Latest revision as of 15:31, 8 October 2024

The data is used in the paper "Count-based differential ....." by Anders et al 2013. The paper has been cited 8136 times according to scholar.google.com and 4330 times according to PubMed on Feb 7, 2019.

This page focuses on RNA-Seq. For DNA-Seq see the Genome page.

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 and pysam package
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. For example, we can use it to remove low quality reads (<30).

java -jar trimmomatic-0.36.jar -phred33 -threads 8 file1.fastq.gz file2.fastq.gz -baseout file.fastq.gz AVGQUAL:30

java -jar trimmomatic-0.36.jar -phred33 -threads 8 file1.fastq.gz file2.fastq.gz -baseout file.fastq.gz HEADCROP:5 MINLEN:50 AVGQUAL:20

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.

HTSeq and pysam

HTSeq requires Python 2 and pysam package. For pysam package, we have two ways to install it

1. Use pip

sudo apt-get install python-pip
# sudo -H pip install pysam
sudo -H pip install pysam==0.10.0  # lock the version

The "-H" flag is used to suppress a warning message.

The latest version of pysam may fail so it is not good to use the command pip install pysam.

$ python
>>> import pysam
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/pysam/__init__.py", line 5, in <module>
    from pysam.libchtslib import *
  File "pysam/libchtslib.pyx", line 1, in init pysam.libchtslib (pysam/libchtslib.c:12515)
ImportError: dlopen(/Library/Python/2.7/site-packages/pysam/libcutils.so, 2): Library not loaded: @rpath/pysam-0.11.2.2-py2.7-macosx-10.12-intel.egg/pysam/libcsamtools.so
  Referenced from: /Library/Python/2.7/site-packages/pysam/libcutils.so
  Reason: image not found

2. Use python to build/install.

wget https://pypi.python.org/packages/de/03/02934438b204565bc5231f38a11da840a3c3e4b2beac8c8770d675770668/pysam-0.9.1.4.tar.gz
tar xzvf pysam-0.9.1.4.tar.gz
sudo apt-get -y install zlib1g-dev
sudo apt-get install python-setuptools
python setup.py build
sudo python setup.py install 
# Installed /usr/local/lib/python2.7/dist-packages/pysam-0.9.1.4-py2.7-linux-x86_64.egg

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.

Other pipelines:

RNA-Seq Data Analysis: From Raw Data Quality Control to Differential Expression Analysis, Sep 2017. Bash and R scripts are included.

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)

(Update) fasterq-dump command. Note there is no --gzip option as in fastq-dump. Use gzip to do that.

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

FASTQ PAIR

FASTQ PAIR Rewrite paired end fastq files to make sure that all reads have a mate and to separate out singletons

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

Encoding

Encoding Starting in Illumina 1.8, the quality scores have basically returned to the use of the Sanger format (Phred+33).
all the FASTQ sequences from ncbi are Phred+33 (same as Sanger).

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

transcriptome_data

If we specify --transcriptome-index=transcriptome_data/known in tophat2 command, we shall get a directory transcriptome with several files

known.1.bt2
known.2.bt2
known.3.bt2
known.4.bt2
known.ver
known.fa
known.fa.tlst
known.gff

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.

To create the transcriptome index folder, it is not necessary to allocate too much memory or multithreads. The 'swarm' and 'jobload' commands show 0.3 GB memory and 1 thread is used even we allocate more memory and more CPUs. It took about 15 minutes to create the folder when the following code is used

tophat2 -p 2 \
    -G "/fdb/igenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf" \
    --transcriptome-index=transcriptome_data/known \
    "/fdb/igenomes/Homo_sapiens/UCSC/hg19/Sequence/Bowtie2Index/genome"

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.

Or use igvtools https://hpc.nih.gov/apps/IGV.html

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

Note that in addition to the bam file, we need to run samtools index XXX.bam to create the bai file before IGV can use it.

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.

See an example of it in IGV from GTF.

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

By default IGV won't show the splice junction. Before loading data, check Show junction track in the Alignment Preferences panel. The panel's settings must be adjusted for your data as default settings are for genomic reads for which splicing is irrelevant.

Below is an example from BRB-SeqTools GSE11209 dataset (Saccharomyces_cerevisiae 酵母屬 yeast).

brb@T3600 ~/Downloads/testdata/GSE11209/dT_ori $ cat junctions.bed 
track name=junctions description="TopHat junctions"
I	142233	142632	JUNC00000001	3	+	142233	142632	255,0,0	2	20,13	0,386
II	60161	60721	JUNC00000002	6	-	60161	60721	255,0,0	2	32,24	0,536
II	332859	333403	JUNC00000003	1	+	332859	333403	255,0,0	2	16,17	0,527
...

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 
# sort by location and index
samtools sort tophat_out/accepted_hits.bam -o tophat_out_s.bam -@ 11  # 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.

Other visualization tools

CHAPTER V : Visualizing next generation sequencing data

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.

-r pos option and 'Maximum alignment buffer size exceeded error'

If the input bam file is sorted by position, we can use -r pos option in HTSeq.scripts.count program. However, I got an error complaining Maximum alignment buffer size exceeded while pairing SAM alignments. Following the author's suggestion on the post, it is better to sort the bam file by read name and skip the -r option.

Performance

http://seqanswers.com/forums/showthread.php?t=18920 htseq-count was designed to use very little memory] (0.1GB from my observation). But the price is it is single thread and may takes several several hours. Note: Another tool is CuffLinks. See Sean Davis's tutorial.

Error: 'pair_alignments' needs a sequence of paired-end alignments

http://seqanswers.com/forums/showthread.php?t=41785 & https://www.biostars.org/p/111221/

Run the following to check if there are single-end in the bam files

samtools view -cF 1 bamfile

It it has, run the following to separate single-end and paired-end reads

samtools view -bf 1 foo.bam > foo.paired-end.bam
samtools view -bF 1 foo.bam > foo.single-end.bam

Then run htseq-count separately and then combine the counts.

summarizeOverlaps() from GenomicAlignments package

http://www.bioconductor.org/packages/release/bioc/vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf

Note that GenomicAlignments can read BAM files.

ReadCounter - htseq alternative

it's multi threaded
can produce the same result as htseq
java based

featureCount from Subread

Subread: high-performance read alignment, quantification and mutation discovery
featureCounts: a ultrafast and accurate read summarization program

DESeq2 normalization

Analyzing RNA-seq data with DESeq2
DESeq2详细用法
https://github.com/mgonzalezporta/TeachingMaterial/blob/master/doc/25.normalising.md
http://www.sthda.com/english/wiki/rna-sequencing-data-analysis-counting-normalization-and-differential-expression
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 by Love, Huber and Anders, 2014. Keywords: LFC (logarithmic fold change), shrinkage for dispersion estimation, MAP (maximum a posteriori), Shrinkage estimation of logarithmic fold changes, Wald test, Cook's distance for outlier detection. R packages: DESeq2, GenomicRanges and GenomicAlignments.

install.packages("~/Downloads/DESeq2paper_1.3.tar.gz", repos = NULL, type="source")
source("http://bioconductor.org/biocLite.R") # https:// not work on my R 3.2.5 Ubuntu
biocLite("DESeq2", ask=FALSE)
# Got the following error because RcppArmidillo requires the following two libraries
# /usr/bin/ld: cannot find -llapack
# /usr/bin/ld: cannot find -lblas
# Go to ubuntu software center search liblapack and libblas
# select the following 6 components
#   libblas3gf, libblas-doc, libblas-dev, liblapack3gf, liblapack-doc, liblapack-dev
# Then double check ls -l /usr/lib/liblapack*
#                   ls -l /usr/lib/libblas*
biocLite("GenomicRanges", ask=FALSE)
biocLite("GenomicAlignments", ask=FALSE)
install.packages("caret", repos="https://cran.rstudio.com")

The count matrix and metadata, including the gene model and sample information, are stored in an S4 class derived from the SummarizedExperiment class of the GenomicRanges package. SummarizedExperiment objects containing count matrices can be easily generated using the summarizeOverlaps() function of the GenomicAlignments package.
Log2 fold-change & DESeq2 model in a nutshell (youtube)

Input

DESeqDataSetFromHTSeqCount(sampleTable, directory, design)
# sampleTable is like colData in the next 2 functions

DESeqDataSetFromTximport(txi, colData, design)
# txi is obtained by tximport()

DESeqDataSetFromMatrix(countData, colData, design)
# count data input

DESeqDataSet(se, design) 
# e.g. the ''SummarizedExperiment'' object created by tximeta()
# 'airway' package/object is an example

collapseReplicates

dds <- makeExampleDESeqDataSet(m=12)

# make data with two technical replicates for three samples
dds$sample <- factor(sample(paste0("sample",rep(1:9, c(2,1,1,2,1,1,2,1,1)))))
dds$run <- paste0("run",1:12)

ddsColl <- collapseReplicates(dds, dds$sample, dds$run)
# dds$sample is a grouping variable

