastq files (SRR031728.fastq, SRR031729.fastq).

In IGV, select the chromosome 2RHet and zoom in to the region of 1,638,560 bp. We are using the ref genome from ensembl.org as Anders2013 paper has used. Note the bp location number always starts at 1 for each chromosome. Here I am focusing on the read SRR031729.8202948 which has a color of a negative strand even the data is single end.

# Find the read sequence. First we find the line number using '-n' option of the 'grep' command
brb@brb-T3500:~/Anders2013$ grep -n 8202948 SRR031729.fastq        
32811789:@SRR031729.8202948 HWI-EAS299:3:65:192:596 length=75
32811791:+SRR031729.8202948 HWI-EAS299:3:65:192:596 length=75

# Next we display the text from line 32811789 to 32811790
brb@brb-T3500:~/Anders2013$ sed -n '32811789,32811790p'  SRR031729.fastq
@SRR031729.8202948 HWI-EAS299:3:65:192:596 length=75
CTTATCCTTTCTCTCTTGTATTTCCTGTGGAGGAAATTGACCTCAACCCATGGACTACCGAAACCTGGCAATATC

Using an online DNA tool we can get the complement and inverse of the sequence (we only need to do this extra step for a negative strand.

GATATTGCCAGGTTTCGGTAGTCCATGGGTTGAGGTCAATTTCCTCCACAGGAAATACAAGAGAGAAAGGATAAG

The reference genome shows the read starts at 2RHet:1638559, Cigar 75M. I write down the sequence by selecting 'Show all bases' in right-click menu. Not that the alignment is 100% for this read.

GATATTGCCAGGTTTCGGTAGTCCATGGGTTGAGGTCAATTTCCTCCACAGGAAATACAAGAGAGAAAGGATAAG

The ref genome seq matched with the complement & reverse seq of the (negative strand; shown in a purple color) read. This verifies the plot.

IGB

It is also a java-based software. Java version 1.8 is required.

It provides similar functions as IGV.
The memory handling is not as good as IGV (tested using Anders2013 data with the same 4000M setting).
There are several tutorial videos for IGB and the user guide is good.

Count reads using htseq-count (sam -> count)

samples$countf = paste(samples$LibraryName, "count", sep=".")
gf = "Drosophila_melanogaster.BDGP5.70.gtf"
cmd = paste0("htseq-count -s no -a 10 ",
            samples$LibraryName, "_sn.sam ",
            gf," > ", samples$countf)

cmd
[1] "htseq-count -s no -a 10 Untreated-3_sn.sam Drosophila_melanogaster.BDGP5.70.gtf > Untreated-3.count"    
[2] "htseq-count -s no -a 10 Untreated-4_sn.sam Drosophila_melanogaster.BDGP5.70.gtf > Untreated-4.count"    
[3] "htseq-count -s no -a 10 CG8144_RNAi-3_sn.sam Drosophila_melanogaster.BDGP5.70.gtf > CG8144_RNAi-3.count"
[4] "htseq-count -s no -a 10 CG8144_RNAi-4_sn.sam Drosophila_melanogaster.BDGP5.70.gtf > CG8144_RNAi-4.count"
[5] "htseq-count -s no -a 10 Untreated-1_sn.sam Drosophila_melanogaster.BDGP5.70.gtf > Untreated-1.count"    
[6] "htseq-count -s no -a 10 CG8144_RNAi-1_sn.sam Drosophila_melanogaster.BDGP5.70.gtf > CG8144_RNAi-1.count"
[7] "htseq-count -s no -a 10 Untreated-6_sn.sam Drosophila_melanogaster.BDGP5.70.gtf > Untreated-6.count"

Output from console

Lots of output

....
37200000 SAM alignment records processed.
37300000 SAM alignment records processed.
37399340 SAM alignments  processed.

Note: Another tool is CuffLinks. See Sean Davis's tutorial.