# examine the colData and column names of the collapsed data
colData(ddsColl)
colnames(ddsColl)
# DataFrame with 9 rows and 4 columns
#         condition   sample         run runsCollapsed
#          <factor> <factor> <character>   <character>
# sample1         A  sample1        run6     run6,run9
# sample2         B  sample2       run12         run12
# sample3         A  sample3        run2          run2
# sample4         A  sample4        run5     run5,run7
# sample5         A  sample5        run1          run1
# sample6         A  sample6        run4          run4
# sample7         B  sample7        run8    run8,run10
# sample8         A  sample8        run3          run3
# sample9         B  sample9       run11         run11

# check that the sum of the counts for "sample1" is the same
# as the counts in the "sample1" column in ddsColl
matchFirstLevel <- dds$sample == levels(dds$sample)[1]
stopifnot(all(rowSums(counts(dds[,matchFirstLevel])) == counts(ddsColl[,1])))

Normalized counts = raw counts/sizeFactor

How to access the normalized data of a DESeqDataSet
How exactly DESeq2 correct for differences in sequencing depth and library composition to get normalized counts?
- To normalize for sequencing depth and RNA composition, DESeq2 uses the median of ratios method. On the user-end there is only one step, but on the back-end there are multiple steps involved. The algorithm performs multiple steps such as computing the pseudo-reference: geometric mean for each gene across all samples.
StatQuest: DESeq2 part 1: Library Normalization (480p only)
1. take the log of all the values (log0 = -Inf)
2. average each row
3. filter out genes with infinity
4. subtract the average log value from the log(counts)
5. calculate the median of the ratios for each sample
6. convert the medians to "normal numbers" to get the final scaling factors for each sample (exp(these medians))
7. divide the original read counts by the scaling factors
Summary
1. Calculate the normalization factor (size factor) for each sample:
  1. Create a pseudo-reference sample: For each gene, calculate the geometric mean of counts across all samples. This geometric mean serves as a pseudo-reference sample.
  2. Calculate the ratio of each sample to the reference: For each gene in each sample, divide the count by the geometric mean (pseudo-reference) calculated in step 1. This gives the ratio of each sample to the reference.
  3. For each sample, calculate the median of the ratios obtained in step 2. This median value is the size factor for that sample.
2. Calculate the normalized count values: Divide the raw counts for each gene in each sample by the size factor for that sample. This gives the normalized count values.

Differential expression analysis for sequence count data for DESeq package from Anders & Huber, 2010. If we want normalized counts, we need to run 1) estimateSizeFactors() for the normalization, and 2) counts(dds, normalized = TRUE) to extract normalized counts or normTransform(dds) on log2 scale. The normalization is part of the DESeq() function so you can extract counts after running that as well.

source("http://bioconductor.org/biocLite.R"); biocLite("pasilla")
directory <- system.file("extdata", package="pasilla", mustWork=TRUE)
sampleFiles <- grep("treated",list.files(directory),value=TRUE)
sampleCondition <- sub("(.*treated).*","\\1",sampleFiles)
sampleTable <- data.frame(sampleName = sampleFiles,
                           fileName = sampleFiles,
                           condition = sampleCondition)
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
                                        directory = directory,
                                        design= ~ condition)
dds <- ddsHTSeq[ rowSums(counts(ddsHTSeq)) > 1, ]
dds <- estimateSizeFactors(dds)
filteredlog = counts(dds, normalized=T)
filteredlog0 = counts(dds, normalized=F)

filteredlog0[1:5, 1:5]
#                 treated1fb.txt treated2fb.txt treated3fb.txt untreated1fb.txt untreated2fb.txt
# FBgn0000008:003              0              1              0                1                1
# FBgn0000008:004              1              0              1                0                1
# FBgn0000008:005              4              1              1                2                2
# FBgn0000008:007             18              8              7                8               11
# FBgn0000008:008              4              1              0                0                1

scale(filteredlog0[1:5, ], center=F, scale=sizeFactors(dds))[, 1:5]
#                 treated1fb.txt treated2fb.txt treated3fb.txt untreated1fb.txt untreated2fb.txt
# FBgn0000008:003      0.0000000       1.246732       0.000000        0.9712273        0.6178182
# FBgn0000008:004      0.6815046       0.000000       1.093313        0.0000000        0.6178182
# FBgn0000008:005      2.7260184       1.246732       1.093313        1.9424546        1.2356364
# FBgn0000008:007     12.2670828       9.973855       7.653188        7.7698183        6.7960001
# FBgn0000008:008      2.7260184       1.246732       0.000000        0.0000000        0.6178182

filteredlog[1:5, 1:5]
#                 treated1fb.txt treated2fb.txt treated3fb.txt untreated1fb.txt untreated2fb.txt
# FBgn0000008:003      0.0000000       1.246732       0.000000        0.9712273        0.6178182
# FBgn0000008:004      0.6815046       0.000000       1.093313        0.0000000        0.6178182
# FBgn0000008:005      2.7260184       1.246732       1.093313        1.9424546        1.2356364
# FBgn0000008:007     12.2670828       9.973855       7.653188        7.7698183        6.7960001
# FBgn0000008:008      2.7260184       1.246732       0.000000        0.0000000        0.6178182

# Retrieve raw counts and size factors
raw_counts <- counts(dds)
size_factors <- sizeFactors(dds)

# Manually calculate normalized counts
manual_normalized_counts <- sweep(raw_counts, 2, size_factors, FUN = "/")

# Retrieve normalized counts from DESeq2
normalized_counts <- counts(dds, normalized = TRUE)

# Compare the two matrices
all.equal(manual_normalized_counts, normalized_counts)

High-Throughput Count Data

http://web.stanford.edu/class/bios221/book/Chap-CountData.html

estimateSizeFactorsForMatrix()

We can compare the output of estimateSizeFactorsForMatrix() with what you get by simply taking the column sums. See High-Throughput Count Data.

Use a robust regression to estimate the size factor instead of taking the sum of reads per sample.

The output estimateSizeFactorsForMatrix() is a vector of the number of samples.

estimateDispersions()

NB dispersion in DESeq2 correspond to Gamma-Poisson parametrisation?

dds <- makeExampleDESeqDataSet() # 1000 x 12
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
length(dispersions(dds))  # [1] 1000

plotDispEsts(dds) # x=mean of normalized counts, y=dispersion
# plots the per-gene dispersion estimates together with 
# the fitted mean-dispersion relationship

So the output of estimateDispersions() is a vector of the number of genes.

Two factors that we use to decide if we use shrinkage: high dispersion values, low gene counts
Estimating Dispersion

DESeq() and gene filtering

Remove genes with very low counts for all samples or let DESeq2 perform independent filtering? The safest threshold would be to not filter anything above row sum of 0... And our recommendation (and the default in DESeq2) is to let the genefilter software optimize the threshold, such that sensitivity (statistical power) is maximized.
plotMA(), plotCounts
DESeq: low p-values for genes with zero counts in all but one sample of the compared groups

Combine two DESeq2 objects (dds) for analysis

How to combine two DESeq2 objects (dds) for analysis

Designs and contrasts

A guide to designs and contrasts in DESeq2
How to correct for a covariate in DESeq2 that is linear with the condition of interest. Nested design.
DESeq2 experimental design, 2 controls and 2 treated. Goal is to see how/if the two cell types respond differently to treatment.
- ~celltype + celltype:treatment
Deseq2 design formula and analysis with internal controls. Goal is to compare two groups by taking the treatment into consideration.
- Factor1: group/cell-type
- Factor2: treatment

results(contrast)

?results
Syntax: contrast=c('variable', 'numerator', 'denominator'). Example: contrast=c('Trt', 'Yes', 'No')

Example 1: two conditions

dds <- makeExampleDESeqDataSet(m=4)

dds <- DESeq(dds)
res <- results(dds, contrast=c("condition","B","A"))  # mean_B - mean_A
res
#log2 fold change (MLE): condition B vs A 
#Wald test p-value: condition B vs A 
#DataFrame with 1000 rows and 6 columns
#          baseMean log2FoldChange     lfcSE      stat    pvalue      padj
#         <numeric>      <numeric> <numeric> <numeric> <numeric> <numeric>
#gene1      2.18621      -3.029859  2.910105 -1.041151  0.297806  0.999351
#gene2     14.97050      -0.850521  1.112673 -0.764395  0.444632  0.999351
#gene3      9.31174       0.448988  1.379610  0.325446  0.744844  0.999351
#gene4     84.12136       0.333454  0.593804  0.561556  0.574419  0.999351
#gene5     21.52635      -0.538152  1.025702 -0.524667  0.599815  0.999351

counts(dds, normalized = T)[1:3, ]
#        sample1    sample2  sample3   sample4
#gene1  6.812072  0.9935038  0.00000 0.9392723
#gene2 11.677838 26.8246037 15.74392 5.6356338
#gene3  7.785225  7.9480307 18.69590 2.8178169

colData(dds)
#DataFrame with 4 rows and 2 columns
#        condition sizeFactor
#         <factor>  <numeric>
#sample1         A    1.02759
#sample2         A    1.00654
#sample3         B    1.01627
#sample4         B    1.06465

# gene 1
> mean(c(6.81, .99)) # condition A
[1] 3.9
> mean(c(0, 0.939))  # condition B
[1] 0.4695

Example 2: two conditions, one continuous variable (age)

set.seed(1234)
dds <- makeExampleDESeqDataSet(n=100,m=12)
dds$age <- rpois(12, 50)
design(dds) <- ~ condition + age
dds <- DESeq(dds) 
resultsNames(dds)
# [1] "Intercept"        "condition_B_vs_A" "age"

results(dds)  # OR  results(dds, name="age")
#log2 fold change (MLE): age 
#Wald test p-value: age 
#DataFrame with 100 rows and 6 columns
#         baseMean log2FoldChange     lfcSE       stat    pvalue      padj
#        <numeric>      <numeric> <numeric>  <numeric> <numeric> <numeric>
#gene1    2.022210    -0.04620020 0.1445006 -0.3197232  0.749178  0.920829
#gene2   24.571867     0.00360519 0.0484505  0.0744097  0.940684  0.991664
#gene3   82.491501     0.00153923 0.0329532  0.0467095  0.962745  0.991664

results(dds, contrast=c("condition","B","A")) 
# OR results(dds, name="condition_B_vs_A")

#log2 fold change (MLE): condition B vs A 
#Wald test p-value: condition B vs A 
#DataFrame with 100 rows and 6 columns
#         baseMean log2FoldChange     lfcSE      stat    pvalue      padj
#        <numeric>      <numeric> <numeric> <numeric> <numeric> <numeric>
#gene1    2.022210      3.0976656  1.854100  1.670711 0.0947788  0.910079
#gene2   24.571867     -0.1667077  0.635550 -0.262305 0.7930867  0.982211
#gene3   82.491501      0.2188290  0.431659  0.506948 0.6121910  0.982211

Example 3: two conditions, two genotypes, with an interaction term

set.seed(1234)
dds <- makeExampleDESeqDataSet(n=100,m=12)
dds$genotype <- factor(rep(rep(c("I","II"),each=3),2))

design(dds) <- ~ genotype + condition + genotype:condition
dds <- DESeq(dds) 
resultsNames(dds)
# [1] "Intercept"             "genotype_II_vs_I"     
# [3] "condition_B_vs_A"      "genotypeII.conditionB"

# the condition effect for genotype I (the main effect)
results(dds, contrast=c("condition","B","A"))

# the condition effect for genotype II
# this is, by definition, the main effect *plus* the interaction term
# (the extra condition effect in genotype II compared to genotype I).
results(dds, list( c("condition_B_vs_A","genotypeII.conditionB") ))

# the interaction term, answering: is the condition effect *different* across genotypes?
results(dds, name="genotypeII.conditionB")

results(alpha, independentFiltering)

By default alpha=.1, Set independentFiltering=FALSE if we don't like independent filtering.

Negative binomial regression and returned from results()

A matrix with columns:

baseMean: mean of normalized counts of all samples, normalizing for sequencing depth. It does not take into account gene length. The base mean is used in DESeq2 only for estimating the dispersion of a gene. See DESeq2 baseMean counts.
```
rowMeans(counts(dds, normalized=TRUE))
```
log2FoldChange log2FoldChange calculation in DESeq2 output, 2019 STAT115 Lect7.4 DESeq2 by Shirley Liu. Theory behind DESeq2 & Null hypothesis. Introduction to DGE from hbctraining.
[math]\displaystyle{ \begin{align} K_{ij} & \sim NB(\mu_{ij}, \alpha_i) \\ \mu_{ij} &= s_{j} q_{ij} \\ \log_2(q_{ij}) &= x_{j} \beta_i \end{align} }[/math]
[math]\displaystyle{ s_j }[/math] is the size factor for sample 𝑗, accounting for differences in sequencing depth.
[math]\displaystyle{ q_{ij} }[/math] is the normalized mean count for gene 𝑖 in sample 𝑗.
The coefficients [math]\displaystyle{ \beta_i }[/math] estimate give the log2 fold changes for gene i for each column of the model matrix [math]\displaystyle{ X }[/math].
- In a simple linear regression [math]\displaystyle{ y = \beta_0 + \beta_1 x + \epsilon }[/math], if the covariate X takes only 0 or 1, we can show that the slope estimate [math]\displaystyle{ \frac{\sum(x_i-\bar{x})(y_i-\bar{y})}{\sum(x_i -\bar{x})^2} }[/math] can be simplified to the difference of group means [math]\displaystyle{ \bar{y}_1 - \bar{y}_0 }[/math]. This matches the idea of the fold change in gene expression.
- You can obtain standard log fold changes (no shrinkage) by using: DESeq(dds, betaPrior=FALSE)
The null hypothesis being tested is: [math]\displaystyle{ H_0: \beta_i = 0 }[/math]. This tests whether the log fold change for the condition is zero, meaning there is no differential expression for gene 𝑖 between conditions.
lfcSE standard error value (lfcSE) returned by DeSeq2
Why the logFC is different between edgeR,DESeq2 results and fpkm logFC(log2(FPKM_tumor_mean/FPKM_normal_mean)? LogFCs are computed by negative binomial generalized linear models.
Why my log2fcs are too high?
stat: negative binomial GLM fitting for βi and Wald statistics by nbinomWaldTest(). log2Foldchange除以标准差所得
pvalue & padj (Exploring DESeq2 results: Wald test from hbctraining)

Fold-change with continuous variables

how limma work with continous variables. It's the slope/fitted coefficient of the line.

Normalization-based vs log-ratio transformation-based methods

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods
If we use log2(normalized count + 1)，有很低counts的基因将倾向于主导结果。作为一种解决方案，DESeq2为counts数据提供了stabilize the variance across the mean的转换。其中之一是regularized-logarithm transformation or rlog2。对于counts较高的基因，rlog转换可以得到与普通log2转换相似的结果。然而，对于counts较低的基因，所有样本的值都缩小到基因的平均值。See DESeq2详细用法 and the section Data transformations and visualization from the DESeq2 vignette.

Error with DESeq2

Error with DESeq2 “every gene contains at least one zero”

Source code

counts(), normalizationFactors(), `normalizationFactor<-`, estimateSizeFactors(). DESeq2 -> R -> methods.R
How can I extract normalized read count values from DESeq2 results?

? normalizationFactors
dds <- makeExampleDESeqDataSet(n=100, m=4)

normFactors <- matrix(runif(nrow(dds)*ncol(dds),0.5,1.5),
                      ncol=ncol(dds),nrow=nrow(dds),
                      dimnames=list(1:nrow(dds),1:ncol(dds)))

normFactors <- normFactors / exp(rowMeans(log(normFactors)))
normalizationFactors(dds) <- normFactors

dds <- DESeq(dds)
res = results(dds)
res[order(res$padj), ] %>% head

class(dds)
showMethods(class="DESeqDataSet") # a long list

isS4(assays(dds))    # [1] TRUE
slotNames(assays(dds))
# [1] "listData"        "elementType"     "elementMetadata" "metadata"       
names(assays(dds))
# [1] "counts"               "normalizationFactors" "mu"  "H"    

all(counts(dds) / normFactors == counts(dds, norm=T)) # TRUE

DESeq normalization 2010 ("median ratio")

edgeR normalization

RNA Sequence Analysis in R: edgeR

library(baySeq)
data(mobData)
head(mobData)
data(mobAnnotation)
#?mobAnnotation
head(mobAnnotation)
library(edgeR)
d <- DGEList(counts=mobData,group=factor(mobDataGroups))
d
d.full <- d # keep the old one in case we mess up
head(d.full$counts)
head(cpm(d))
apply(d$counts, 2, sum) # total gene counts per sample

d <- calcNormFactors(d)
d

?calcNormFactors. If ‘object’ is a ‘matrix’, the output is a vector with length ‘ncol(object)’ giving the relative normalization factors.

y <- matrix( rpois(1000, lambda=5), nrow=200 )
calcNormFactors(y)

dge <- DGEList(M)
dge <- calcNormFactors(dge)
logCPM <- cpm(dge, log=TRUE)  # section 2.16 of vignette

DESeq2 for DE analysis

By default, DESeq(test = "Wald"). Another test option is LRT.

Vignette. Keyword: Wald test, coefficients [math]\displaystyle{ \beta }[/math], nbinomWaldTest. The coefficients βi give the log2 fold changes for gene i for each column of the model matrix X.