DESeq for DE analysis

> samplesDESeq = with(samples, data.frame(
         shortname = I(shortname),
         countf = I(countf),
         condition = condition,
         LibraryLayout = LibraryLayout))

> samplesDESeq

 shortname              countf condition LibraryLayout
1   CT.PA.1   Untreated-3.count       CTL        PAIRED
2   CT.PA.2   Untreated-4.count       CTL        PAIRED
3   KD.PA.3 CG8144_RNAi-3.count        KD        PAIRED
4   KD.PA.4 CG8144_RNAi-4.count        KD        PAIRED
5   CT.SI.5   Untreated-1.count       CTL        SINGLE
6   KD.SI.6 CG8144_RNAi-1.count        KD        SINGLE
7   CT.SI.7   Untreated-6.count       CTL        SINGLE

> library("DESeq")
cds <- newCountDataSetFromHTSeqCount(samplesDESeq) # quick, 15682 features, 7 samples
cds <- estimateSizeFactors(cds) # quick
sizeFactors(cds)

#   CT.PA.1   CT.PA.2   KD.PA.3   KD.PA.4   CT.SI.5   KD.SI.6   CT.SI.7
# 0.6991612 0.8104921 0.8217403 0.8941097 1.6431467 1.3720872 1.1041186

cds <- estimateDispersions(cds) # quick
res <- nbinomTest(cds,"CTL","KD") # 44 seconds
sum(res$pval <= .05, na.rm=T) # [1] 1574
sum(res$padj <= .05, na.rm=T) # [1] 730

options(width=100)
res[1:3,]
#            id  baseMean baseMeanA baseMeanB foldChange log2FoldChange      pval padj
# 1 FBgn0000003  0.000000  0.000000  0.000000        NaN            NaN        NA   NA
# 2 FBgn0000008 88.526695 88.486694 88.580029   1.001055    0.001520941 0.9298627    1
# 3 FBgn0000014  3.054418  2.327533  4.023599   1.728697    0.789684778 0.6839858    1

File Format

http://genome.ucsc.edu/FAQ/FAQformat.html which includes a lot of common formats like BAM, BED, bedGraph, bigBed, bigWig, VCF, WIG, et al.
http://stephenturner.us/biofiletypes/

fastq

The wikipedia website provides information to convert FASTQ to FASTA format (when do we want to do that?).

fasta

FASTQ files provide more information than FASTA. This perl program allows to convert FASTA format to FASTQ format while assuming quality score of 40.

The reference genome is saved in FASTA format.

For the fruit fly genome file Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa. The grep -n command can be used to get all chromosome names.

$ head Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa
>2L dna:chromosome chromosome:BDGP5:2L:1:23011544:1 REF
CGACAATGCACGACAGAGGAAGCAGAACAGATATTTAGATTGCCTCTCATTTTCTCTCCC
ATATTATAGGGAGAAATATGATCGCGTATGCGAGAGTAGTGCCAACATATTGTGCTCTTT
GATTTTTTGGCAACCCAAAATGGTGGCGGATGAACGAGATGATAATATATTCAAGTTGCC
GCTAATCAGAAATAAATTCATTGCAACGTTAAATACAGCACAATATATGATCGCGTATGC
GAGAGTAGTGCCAACATATTGTGCTAATGAGTGCCTCTCGTTCTCTGTCTTATATTACCG
CAAACCCAAAAAGACAATACACGACAGAGAGAGAGAGCAGCGGAGATATTTAGATTGCCT
ATTAAATATGATCGCGTATGCGAGAGTAGTGCCAACATATTGTGCTCTCTATATAATGAC
TGCCTCTCATTCTGTCTTATTTTACCGCAAACCCAAATCGACAATGCACGACAGAGGAAG
CAGAACAGATATTTAGATTGCCTCTCATTTTCTCTCCCATATTATAGGGAGAAATATGAT