# Suppose the condition has 2 levels
dds <- DESeqDataSetFromMatrix(countData = round(counts), 
                              colData = DataFrame(condition),
                              design = ~ condition)
dds <- DESeq(dds)
res <- results(dds)
res$stat  # same as coef(dds)[, 2]/coef(dds, SE=T)[, 2]

# coef(): extracting the matrix beta_{ir} for all genes i and model coefficients r
# In the case of 2 conditions, the 1st column of coef() is intercept
#    and the 2nd column of coef() is the condition.
all(res$stat == coef(dds)[, 2]/coef(dds, SE=T)[, 2], na.rm=T )
# [1] TRUE

all(res$log2FoldChange == coef(dds)[, 2] )
all(res$lfcSE == coef(dds, SE=T)[, 2] )

log2FoldChange represents the log normalized counts from the mean of level 2 vs level 1. But it is not clear how to calculate that exactly. See DESEq2 log2FoldChange and lfcSE calculations.
According to ?results, The lfcSE gives the standard error of the log2FoldChange. For the Wald test, stat is the Wald statistic: the log2FoldChange divided by lfcSE, which is compared to a standard Normal distribution to generate a two-tailed pvalue.
Differential expression analysis with DESeq2

Videos from Chipster Tutorials https://www.youtube.com/watch?v=5tGCBW3_0IA

> 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

DESeqDataSet methods

counts(): This method returns the raw count data for the genes in the DESeqDataSet object.
colData(): This method returns the metadata (e.g., sample information) for the samples in the DESeqDataSet object.
rowRanges()
design()
sizeFactors(): This method returns the size factors for the DESeqDataSet object, which are used to adjust for library size differences between samples.
rld(): This method returns the regularized-logarithm (rlog) transformed data for the DESeqDataSet object, which is a variance-stabilizing transformation that can be used for visualization and clustering.
plotDispEsts(): This method generates a plot of the dispersion estimates for the DESeqDataSet object, which can be used to diagnose the fit of the model.
plotCounts(): This method generates a plot of the raw count data for the DESeqDataSet object, which can be used to visualize the distribution of the data.
results(): This method returns the results of the differential expression analysis for the DESeqDataSet object, including the log2FoldChange values and the p-values.
resultsNames()
coef()
plotMA(): MA plot

Volcano plot:

EnhancedVolcano::EnhancedVolcano(). Note:

There are no default values for x and y parameters. All examples in the vignette use y = ‘pvalue’ (not ‘padj’).
pCutoff means a p-value, not fdr adjustment

EnhancedVolcano(res,
    lab = rownames(res),
    pCutoff = 1e-05,
    FCcutoff = 1.0,
    x = 'log2FoldChange',
    y = 'pvalue')

To draw adjusted p-values on the y-axis and use adjusted p-values as a cutoff (however, the label for some sig genes are missing. IOW, ggplot2 solution is better)

EnhancedVolcano(res,
    lab = rownames(res),
    pCutoff = 0.05,
    pCutoffCol = "padj", 
    FCcutoff = 1.0,
    ylim = c(0, max(-log10(res$padj), na.rm = TRUE)),
    labSize = 2.5,
    x = 'log2FoldChange',
    y = 'padj',
    title = 'Volcano plot',
    ylab = bquote(~-Log[10]~'Adjusted p-value'),
    legendLabels = c('NS', 'Log2 FC', 'Adjusted p-value', 'Adjusted p-value & Log2 FC'))

ggplot2

ggplot(res, aes(x = log2FoldChange, y = -log10(pvalue))) + 
  geom_point() +
  xlab("log2 Fold Change") + ylab("-log10(p-value)") +
ggtitle("Volcano Plot")

If we like to add vertical and horizontal cutoff lines and change to use adjusted p-values (padj column), we can follow Making Volcano Plots With ggplot2:

library(ggplot2)
library(ggrepel)

data <- res
data$gene <- rownames(res)
ggplot(data, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(color = padj < 0.05 & abs(log2FoldChange) > 1), alpha = 0.5) +
    scale_color_manual(values = c("black", "red"), na.translate = F) +
    geom_vline(
      xintercept = c(-1, 1),
      linetype = 'dashed'
    ) +
    geom_hline(
      yintercept = -log10(.05),
      linetype = 'dashed'
    ) +
    theme_bw() +
    labs(title = "Volcano Plot", x = "Log2 Fold Change", y = "-Log10 Adjusted P-Value") +
    geom_label_repel(
        data = significant_genes,
        aes(label = gene),
        size=3,
        box.padding = 0.25,     # default
        point.padding = 1e-06,  # default
        max.overlaps = 10       # default
    )

Pre-filter

DESeq2 vignette. row sum >= 10.
Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes 2022.

betaPrior is FALSE by default

Different results in DESeq2 when using betaPrior=TRUE and betaPrior=FALSE

lfcShrink

Getting NAs in p-values

Getting NAs in both p-values (normal and adjusted) in DEseq2

Comparison

A comparison study on modeling of clustered and overdispersed count data for multiple comparisons Kruppa 2020

Wald vs Likelihood ratio test

HELP - DESeq2 difference between LRT and Wald Test in RNA-seq/ATAC-seq.
- The Wald tests are specific to a given contrast. You are specifically asking if there is evidence that the coefficient comparing two groups is larger than zero... I would imagine that the vast majority of people use Wald tests exclusively, as they are mostly wanting to make specific comparisons.
- If you fit a full and reduced model and then compute a likelihood ratio test, you are testing if the full model fits the data better than the reduced model. It's an omnibus test that tells you that at least one coefficient is significant, much like an F-test in a conventional linear regression.

DESeq2, edgeR, limma

DESeq2, edgeR, limma 差异分析结果比较
How do I explain the difference between edgeR, LIMMA, DESeq etc. to experimental Biologist/non-bioinformatician.
- DESeq and EdgeR are very similar and both assume that no genes are differentially expressed.
- Limma / Voom is different in that it normalises via the very successful (for microarrays) quantile nomalisation, where an attempt is made to match gene count distributions across samples in your dataset.
Differential Expression with Limma-Voom

DESeq2 paired multifactor test

t-test vs DESeq2

FPKM t-test vs DEseq2. t-tests are parametric, based on the normal distribution. t-tests are expected to yield far fewer DEGs simply because at low sample size they're massively underpowered, please read papers to learn why. That is the basis why methods such as DESeq2 even exist. RNA-seq is not normally distributed, that is well-known.

Time series

Interaction

Complex DESeq2 Experimental Design

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?).

fastq format

A FASTQ file normally uses four lines per sequence.

@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

Line 1 begins with a '@' character and is followed by a sequence identifier and an optional description (like a FASTA title line).
Line 2 is the raw sequence letters.
Line 3 begins with a '+' character and is optionally followed by the same sequence identifier (and any description) again.
Line 4 encodes the quality values for the sequence in Line 2, and must contain the same number of symbols as letters in the sequence.

It seems line 3 & 4 are not required. See the simulated fastq files on http://genomespot.blogspot.com/2014/11/dna-aligner-accuracy-bwa-bowtie-soap.html. I can use BWA to run alignment without a problem:)

Paired end

Forward and reverse reads in paired-end sequencing

The paired files can be two formats. See Bioinformatics at COMAV

Format1:

Fastq file 1
@molecule_1 1st_read_from_pair
@molecule_2 1st_read_from_pair
@molecule_3 1st_read_from_pair

Fastq file 2
@molecule_1 2nd_read_from_pair
@molecule_2 2nd_read_from_pair
@molecule_3 2nd_read_from_pair

Format 2:

Interleaved Fastq file
@molecule_1 1st_read_from_pair
@molecule_1 2nd_read_from_pair
@molecule_2 1st_read_from_pair
@molecule_2 2nd_read_from_pair
@molecule_3 1st_read_from_pair
@molecule_3 2nd_read_from_pair

Note from my experiment, if we flip the two fastq files order in the BWA command, the resulting bam file will diff at FLAG (2nd) column. The final vcf file is not changed.

FastQValidator

http://genome.sph.umich.edu/wiki/FastQValidator

fasta

FASTA format is a text-based format for representing nucleotide sequences . cf. FASTQ files provide more information than FASTA since FASTQ format stores both a biological sequence (usually nucleotide sequence) and its corresponding quality scores.

The reference genome is saved in FASTA format.

Normally the first line in a FASTA file starts with a ">" (greater-than) symbol. Subsequent lines starting with a semicolon would be ignored by software. Following the initial line (used for a unique description of the sequence) is the actual sequence itself in standard one-letter code. A multiple sequence FASTA format would be obtained by concatenating several single sequence FASTA files.

For the fruit fly genome file Drosophila_melanogaster.BDGP5.70.dna.toplevel.fa. The grep -n command can be used to get the row numbers of some keyword (chromosome names in this case).

$ 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

For human,

$ grep -n ">" ~/igenomes/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa
1:>chrM
334:>chr1
4985348:>chr2
9849337:>chr3
13809787:>chr4
17632874:>chr5
21251181:>chr6
24673484:>chr7
27856259:>chr8
30783541:>chr9
33607811:>chr10
36318507:>chr11
39018639:>chr12
41695678:>chr13
43999077:>chr14
46146069:>chr15
48196698:>chr16
50003795:>chr17
51627701:>chr18
53189247:>chr19
54371828:>chr20
55632340:>chr21
56594939:>chr22
57621032:>chrX
60726445:>chrY

chromosome names

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
# for example
grep -n ">" ~/Downloads/Homo_sapiens.GRCh38.dna.chromosome.22.fa | cut -d' ' -f1 | cut -d '>' -f2  
# 22

I get the following result

Ensembl:
1. GRCh37: 1, 2, 3, ...., X, Y, MT
2. GRCh38: 1, 2, 3, ... (see note below)
NCBI:
1. build37.1, build37.2: 1, 2, 3, ....,X, Y, MT
2. GRCh38: chr1, chr2, ...., chr22, chrX, chrY, chrM, chr1_KI270706v1_random, ....., chrEBV
UCSC:
1. hg18, hg19: chr1, chr2, ...., chr22, chrX, chrY, chrM
2. hg38: chr1, chr2, ..., chr22, chrX, chrY, chrM, ..., chrUn_GL000218v1, chrEBV

Note that

Neither Illumina or Biowulf provide GRCh38 from Ensembl (maybe for a good reason). Ensembl (http://ftp.ensembl.org/pub/release-76/fasta/homo_sapiens/dna) does provide GRCh38.
GRCh38 from Ensembl is still following its GRCh37 convention; it won't include 'chr' on their chromosome names. That is, even the same name GRCh38 was used in both Ensembl and NCBI, they have different conventions.