$ grep -n ">" Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa
1:>2L dna:chromosome chromosome:BDGP5:2L:1:23011544:1 REF
383528:>2LHet dna:chromosome chromosome:BDGP5:2LHet:1:368872:1 REF
389677:>2R dna:chromosome chromosome:BDGP5:2R:1:21146708:1 REF
742124:>2RHet dna:chromosome chromosome:BDGP5:2RHet:1:3288761:1 REF
796938:>3L dna:chromosome chromosome:BDGP5:3L:1:24543557:1 REF
1205999:>3LHet dna:chromosome chromosome:BDGP5:3LHet:1:2555491:1 REF
1248592:>3R dna:chromosome chromosome:BDGP5:3R:1:27905053:1 REF
1713678:>3RHet dna:chromosome chromosome:BDGP5:3RHet:1:2517507:1 REF
1755638:>4 dna:chromosome chromosome:BDGP5:4:1:1351857:1 REF
1778170:>U dna:chromosome chromosome:BDGP5:U:1:10049037:1 REF
1945655:>Uextra dna:chromosome chromosome:BDGP5:Uextra:1:29004656:1 REF
2429067:>X dna:chromosome chromosome:BDGP5:X:1:22422827:1 REF
2802782:>XHet dna:chromosome chromosome:BDGP5:XHet:1:204112:1 REF
2806185:>YHet dna:chromosome chromosome:BDGP5:YHet:1:347038:1 REF
2811970:>dmel_mitochondrion_genome dna:chromosome chromosome:BDGP5:dmel_mitochondrion_genome:1:19517:1 REF

$ wc -l *.fa
2812296 Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa

When I apply the above method to examine the genome.fa files from Ensembl, NCBI and UCSC using the command

grep -n ">" genome.fa | cut -d' ' -f1 | cut -d '>' -f2

I get the following result

Ensembl: 1, 2, 3, ...., X, Y, MT
NCBI: chr1, chr2, ...., chr22, chrX, chrY, chrM, chr1_KI270706v1_random, ....., chrEBV
UCSC: chr1, chr2, ...., chr22, chrX, chrY, chrM

gff/gtf

The GTF file can be downloaded from the Tophat website. After that, extract the file genes.gtf according to the instruction in BDGE website.
GTF file can also be downloaded from
- UCSC Genome Bioinformatics or UCSC Table Browser. See the Mapping reads to the genome tutorial in homer.salk.edu.
- NCBI
- Ensembl
The meaning of Gene isoform in wikipedia. It contains links to TSS, CDS, UTR.
The meaning of transcriptome in wikipedia. Each row in a GTF file contains transcript id and gene name. See gist.
Gene name in GTF file

$ grep KRAS ~/igenome/human/UCSC/hg19/genes.gtf | wc -l
23

What is CDS in GTF file? CDS (contiguous sequence) CDS is the sequence that actually makes proteins. See biostars.org.
What is exon? A region of the transcript sequence within a gene which is not removed from the primary RNA transcript by RNA splicing. See its definition on
GFF3 format from sequenceontology.org

The following output shows how many features from different sources.

brb@brb-T3500:/tmp$ wc -l ~/igenome/human/UCSC/hg19/genome.fa
61913917 /home/brb/igenome/human/UCSC/hg19/genome.fa
brb@brb-T3500:/tmp$ wc -l ~/igenome/human/NCBI/build37.2/genome.fa
44224234 /home/brb/igenome/human/NCBI/build37.2/genome.fa
brb@brb-T3500:/tmp$ wc -l ~/igenome/human/Ensembl/GRCh37/genome.fa
51594937 /home/brb/igenome/human/Ensembl/GRCh37/genome.fa

brb@brb-T3500:~/igenome/human$ wc -l UCSC/hg19/genes.gtf
869204 UCSC/hg19/genes.gtf
brb@brb-T3500:~/igenome/human$ wc -l NCBI/build37.2/genes.gtf
819119 NCBI/build37.2/genes.gtf
brb@brb-T3500:~/igenome/human$ wc -l Ensembl/GRCh37/genes.gtf
2280612 Ensembl/GRCh37/genes.gtf

brb@brb-T3500:~/igenome/human$ R
> x <- read.delim("~/igenome/human/Ensembl/GRCh37/genes.gtf", header=FALSE, as.is=TRUE)
> dim(x)
[1] 2280612       9
> x[1:2, ]
  V1                     V2   V3    V4    V5 V6 V7 V8
1  1   processed_transcript exon 11869 12227  .  +  .
2  1 unprocessed_pseudogene exon 11872 12227  .  +  .
                                                                                                                                                                                          V9
1  exon_id ENSE00002234944; exon_number 1; gene_biotype pseudogene; gene_id ENSG00000223972; gene_name DDX11L1; transcript_id ENST00000456328; transcript_name DDX11L1-002; tss_id TSS15000;
2 exon_id ENSE00002234632; exon_number 1; gene_biotype pseudogene; gene_id ENSG00000223972; gene_name DDX11L1; transcript_id ENST00000515242; transcript_name DDX11L1-201; tss_id TSS190873;

> table(x[, 3])

        CDS        exon start_codon  stop_codon
     794920     1309155       92839       83698
> y <- read.delim("~/igenome/human/NCBI/build37.2/genes.gtf", header=FALSE, as.is=TRUE)
> table(y[, 3])

        CDS        exon start_codon  stop_codon
     345395      405671       34041       34012
> z <- read.delim("~/igenome/human/UCSC/hg19/genes.gtf", header=FALSE, as.is=TRUE)
> table(z[, 3])

        CDS        exon start_codon  stop_codon
     365947      430178       36547       36532

> table(x[, 2])

          3prime_overlapping_ncrna                      ambiguous_orf
                                79                                186
                         antisense                          IG_C_gene
                             24437                                135
                   IG_C_pseudogene                          IG_D_gene
                                16                                 56
                         IG_J_gene                    IG_J_pseudogene
                                41                                  3
                         IG_V_gene                    IG_V_pseudogene
                               649                                232
                           lincRNA                              miRNA
                             28635                               3401
                          misc_RNA                            Mt_rRNA
                              2187                                  2
                           Mt_tRNA                         non_coding
                                22                                 71
           nonsense_mediated_decay                     non_stop_decay
                            196359                                808
            polymorphic_pseudogene               processed_pseudogene
                              1453                              12548
              processed_transcript                     protein_coding
                            159015                            1703902
                        pseudogene                    retained_intron
                              1582                             118186
                              rRNA                     sense_intronic
                               566                               1535
                 sense_overlapping                             snoRNA
                               340                               1613
                             snRNA                                TEC
                              2066                                124
  transcribed_processed_pseudogene transcribed_unprocessed_pseudogene
                               483                               4160
                         TR_C_gene                          TR_D_gene
                                50                                  6
                         TR_J_gene                    TR_J_pseudogene
                               167                                  4
                         TR_V_gene                    TR_V_pseudogene
                               752                                 67
                unitary_pseudogene             unprocessed_pseudogene
                              1234                              13440
> table(y[, 2])

unknown
 819119
> table(z[, 2])

unknown
 869204

The following table gives an example (UCSC/hg19) from one feature. Notice that the last column contains transcript ID, tss ID and gene ID. If the GTF is obtained from Ensembl, this column also has exon ID.

1	seqname	chr1 (called 1 from NCBI & Ensembl)
2	source	unknown
3	feature	exon/transcript
4	start	11874
5	end	12227
6	score	.
7	strand	+
8	frame	0
9	attribute	gene_id "DDX11L1"; gene_name "DDX11L1"; transcript_id "NR_046018"; tss_id "TSS14844";

The gene attributes (gene_id, transcript_id, exon_number, gene_name, gene_biotype, transcript_name, protein_id) appears on the mouse-over pop-up in IGV. See the 1st screenshot below from fly genome (imported manually by myself to IGV). From the 1st screenshot it is clear that one position may contain more than one transcripts.

This post on seqanswers.com has a discussion about the GTF file, exon, intron, CDS, exon, etc. It says

"Exon" refers to transcription and "CDS" to translation. These are two different biological mechanisms.

bed

A BED file (.bed) is a tab-delimited text file that defines a feature track. Bed files can be like GTF/GFF to provide gene annotations. See File Formats section from broadinstitute.org or ensembl.org. Tracks in the UCSC Genome Browser (http://genome.ucsc.edu/) can be downloaded to BED files and loaded into IGV.