chromosome sizes

https://www.biostars.org/p/134807/

GENOMEFA=Arabidopsis_thaliana.TAIR10.23.dna.genome.fa

# Method 1: use pyfaidx
# upgrade pip from 8.1.2 to 9.0.1
sudo pip install --upgrade pip
sudo pip install pyfaidx
$ cd /tmp
$ faidx $GENOMEFA -i chromsizes > sizes.genome
$ cat sizes.genome
Pt	154478
Mt	366924
4	18585056
2	19698289
3	23459830
5	26975502
1	30427671
# Method 2: use samtools
$ /opt/SeqTools/bin/samtools-1.3/samtools faidx $GENOMEFA
$ cat $GENOMEFA.fai
Pt	154478	51	60	61
Mt	366924	157155	60	61
4	18585056	530246	60	61
2	19698289	19425104	60	61
3	23459830	39451749	60	61
5	26975502	63302628	60	61
1	30427671	90727773	60	61
$ cut -f1,2 $GENOMEFA.fai > sizes.genome
$ cat sizes.genome2
Pt	154478
Mt	366924
4	18585056
2	19698289
3	23459830
5	26975502
1	30427671

R approach

library(Biostrings)
x <- readDNAStringSet("~/igenomes/Homo_sapiens/UCSC/hg38/Sequence/WholeGenomeFasta/genome.fa")
y <- width(x[1:24])
barplot(y, las = 2)
y
#     chr1      chr2      chr3      chr4      chr5      chr6      chr7      chr8 
# 248956422 242193529 198295559 190214555 181538259 170805979 159345973 145138636 
#      chr9     chr10     chr11     chr12     chr13     chr14     chr15     chr16 
# 138394717 133797422 135086622 133275309 114364328 107043718 101991189  90338345 
#     chr17     chr18     chr19     chr20     chr21     chr22      chrX      chrY 
#  83257441  80373285  58617616  64444167  46709983  50818468 156040895  57227415

Remove/Add 'chr' from fasta

sed 's/chr//g' ucsc.hg19.fa >ucsc.hg19.nochr.fa
# or
cat hg19.fa | sed 's/>chr/>/g' > hg19_new.fa

Similarly to add 'chr' to fasta:

cat hg19.fa | sed 's/>/>chr/g' > hg19_new.fa

Convert Fasta to Fastq

It is OK to use Fasta file as the input sequencing files in alignment programs.

If we still want to convert FASTA to FASTQ, two methods are given with an example here. Note quality scores are padded.

Convert Fastq to Fasta

Note the quality scores are discarded.

https://wikis.utexas.edu/display/bioiteam/Mapping+tutorial
https://www.biostars.org/p/85929/. Use cat test.fastq | paste - - - - | sed 's/^@/>/g'| cut -f1-2 | tr '\t' '\n' .

$ cat test.fastq
@SRR002051.3554692 :8:290:671:993 length=33
CCATTCCCCAAAACAGTAAAACCACACACCGAG
+SRR002051.3554692 :8:290:671:993 length=33
40&&+(&#$&3""%$#"#%$)%"%"%$/%%""#
@SRR002051.3554693 :8:290:941:550 length=33
ACTAACATCCCATAATCGTACCTCCTACCATCA
+SRR002051.3554693 :8:290:941:550 length=33
IIII*I$+(/II'$&:I"%""""&"&$%"'""I
$ cat test.fastq | paste - - - - | sed 's/^@/>/g'| cut -f1-2 | tr '\t' '\n'
>SRR002051.3554692 :8:290:671:993 length=33
CCATTCCCCAAAACAGTAAAACCACACACCGAG
>SRR002051.3554693 :8:290:941:550 length=33
ACTAACATCCCATAATCGTACCTCCTACCATCA

Convert fastq files to two fasta files with one containing the reads and another containing the quality scores. In the example below we try to separate paired end reads generated by BEERS program.

$ head simulated_reads_testbeers.fa
>seq.1a
CGAAGAAGGACCCAAAGATGACAAGGCTCACAAAGTACACCCAGGGCAGTTCATACCCCATGGCATCTTGCATCCAGTAGAGCACATCGGTCCAGCCTTC
>seq.1b
GCTCGAGCTGTTCCTTGGACGAATGCACAAGACGTGCTACTTCCTGGGATCCGACATGGAAGCGGAGGAGGACCCATCGCCCTGTGCATCTTCGGGATCA
>seq.2a
GCCCCAGCAGAGCCGGGTAAAGATCAGGAGGGTTAGAAAAAATCAGCGCTTCCTCTTCCTCCAAGGCAGCCAGACTCTTTAACAGGTCCGGAGGAAGCAG
>seq.2b
ATGAAGCCTTTTCCCATGGAGCCATATAACCATAATCCCTCAGAAGTCAAGGTCCCAGAATTCTACTGGGATTCTTCCTACAGCATGGCTGATAACAGAT
>seq.3a
CCCCAGAGGAGCGCCACCTGTCCAAGATGCAGCAGAACGGCTACGAAAATCCAACCTACAAGTTCTTTGAGCAGATGCAGAACTAGACCCCCGCCACAGC
$ awk 'NR % 4 == 1 {print ">" $0 } NR % 4 == 2 {print $0}' simulated_reads_testbeers.fa > my.fasta
$ head my.fasta
>>seq.1a
CGAAGAAGGACCCAAAGATGACAAGGCTCACAAAGTACACCCAGGGCAGTTCATACCCCATGGCATCTTGCATCCAGTAGAGCACATCGGTCCAGCCTTC
>>seq.2a
GCCCCAGCAGAGCCGGGTAAAGATCAGGAGGGTTAGAAAAAATCAGCGCTTCCTCTTCCTCCAAGGCAGCCAGACTCTTTAACAGGTCCGGAGGAAGCAG
>>seq.3a
CCCCAGAGGAGCGCCACCTGTCCAAGATGCAGCAGAACGGCTACGAAAATCCAACCTACAAGTTCTTTGAGCAGATGCAGAACTAGACCCCCGCCACAGC
>>seq.4a
CGGGTGGAGGCTGGCCAAGCAACGAAGGAGATAAAATTGCAGCTGGCATTGTGTGTGCGCGCGCCCGCGGCGGCGGCCAGCAGGGGTGCGCGCGGAGCGG
>>seq.5a
CGCTCACCTTCAATGTCACTGGCAAACATACTGCCTACCAGCTACTCCTCAGCCTTACACCTTAAGCATACGTCCTGCTCTTACTCTTCGAGTTGAGGGT
$ awk 'NR % 4 == 0 {print ">" $0 } NR % 4 == 3 {print $0}' simulated_reads_testbeers.fa > my.fasta
$ head my.fasta
>seq.1b
>GCTCGAGCTGTTCCTTGGACGAATGCACAAGACGTGCTACTTCCTGGGATCCGACATGGAAGCGGAGGAGGACCCATCGCCCTGTGCATCTTCGGGATCA
>seq.2b
>ATGAAGCCTTTTCCCATGGAGCCATATAACCATAATCCCTCAGAAGTCAAGGTCCCAGAATTCTACTGGGATTCTTCCTACAGCATGGCTGATAACAGAT
>seq.3b
>GTTTGATACAGCTAAATTCTTTACAGTACACAAAACCCATTAATAATGTAGTATAGAGACCAAAATGTAAAGAGAGATAGAATACATTACTGATGTGTGG
>seq.4b
>CGGGGCCCCGCCCGCGGCTAGGGAGGTGGCCGCCCTGGCCCGAGCCTGCTGCCCACGGCCGCGGCACTCAGCTCGCACTCCTTCAGCCCGGGAGGCCGGG
>seq.5b
>ACTCCCTGATAGTCTATGGAAGGAAAATGACAACTATTTTAGAATATTTCTAGTTTGTTTTTTCAGTGATCTTTTCATCCAGGCCTTGTTACTGTTACAG

https://gif.biotech.iastate.edu/converting-fastq-fasta
Bioconductor packages Biostrings or shortread https://support.bioconductor.org/p/38338/

GATK

When I use Ensembl/GRCh37 as the reference to run GATK, I got an error

##### ERROR MESSAGE: Input files /home/brb/SeqTestdata/usefulvcf/hg19/gatk/common_all_20160601.vcf and reference have incompatible contigs. Please see http://gatkforums.broadinstitute.org/discussion/63/input-files-have-incompatible-contigsfor more information. Error details: No overlapping contigs found.
##### ERROR   /home/brb/SeqTestdata/usefulvcf/hg19/gatk/common_all_20160601.vcf contigs = [chr1, chr2, chr3, chr4, chr5, chr6, chr7, chr8, chr9, chr10, chr11, chr12, chr13, chr14, chr15, chr16, chr17, chr18, chr19, chr20, chr21, chr22, chrX, chrY]
##### ERROR   reference contigs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, Y, MT]

The conclusion is I need to check if the chromosome name in dbsnp vcf file (ftp://ftp.ncbi.nih.gov/snp/organisms/) and reference fa file has the same naming convention.

For example, for Ensemble/GRCh37, I should NOT use the GATK version (i.e. no 'chr' was added) from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/VCF/

(P.S. For UCSC hg19, we shall use the GATK version ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh37p13/VCF/GATK/)

But for UCSC/hg38, I should use the GATK version ('chr' was added) from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606_b147_GRCh38p2/VCF/GATK/. Read the readme

So I end up with the file structure in my PC

usefulvcf/hg19/common_all*.vcf (no 'chr' was added)
~~usefulvcf/hg38/gatk/common_all*.vcf~~ usefulvcf/hg38/chr/common_all*.vcf ('chr' was added)

gff/gtf

Comparing Ensembl GTF and cDNA/ensembl's Fasta and GTF annotation files do not match
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
GTF file can change. For example there are 7 versions of hg19 reference genome,

brb@T3600 ~ $ ls -l ~/igenomes/Homo_sapiens/UCSC/hg19/Annotation/
total 4
drwxrwxr-x 9 brb brb 4096 Jul 24  2015 Archives
lrwxrwxrwx 1 brb brb   30 Mar 10 03:03 Genes -> Archives/archive-current/Genes
lrwxrwxrwx 1 brb brb   35 Mar 10 03:03 README.txt -> Archives/archive-current/README.txt
lrwxrwxrwx 1 brb brb   33 Mar 10 03:03 SmallRNA -> Archives/archive-current/SmallRNA
lrwxrwxrwx 1 brb brb   34 Mar 10 03:03 Variation -> Archives/archive-current/Variation
brb@T3600 ~ $ ls -lH ~/igenomes/Homo_sapiens/UCSC/hg19/Annotation/Archives
total 28
drwxrwxr-x 4 brb brb 4096 Mar 15  2012 archive-2010-09-27-22-25-17
drwxrwxr-x 5 brb brb 4096 Mar 15  2012 archive-2011-01-27-18-25-49
drwxrwxr-x 5 brb brb 4096 Mar 15  2012 archive-2011-08-30-21-45-18
drwxrwxr-x 5 brb brb 4096 Mar 15  2012 archive-2012-03-09-03-24-41
drwxrwxr-x 5 brb brb 4096 Oct  1  2013 archive-2013-03-06-11-23-03
drwxrwxr-x 5 brb brb 4096 Jun  2  2014 archive-2014-06-02-13-47-56
drwxrwxr-x 5 brb brb 4096 Jul 24  2015 archive-2015-07-17-14-32-32
lrwxrwxrwx 1 brb brb   27 Mar 10 03:35 archive-current -> archive-2015-07-17-14-32-32

GTF file is used in RNA-Seq reads alignment and count reads.
- Tophat - If this option is provided, TopHat will first extract the transcript sequences and use Bowtie to align reads to this virtual transcriptome first. Only the reads that do not fully map to the transcriptome will then be mapped on the genome. The reads that did map on the transcriptome will be converted to genomic mappings (spliced as needed) and merged with the novel mappings and junctions in the final tophat output.
- STAR - GTF file (containing annotated transcripts) can be used in creating STAR index files (--sjdbGTFfile option in STAR --runMode genomeGenerate). STAR will extract splice junctions from this file and use them to greatly improve accuracy of the mapping. Note that in the mapping step there is no need to supply the GTF file again.
- htseq-count: one of two required inputs (the other one is a sorted bam file).
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 one feature and the the feature's transcript id and gene name. See gist.
Gene name in GTF file

$ grep KRAS ~/igenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/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 ~/igenomes/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa
61913917 /home/brb/igenomes/Homo_sapiens/UCSC/hg19/Sequence/WholeGenomeFasta/genome.fa
brb@brb-T3500:/tmp$ wc -l ~/igenomes/Homo_sapiens/NCBI/build37.2/Sequence/WholeGenomeFasta/genome.fa
44224234 /home/brb/igenomes/Homo_sapiens/NCBI/build37.2/Sequence/WholeGenomeFasta/genome.fa
brb@brb-T3500:/tmp$ wc -l ~/igenomes/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa
51594937 /home/brb/igenomes/Homo_sapiens/Ensembl/GRCh37/Sequence/WholeGenomeFasta/genome.fa

brb@brb-T3500:~/igenomes/Homo_sapiens$ wc -l UCSC/hg19/Annotation/Genes/genes.gtf
869204 UCSC/hg19/Annotation/Genes/genes.gtf
brb@brb-T3500:~/igenomes/Homo_sapiens$ wc -l NCBI/build37.2/Annotation/Genes/genes.gtf
819119 NCBI/build37.2/Annotation/Genes/genes.gtf
brb@brb-T3500:~/igenomes/Homo_sapiens$ wc -l Ensembl/GRCh37/Annotation/Genes/genes.gtf
2280612 Ensembl/GRCh37/Annotation/Genes/genes.gtf

An example from UCSC/hg19. As you can see from IGV and the output of the first few rows,

the locations of each rows are not exclusive. For example, rows #3 and #4 are overlapped that matches with what we saw on IGV (fly example)
one row represents one feature; one transcript consists several features
one gene (e.g. WASH7P) can be split over multiple regions (UCSC/hg19 example).

$ more ~/igenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf
chr1    unknown exon    11874   12227   .       +       .       gene_id "DDX11L1"; gene_name "DDX11L1"; transcript_id "NR_046018"; tss_id "TSS16932";
chr1    unknown exon    12613   12721   .       +       .       gene_id "DDX11L1"; gene_name "DDX11L1"; transcript_id "NR_046018"; tss_id "TSS16932";
chr1    unknown exon    13221   14409   .       +       .       gene_id "DDX11L1"; gene_name "DDX11L1"; transcript_id "NR_046018"; tss_id "TSS16932";
chr1    unknown exon    14362   14829   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    14970   15038   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    15796   15947   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    16607   16765   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    16858   17055   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    17233   17368   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    17369   17436   .       -       .       gene_id "MIR6859-3"; gene_name "MIR6859-3"; transcript_id "NR_107063_2"; tss_id "TSS25677";
chr1    unknown exon    17369   17436   .       -       .       gene_id "MIR6859-2"; gene_name "MIR6859-2"; transcript_id "NR_107062_1"; tss_id "TSS25677";
chr1    unknown exon    17369   17436   .       -       .       gene_id "MIR6859-4"; gene_name "MIR6859-4"; transcript_id "NR_128720_2"; tss_id "TSS25677";
chr1    unknown exon    17369   17436   .       -       .       gene_id "MIR6859-1"; gene_name "MIR6859-1"; transcript_id "NR_106918_2"; tss_id "TSS25677";
chr1    unknown exon    17606   17742   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    17915   18061   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    18268   18366   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    24738   24891   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    29321   29370   .       -       .       gene_id "WASH7P"; gene_name "WASH7P"; transcript_id "NR_024540"; tss_id "TSS8568";
chr1    unknown exon    30366   30503   .       +       .       gene_id "MIR1302-11"; gene_name "MIR1302-11"; transcript_id "NR_036268"; tss_id "TSS10004";

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 (DNA->RNA) and "CDS" to translation (RNA->Proteins). These are two different biological mechanisms.

rtracklayer package

See also the vignette of SingscoreAMLMutations

brb@brb-T3500:~/igenomes/Homo_sapiens$ R
> x <- read.delim("~/igenomes/Homo_sapiens/Ensembl/GRCh37/Annotation/Genes/genes.gtf", header=FALSE, as.is=TRUE)
> dim(x)
[1] 2280612       9
> x[1:2, 1:8]
  V1                   V2         V3    V4    V5 V6 V7 V8
1  1 processed_transcript       exon 11869 12227  .  +  .
2  1 processed_transcript transcript 11869 14409  .  +  .
> strsplit(x[1:2, 9], ";") # not the same number of elements so we cannot apply as.data.frame()
[[1]]
 [1] "exon_id ENSE00002234944"        " exon_number 1"
 [3] " gene_biotype pseudogene"       " gene_id ENSG00000223972"
 [5] " gene_name DDX11L1"             " gene_source ensembl_havana"
 [7] " transcript_id ENST00000456328" " transcript_name DDX11L1-002"
 [9] " transcript_source havana"      " tss_id TSS15145"

[[2]]
[1] "gene_biotype pseudogene"        " gene_id ENSG00000223972"
[3] " gene_name DDX11L1"             " gene_source ensembl_havana"
[5] " transcript_id ENST00000456328" " transcript_name DDX11L1-002"
[7] " transcript_source havana"      " tss_id TSS15145"

> table(x[, 3])

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

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

        CDS        exon start_codon  stop_codon
     365947      430178       36547       36532

> options(width=120)
> table(x[, 2])