A simple bed file looks like

chr7    127471196  127472363

We can also take a look some bed files generated after running the Tophat program.

brb@brb-T3500:~/Anders2013/Untreated-6$ head -n 3 insertions.bed
track name=insertions description="TopHat insertions"
2L      10457   10457   T       1
2L      10524   10524   T       1
brb@brb-T3500:~/Anders2013/Untreated-6$ head -n 3 deletions.bed
track name=deletions description="TopHat deletions"
2L      10832   10833   -       1
2L      12432   12434   -       1
brb@brb-T3500:~/Anders2013/Untreated-6$ head -n 3 junctions.bed
track name=junctions description="TopHat junctions"
2L      11095   11479   JUNC00000001    4       -       11095   11479   255,0,0 2       74                                           ,70     0,314
2L      11271   11479   JUNC00000002    24      -       11271   11479   255,0,0 2       73                                           ,70     0,138

From my observations, the IGV can create the junctions track (it is called 'Untreated-6_s.bam junctions' when I load the <Untreated-6_s.bam> file) automatically once we load the bam file. If we manually load the <junctions.bed> file saved in the 'Untreated-6' directory, we can see both junction tracks are the same.

sam/bam, "samtools view" and Rsamtools

There are 11 required fields in the sam file and the header is optional. See section 1.4 The alignment section: mandatory fields on p4 of the pdf file.

We can use samtools view to view the bam file.

samtools view accepted_hits.bam | head -1000 | less

We can also use Bioconductor's Rsamtools package to manipulate sam/bam, fasta, bcf and tabix files.

source("https://bioconductor.org/biocLite.R")
biocLite("Rsamtools")
library(Rsamtools)
bf1 = BamFileList(file = c("file1.bam", "file2.bam"))
seqinfo(bf1)

We can use the shell command in this post to extract the chromosome name, start position from the bam file. There we can see the difference of the chromosome name of bam files created from using Ensembl and UCSC.

# UCSC hg38
brb@T3600 ~/SeqTestdata/RNASeqFibroblast $ samtools view RNAseq_Bam_hg38/LFB_scramble_repA.bam | head | awk '{print $3}'
chr1
chr1
chr1
chr1
chr1
chr1

# Ensembl GRCh37
brb@T3600 ~/SeqTestdata/RNASeqFibroblast $ samtools view LFB_scramble_repA/accepted_hits.bam | head | awk '{print $3}'
1
1
1
1
1

What's special with rna-seq count data

Overdispersion

Normalization

The Impact of Normalization Methods on RNA-Seq Data Analysis by Zyprych-Walczak 2015. Two public datasets (Bodymap and Cheung) are available from recount website.
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments by Bullard 2010.
Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data by Peipei Li 2015.

Other RNA-Seq data

GEO

Go to http://www.ncbi.nlm.nih.gov/sites/entrez and select 'GEO Datasets' from the drop down menu, and use 'RNA-Seq' as your search term. http://www.ncbi.nlm.nih.gov/gds?term=rna-seq

GSE28666

as used in DAFS paper. The timing is

bowtie2-build 115 minutes
fastq-dump 25 minutes per fastq (3.3 - 5.9GB fastq files)
tophat2 90 minutes per fastq
samtools 5 minutes
htseq-count 13 minutes

GSE51403 (full R script)

RNA-seq differential expression studies: more sequence, or more replication?

An example of R code to download SRA files.

        homo sapiens,
        Illumina HiSeq 2000.
        Question: what reference genome I should use?
        Ans1: hg18 was used.
              See http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM1244821
              Or the paper.

        Ans2: GRCh38:
              Click 'BioProject' in http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51403
                then click 'Genome' in http://www.ncbi.nlm.nih.gov/bioproject/PRJNA222975
                But tophat website only provides GRCh37.
                https://genome.ucsc.edu/goldenPath/newsarch.html shows GRCh38 assembly is released
                in Dec/2013.