          3prime_overlapping_ncrna                          antisense                          IG_C_gene
                                92                              40326                                290
                   IG_C_pseudogene                          IG_D_gene                          IG_J_gene
                                40                                196                                 78
                   IG_J_pseudogene                          IG_V_gene                    IG_V_pseudogene
                                12                               1099                                657
                           lincRNA                              miRNA                           misc_RNA
                             48588                               6848                               4380
                           Mt_rRNA                            Mt_tRNA            nonsense_mediated_decay
                                 4                                 44                             293471
                    non_stop_decay             polymorphic_pseudogene               processed_pseudogene
                              1132                               1853                              24516
              processed_transcript                     protein_coding                         pseudogene
                            172582                            1978244                               2247
                   retained_intron                               rRNA                     sense_intronic
                            150034                               1140                               2474
                 sense_overlapping                             snoRNA                              snRNA
                              1255                               3242                               4148
  transcribed_processed_pseudogene transcribed_unprocessed_pseudogene    translated_processed_pseudogene
                              1182                               7931                                  3
                         TR_C_gene                          TR_D_gene                          TR_J_gene
                                58                                  9                                246
                   TR_J_pseudogene                          TR_V_gene                    TR_V_pseudogene
                                 8                                869                                107
                unitary_pseudogene             unprocessed_pseudogene
                              1467                              13763
> table(y[, 2])

unknown
 819119
> table(z[, 2])

unknown
 869204

Another example. Read GTF file into R.

library(rtracklayer)
x <- import("/fdb/igenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf")
x
# GRanges object with 1006819 ranges and 9 metadata columns:
length(unique(x$gene_name))
# [1] 26364
length(unique(x$transcript_id))
# [1] 54070

y <- read.delim("/fdb/igenomes/Homo_sapiens/UCSC/hg19/Annotation/Genes/genes.gtf")
dim(y)
# [1] 1006818       9
colnames(y)
# [1] "chr1"
# [2] "unknown"
# [3] "exon"
# [4] "X11874"
# [5] "X12227"
# [6] "."
# [7] "X."
# [8] "..1"
# [9] "gene_id.DDX11L1..gene_name.DDX11L1..transcript_id.NR_046018..tss_id.TSS16932."

Convert transcript to symbol

Check out XXX.genes.results or XXX.isoforms.results file from RSEM output

$ head Sample1.genes.results
gene_id	transcript_id(s)	length	effective_length	expected_count	TPM	FPKM
A1BG	NM_130786	1766.00	1589.99	0.00	0.00	0.00
A1BG-AS1	NR_015380	2130.00	1953.99	0.00	0.00	0.00

$ head Sample1.isoforms.results
transcript_id	gene_id	length	effective_length	expected_count	TPM	FPKM	IsoPct
NM_130786	A1BG	1766	1589.99	0.00	0.00	0.00	0.00
NR_015380	A1BG-AS1	2130	1953.99	0.00	0.00	0.00	0.00

biotools (online)

Converting between UCSC id and gene symbol with bioconductor annotation resources. You need to use the Homo.sapiens package.

library(Homo.sapiens)
select(Homo.sapiens, "uc001qvk.1", "SYMBOL","TXNAME")
# 'select()' returned 1:1 mapping between keys and columns
#       TXNAME SYMBOL
# 1 uc001qvk.1    A2M

How to map UCSC transcripts to gene symbol? org.Hs.eg.db package.

Splice junction

GTF file contains splice junction information. See Genome > Splice junction on how to extract this information.

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 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

The format is explained in UCSC.

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

http://biobits.org/samtools_primer.html
Genome-SAMtools
http://samtools.github.io/hts-specs/SAMv1.pdf (especially p4)
http://genome.sph.umich.edu/wiki/SAM
https://class.coursera.org/gencommand-003/lecture/39 (video)
See VCF format
https://youtu.be/2KqBSbkfhRo?list=UUqaMSQd_h-2EDGsU6WDiX0Q (Bioconductor)

There are 11 required fields in the sam file and the header is optional. See also http://bio-bwa.sourceforge.net/bwa.shtml.

column field	example (/ExomeLungCancer/H1975cellLine)
1 QNAME	SRR2923335.1
2 FLAG	99
3 RNAME (chromosome name)	6
4 POS	36938157
5 MAPQ	60
6 CIGAR	100M
7 RNEXT	=
8 PNEXT	36938259
9 TLEN	202
10 SEQ	NGCAAGTAGAGGGGCGCACACCTACCCGCAGTTGTTCACAGCCATG…
11 QUAL	#1=DDFDFHHHHHJIJJJJIJJJJJJJJIJJJJJIJIHHHHHFFF…
12 Tags (optional)	NM:i:1 MD:Z:0G99 AS:i:99 XS:i:61 MQ:i:60

We can use samtools view to view the bam file (samtools view will not include the header, samtools view -H will include only the header, samtools view -h will include the header)

$ samtools view -h accepted_hits.bam | more
@HD	VN:1.4
@SQ	SN:1	LN:249250621
@SQ	SN:2	LN:243199373
@SQ	SN:3	LN:198022430
@SQ	SN:4	LN:191154276
@SQ	SN:5	LN:180915260
@SQ	SN:6	LN:171115067
@SQ	SN:7	LN:159138663
@SQ	SN:8	LN:146364022
@SQ	SN:9	LN:141213431
@SQ	SN:10	LN:135534747
@SQ	SN:11	LN:135006516
@SQ	SN:12	LN:133851895
@SQ	SN:13	LN:115169878
@SQ	SN:14	LN:107349540
@SQ	SN:15	LN:102531392
@SQ	SN:16	LN:90354753
@SQ	SN:17	LN:81195210
@SQ	SN:18	LN:78077248
@SQ	SN:19	LN:59128983
@SQ	SN:20	LN:63025520
@SQ	SN:21	LN:48129895
@SQ	SN:22	LN:51304566
@SQ	SN:X	LN:155270560
@SQ	SN:Y	LN:59373566
@SQ	SN:MT	LN:16569
@PG	ID:STAR	PN:STAR	VN:STAR_2.5.0c	CL:STAR   --runThreadN 11   --genomeDir STARindex   --readFilesIn test.SRR493369
_1.fastq   test.SRR493369_2.fastq      --outFileNamePrefix ./LFB_HOXA1KD_repA/   --outSAMtype BAM   Unsorted      --twop
assMode Basic
@CO	user command line: STAR --genomeDir STARindex --twopassMode Basic --runThreadN 11 --outSAMtype BAM Unsorted --ou
tFileNamePrefix ./LFB_HOXA1KD_repA/ --readFilesIn test.SRR493369_1.fastq test.SRR493369_2.fastq
SRR493369.1	99	19	3977426	255	1S100M	=	3977605	488	ACTGGATCTCCACAAGGTAGATGGGCTCCATGAGGCGTGG
CTGGGCGGTCAGCACACTGGCGTAGAGGCAGCGCCGTGCTGTGGGGATGATCTGGCCCCCT	CCCFFFFFHHHHHJJJJGIJJHIIJIJIJJIIJIJJJHJJJJJIIJDGDCDEDDDC
DDCDDDB@BDDDDDDDBBB@99@BD>CCDD<@BCCCD:3@#####	NH:i:1	HI:i:1	AS:i:199	nM:i:1
SRR493369.1	147	19	3977605	255	4M208N97M	=	3977426	-488	CGCCCTCCTTGGTGGCCCACTGGAAGCCGGCC
ACCACACTGTCCTTGATCTCGTTGAGGTACTGCACACCCTTGGTGATGTCGGTGAGGATGTTGGGGCCG	5BDDBBDDDDDDBABDDDCDDDDDDDCDDDDDCDCCCADEFFFFFHHH

To count the number of rows in the header, use the Linux comand grep (though samtools view provides the -H argument to keep only the header)

$ samtools view -h SeqTestdata/RNASeqFibroblast/LFB_HOXA1KD_repA/accepted_hits.bam | grep "^@" | wc -l
$ # same as samtools view -H SeqTestdata/RNASeqFibroblast/LFB_HOXA1KD_repA/accepted_hits.bam | wc -l
28
$ samtools view -h SeqTestdata/RNASeqFibroblast/LFB_HOXA1KD_repA/accepted_hits.bam | grep -v "^@" | wc -l
$ # same as samtools view SeqTestdata/RNASeqFibroblast/LFB_HOXA1KD_repA/accepted_hits.bam | wc -l
9856
$ samtools view -h SeqTestdata/RNASeqFibroblast/LFB_HOXA1KD_repA/accepted_hits.bam | wc -l
9884

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)

# Suppose we want to get the MAPQ value from reads
param<-ScanBamParam(what="mapq") # other choices, what = c("rname", "strand", "pos", "qwidth", "seq")
                                 # see scanBamwhat() 
bam<-scanBam(file="filename.bam", param=param)
str(bam)
summary(bam)
# List of 1
#  $ :List of 1
#   ..$ mapq: int [1:195875920] 0 13 0 0 2 0 0 0 9 0 ...
object.size(bam)/2^20
# 747.207664489746 bytes
summary(bam[[1]][[1]])
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's
#     0.0    60.0    60.0    58.4    60.0    60.0 2434291
table(bam[[1]][[1]])

        0         1         2         3         4         5         6         7 
  3186501     59642     40093     91541     52501     43220    123651     81155 
        8         9        10        11        12        13        14        15 
    23354    133441     19302     17924     61882     30326     17755     54093 
       16        17        18        19        20        21        22        23 
    24826     21290     52880     37874     15116     69845     25458     30183 
       24        25        26        27        28        29        30        31 
    57684     80430     13746    537584     18437     14125     41978     25748 
       32        33        34        35        36        37        38        39 
    11848     63802     13745     16987     33168     29840     12198     64799 
       40        41        42        43        44        45        46        47 
   763253     32690     61987     46020     64149     85350    129188    100040 
       48        49        50        51        52        53        54        55 
   193770    683051     64096     95453     58853     87942     58469     48584 
       56        57        58        59        60 
    50132    135261     70818    101295 185161256

Flag

https://broadinstitute.github.io/picard/explain-flags.html Interpret FLAG value (interactive)

For single end data, no flag is set so the flag is always 0.