        38 fastq/SRR  (all are single lane)
        14 samples/GSM

        GSM#    SRX#    Sample          SRR#
       ---------------------------------------------------------------
Ctrl    1244822 365217  Ctrl_Rep7       1012952, 1012953, 1012954 (3 runs, 70M spots, 3.5G bases)
        1244821 365216  Ctrl_Rep6       949-951 (I just skip prefix '1012')
        1244820 365215  Ctrl_Rep5       946-948 (3 runs)
        1244819 365214  Ctrl_Rep4       943-945 (3 runs)  Control ethanol 24h
        1244818 365213  Ctrl_Rep3       940-942 (3 runs)
        1244817 365212  Ctrl_Rep2       937-939 (3 runs)
        1244816 365211  Ctrl_Rep1       934-936 (3 runs)  Control ethanol 24h

Trmnt   1244815 365210  E2_Rep7         931-933 (3 runs)  10nM E2 treatment for 24h
        1244814 365209  E2_Rep6         928-930 (3 runs)
        1244813 365208  E2_Rep5         925-927 (3 runs)
        1244812 365207  E2_Rep4         923-924 (2 runs)
        1244811 365206  E2_Rep3         921-922 (2 runs)
        1244810 365205  E2_Rep2         919-920 (2 runs)  10nM E2 treatment for 24h
        1244809 365204  E2_Rep1         917-918 (2 runs)  10nM E2 treatment for 24h
# Step 1. Download and convert data

library(SRAdb)
setwd("~/GSE51403")
 if( ! file.exists('SRAmetadb.sqlite') ) {
        # sqlfile <- getSRAdbFile()
   sqlfile <- "~/Anders2013/SRAmetadb.sqlite"
 } else {
sqlfile <- 'SRAmetadb.sqlite'
 }
sra_con <- dbConnect(SQLite(),sqlfile)
fs <- listSRAfile("SRP031476", sra_con, fileType = "sra" )     # 38 files
# getSRAfile("SRP031476", sra_con, fileType = "sra" )          # starting to download

getSRAfile(fs$run[13], sra_con, fileType='sra') # SRR1012917.sra
getSRAfile(fs$run[30], sra_con, fileType='sra') # SRR1012918.sra

dbDisconnect( sra_con )

fs <- dir(pattern='sra')
# for(f in fs)  system(paste("fastq-dump --split-3", f)) # Single thread, Not efficient
scrp <- paste("/opt/RNA-Seq/bin/sratoolkit.2.3.5-2-ubuntu64/bin/fastq-dump --split-3", fs)

library(parallel)
cl <- makeCluster(getOption("cl.cores", 5))
# note that if something is wrong, we need to delete broken fastq first and then run again.
system.time(clusterApply(cl, scrp, function(x) system(x))) 
stopCluster(cl)

# Step 2. Create samples object
samples <- data.frame(LibraryName = c(paste("Ctrl_Rep", 7:1, sep=''),
                                      paste("E2_Rep", 7:1, sep='')),
             LibraryLayout = rep("SINGLE", 14),
             fastq1=c(paste(paste("SRR", 1012952:1012954, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012949:1012951, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012946:1012948, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012943:1012945, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012940:1012942, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012937:1012939, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012934:1012936, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012931:1012933, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012928:1012930, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012925:1012927, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012923:1012924, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012921:1012922, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012919:1012920, ".fastq", sep=''), collapse=','),
                      paste(paste("SRR", 1012917:1012918, ".fastq", sep=''), collapse=',')),
             fastq2=rep("", 14),
             countf=c(paste("Ctrl_Rep", 7:1, ".count", sep=''),
                      paste("E2_Rep", 7:1, ".count", sep='')))
# Step 3: run tophat2
i <- 10
system.time(paste("tophat2 -G genes.gtf -p 5 -o",
                   samples$LibraryName[i],
                   "genome",
                   samples$fastq1[i]))
# system("tophat2 -G genes.gtf -p 5 -o E2_Rep5 genome SRR1012925.fastq")
# system("tophat2 -G genes.gtf -p 5 -o E2_Rep5 genome SRR1012925.fastq,SRR1012926.fastq,SRR1012927.fastq") 
for(i in c(1:14)) {
  system(paste("tophat2 -G genes.gtf -p 5 -o", samples$LibraryName[i], "genome", samples$fastq1[i]))
}

# Step 4. Organize, sort and index the BAM files and create SAM files.
for(i in seq_len(nrow(samples)) {
  lib = samples$LibraryName[i]
  ob = file.path(lib, "accepted_hits.bam")
  # sort by name, convert to SAM for htseq-count
  c1 <- paste0("samtools sort -n ", ob, " ", lib, "_sn")
  c2 <- paste0("samtools view -o ", lib, "_sn.sam ", lib, "_sn.bam")
  system(c1)
  system(c2)
}

samtools sort -n Sample1/accepted_hits.bam Sample1_sn  # 283 seconds
samtools view -o Sample1_sn.sam Sample1_sn.bam         # 38 seconds
# samtools sort Sample1/accepted_hits.bam Sample1_s
# samtools index Sample1_s.bam

# Step 5. Count reads using htseq-count
htseq-count -s no -a 10 Sample1_sn.sam Mus_musculus.GRCm38.70.gtf > Sample1.count # 759 seconds
htseq-count -s no -a 10 Sample2_sn.sam Mus_musculus.GRCm38.70.gtf > Sample2.count
samples$countsf <- paste(samples$LibraryName, "count", sep=".")
gf <- "genes.gtf"
cmd <- paste0("htseq-count -s no -a 10 ", samples$LibraryName, "_sn.sam ", gf, " > ", samples$countf)
system.time(clusterApply(cl, cmd, function(x) system(x))) # 119 minutes


samplesDESeq = data.frame(shortname = c("Sample1", "Sample2"), countf=c("Sample1.count", "Sample2.count"),
                         condition = c("CTL", "KD"), LibraryLayout =c("SINGLE", "SINGLE"))
library("DESeq")
cds = newCountDataSetFromHTSeqCount(samplesDESeq)
cds = estimateSizeFactors(cds)
sizeFactors(cds)

SRA

SRA000299

Paper
Blog

Note that one fastq file can represent several channels/lanes. The lane information can be extracted from fastq definition line (first line of each read). For example,

@SRR002321.1 080226_CMLIVERKIDNEY_0007:2:1:115:885

"SRR002321.1" is the short read archive name, where the .1 is the read number, "080226_CMLIVERKIDNEY_0007" should be the Machine name, which has been arbitrarily changed

"2" is the Channel/lane number
"1" is the Tile number
"115" is the X position
"885" is the Y position

The website also shows how to extract reads with the same run from fastq files.

In this example, there are

two full sequencing runs (this info can be obtained from paper)
4 experiments (SRX000571, SRX000604, SRX000605, SRX000606)
6 runs (SRRR002321, SRR002323, SRR002322, SRR002320, SRR002325, SRR002324)
7 lanes (eg "080226_CMLIVERKIDNEY_0007:2") for each full sequencing run

SRA data linked from QuickNGS

Include Chip-Seq, RNA-Seq, WGS and miRNA-Seq. QuickNGS is a production environment for quick batch-wise analysis of Next-Generation Sequencing (NGS) data in high-throughput laboratories.

European Nucleotide Archive/ENA

DDBJ

ENCODE

http://www.ncbi.nlm.nih.gov/geo/info/ENCODE.html has a list of data with GSE number.

1000 genomes

Human BodyMap 2.0 data from Illumina

http://www.ensembl.info/blog/2011/05/24/human-bodymap-2-0-data-from-illumina/
https://usegalaxy.org/u/jeremy/p/galaxy-rna-seq-analysis-exercise. Galaxy needs to install hg19 genome before we can use tophat or bowtie. See Galaxy/Wiki/Admin/Data Preparation page. Genome can be downloaded from tophat webpage. It is 20GB for hg19! We can also download just chromosome 19 fasta file chr19.fa.gz from UCSC genomes FTP server and it is only 19MB. We can then create reference index files (.bt2) by using bowtie2-build command. If we use Galaxy, we don't need to create reference index files.
Impact of gene annotation on RNA-Seq data analysis

TCGA/The Cancer Genome Atlas

Broad Institute TCGA GDAC
https://wiki.nci.nih.gov/display/TCGA/RNASeq
https://www.biostars.org/p/128132/
RTCGA package from Bioconductor.

The TCGA Data Portal does not host lower levels of sequence data. NCI's Cancer Genomics Hub (CGHub) is the new secure repository for storing, cataloging, and accessing BAM files and metadata for sequencing data (need to 1. register from HHS to get an access 2. request access to the secure data set).

UCI

Machine learning repository (not RNA-Seq)

ReCount

ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets
http://bowtie-bio.sourceforge.net/recount/
Two datasets were used in Getting the most out of RNA-seq data analysis

Other RNA-Seq tools

http://en.wikipedia.org/wiki/List_of_RNA-Seq_bioinformatics_tools

Galaxy

The first 3 lines will install galaxy on local computer. The last command will start Galaxy. PS. It will take a while to start the first time.

hg clone https://bitbucket.org/galaxy/galaxy-dist/
cd galaxy-dist
hg update stable
sh run.sh

A screenshot of the available tools is given below. Strangely, I did not find "NGS: RNA Analysis" in the NGS: ToolBox on left hand side.

We cannot upload > 2GB fastq data. For such large dataset (we have one 3.3 GB fastq in this case), Galaxy asks to use http or ftp to load the data. I install Apache in Ubuntu and put this fastq file there for upload.

Galaxy does not include bowtie, tophat, .... by default. See this post and Galaxy wiki about the installation. Another option is to modify line 614 of 'universe_wsgl.ini' file and enable ourselves as adminstrator. Also modify line 146 to add a path for installing tools through Galaxy Admin page.

If you submit a lot of jobs (eg each tophat2 generates 5 jobs) some of jobs cannot be run immediately. In my experience, there is a 25 jobs cap. Running jobs has a yellow background color. Waiting for run jobs has a gray background color.

Some helpful information

https://wiki.galaxyproject.org/CitingGalaxy Citation
https://wiki.galaxyproject.org/Learn Search rna-seq or NGS as keywords.
https://biostar.usegalaxy.org/
Galaxy & DESeq2

Anders2013

Required tools

Installation

Change PATH variable

Data directory

Subdirectory

Pipeline

Download example data (22 files in SRA format)

Convert SRA to FASTQ (sra -> fastq)

Access quality control through ShortRead or FastQC

Read quality issues

Download reference genome (for Tophat)

Structure of downloaded reference genome from Tophat

Download GTF (gene transfer format) file (for Tophat)

Preprocess reference FASTA files into an index (fasta -> bt2)

Create a metadata table called "samples"

Align the reads to the reference genome using tophat2 (fastq -> bam, bed)

Output files

Output from console

Some lessons

FAQs for Tophat

Organize, sort and index the BAM files and create SAM files (bam -> sam)

Output from console

Check Coverage from bam file

Inspect alignments with IGV

Launch

Command line options

Genomes

Small test data from Tophat (paired end)

Paire end sequencing

SRA000299 (single end)

Anders2013 (mix of single & paired end)

Count reads using htseq-count (sam -> count)

Output from console

DESeq for DE analysis

File Format

bed

sam/bam, "samtools view" and Rsamtools

What's special with rna-seq count data

Overdispersion

Normalization

Other RNA-Seq data

GEO

GSE28666

GSE51403 (full R script)

GSE37544

GSE11045

GSE37704

GSE64570, GSE69244, GSE72165

SRA

SRA000299

SRA data linked from QuickNGS

ENCODE

Human BodyMap 2.0 data from Illumina

UCI

ReCount

Other RNA-Seq tools

Cufflink -> Cuffcompare or Cuffmerge -> Cuffdiff

Forum

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