Extract by read names with grep

# Extract reads SRR2923335.1 and SRR2923335.1999 and output only the read name (1st) and mapping quality columns (5th)
# -e = --extended-regexp, -w = matching the whole word
# awk print $1,$5 will output with a space character between columns
samtools view accepted_hits.bam  | grep -E -w "SRR2923335.1|SRR2923335.1999" | awk '{print $1,$5}'

Extract by a region

# https://www.biostars.org/p/48719/
# the output will be all reads starting anywhere between 18000-45500.
samtools view input.bam "Chr10:18000-45500" > output.bam

# https://www.biostars.org/p/45936/
# index first!!!
samtools view -h reads.bam 1:1042000-1042010 > subset.sam
samtools view -h -b reads.bam chr1:10420000-10421000 > subset.bam

chromosomes

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.

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

# NCBI GRCh38
brb@T3600 ~/SeqTestdata/RNASeqFibroblast $ samtools view RNAseq_Bam_ncbigrch38/LFB_scramble_repA.bam | head | awk '{print $3}'
chr1
chr1
chr1
chr1
chr1
chr1

# 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

Subset reads from specific chromosomes

https://www.biostars.org/p/128967/

For example to remove mitochondrial reads from BAM files

samtools view -b input.bam chr1 chr2 chr3 chr4 > output.bam
# Or if the bam file has been indexed (i.e. .bai file is available)
samtools idxstats input.bam | cut -f 1 | grep -v MT | xargs samtools view -b input.bam > output.bam

where samtools idxstat returns 4 columns (TAB-delimited with each line consisting of reference sequence name, sequence length, # mapped reads and # unmapped reads).

$ samtools idxstats input.bam | head
1	249250621	0	0
2	243199373	0	0
3	198022430	0	0
4	191154276	0	0
5	180915260	0	0
6	171115067	0	0
7	159138663	0	0
8	146364022	0	0
9	141213431	0	0
10	135534747	0	0

Count the number of reads with certain number of mismatches

https://www.biostars.org/p/130256/ Bwa appears to include only mismatches, but bowtie includes insertions and deletions in its NM.
https://gist.github.com/davfre/8596159

Other alignment methods

Online tools (global and local alignments)

https://www.ebi.ac.uk/Tools/psa/

STAR

Current Manual (pdf is embedded in github)
Version 2.4 Manual (pdf is searchable)

Kallisto

https://pachterlab.github.io/kallisto/about
Used in ARCHS4

Subread

See Subread.

Hisat

Command line usages

Aligner	Command Line
bowtie2	bowtie2 -x /path/to/bowtie2/BASENAME -1 A_1.fq,B_1.fq -2 A_2.fq,B_2.fq -S alignment.sam
tophat2	tophat2 --no-coverage-search -p $SLURM_CPUS_PER_TASK \ -o OutputDir \ /path/to/bowtie2/BASENAME \ R1.FASTQ.gz R2.FASTQ.gz
STAR	mkdir STARindex STAR --runMode genomeGenerate \ --runThreadsN $SLURM_CPUS_PER_TASK \ --genomeDir STARindex \ --genomeFastaFiles /path/to/fasta.fa \ --sjdbGTFfile /path/to/genes.gtf \ --sdjbOverhang 85 STAR --twopassMode Basic \ --runThreadN $SLURM_CPUS_PER_TASK \ --genomeDir STARindex \ --outSAMmapqUnique 50 \ --sjdbOverhang 85 \ --readFilesIn r1.fastq.gz r2.fastq.gz \ --readFilesCommand zcat \ --outSAMtype BAM Unsorted \ --outFileNamePrefix output/star
Subread	subread-buildindex -o INDEX /path/to/fasta.fa subread-align -T $SLURM_CPUS_PER_TASK -i INDEX -r R1.FASTQ.gz -R R2.FASTQ.gz -o output/alignment.sam
Hisat2	hisat2-build -p $SLURM_CPUS_PER_TASK \ /path/to/fasta.fa INDEX hisat2 -p $SLURM_CPUS_PER_TASK -x INDEX \ -1 R1.FASTQ.gz \ -2 R2.FASTQ.gz -S output/alignment.sam

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.
Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods by Maza E 2016
A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data by Zhou 2017

Power Calculation

Phenotype Prediction

Improving the value of public RNA-seq expression data by phenotype prediction

Clustering

ICAclust – Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data

Correlation between RNA-Seq and microarrays

Correlation between RNA-Seq and microarrays results using TCGA data

Techniques for characterizing RNA transcripts

qRT-PCR -> Microarray -> Nanostring -> RNA-Seq

Integrating RNA sequencing into neuro-oncology practice

Differential expression analysis

Reproducible RNA-Seq analysis

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

GSE30567

Used in STAR paper

GSE38886

Used in STAR paper

GSE48215 & GSE48216

Modeling precision treatment of breast cancer

GSE48215 is Whole exome Seq and GSE48216 is RNA-Seq

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")
  system(c1)
}

samtools sort -n Sample1/accepted_hits.bam Sample1_sn  # 283 seconds
# samtools sort Sample1/accepted_hits.bam Sample1_s

# Step 5. Count reads using htseq-count
htseq-count -f bam -s no -a 10 Sample1_sn.bam Mus_musculus.GRCm38.70.gtf > Sample1.count # 759 seconds
htseq-count -f bam -s no -a 10 Sample2_sn.bam 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)

GSE37544

RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance

GSE11045

Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

GSE37703/SRP012607

Differential analysis of HOXA1 in adult cells at isoform resolution by RNA-Seq by Trapnell C, Hendrickson DG, Sauvageau M, Goff L et al. Nat Biotechnol 2013.

The <samples.txt> file required by BRB-SeqTools looks like

LibraryName	LibraryLayout	fastq1	fastq2	ReadGroup	SequencerManufacturer
LFB_scramble_repA	PAIRED	SRR493366_1.fastq	SRR493366_2.fastq	1	illumina
LFB_scramble_repB	PAIRED	SRR493367_1.fastq	SRR493367_2.fastq	2	illumina
LFB_scramble_repC	PAIRED	SRR493368_1.fastq	SRR493368_2.fastq	3	illumina
LFB_HOXA1KD_repA	PAIRED	SRR493369_1.fastq	SRR493369_2.fastq	4	illumina
LFB_HOXA1KD_repB	PAIRED	SRR493370_1.fastq	SRR493370_2.fastq	5	illumina
LFB_HOXA1KD_repC	PAIRED	SRR493371_1.fastq	SRR493371_2.fastq	6	illumina

GSE37704

http://www.gettinggeneticsdone.com/2015/12/tutorial-rna-seq-differential.html

GSE64570, GSE69244, GSE72165

Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

Ion Torrent

GEO
GSE46876 from GPL17301 PGM (Homo sapiens) and GPL17303 Ion Torrent Proton (Homo sapiens). Unequal read length.
GSE83668 from GPL17303 Ion Torrent Proton (Homo sapiens) and GPL11154 Illumina HiSeq 2000 (Homo sapiens)
GSE79051/SRP071553 from GPL17303 Ion Torrent Proton (unequal read length)

SRA

Pipeline

Extracting allelic read counts from 250,000 human sequencing runs in Sequence Read Archive 2018. Code in Github and Data.

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.

Data Processing Pipelines

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/
TCGAretriever from CRAN
RTCGA package from Bioconductor.
- RTCGA.clinical - Clinical datasets from The Cancer Genome Atlas Project
- Extract Survival Information from Datasets Included in RTCGA.clinical and RTCGA.clinical.20160128 Packages
- https://rtcga.github.io/RTCGA/reference/expressionsTCGA.html

> if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
> BiocManager::install("RTCGA.clinical")
> library(RTCGA.clinical)

# Available cohorts
> checkTCGA('Dates')
 [1] "2011-10-26" "2011-11-15" "2011-11-28" "2011-12-06" "2011-12-30"
 [6] "2012-01-10" "2012-01-24" "2012-02-17" "2012-03-06" "2012-03-21"
[11] "2012-04-12" "2012-04-25" "2012-05-15" "2012-05-25" "2012-06-06"
[16] "2012-06-23" "2012-07-07" "2012-07-25" "2012-08-04" "2012-08-25"
[21] "2012-09-13" "2012-10-04" "2012-10-18" "2012-10-20" "2012-10-24"
[26] "2012-11-02" "2012-11-14" "2012-12-06" "2012-12-21" "2013-01-16"
[31] "2013-02-03" "2013-02-22" "2013-03-09" "2013-03-26" "2013-04-06"
[36] "2013-04-21" "2013-05-08" "2013-05-23" "2013-06-06" "2013-06-23"
[41] "2013-07-15" "2013-08-09" "2013-09-23" "2013-10-10" "2013-11-14"
[46] "2013-12-10" "2014-01-15" "2014-02-15" "2014-03-16" "2014-04-16"
[51] "2014-05-18" "2014-06-14" "2014-07-15" "2014-09-02" "2014-10-17"
[56] "2014-12-06" "2015-02-02" "2015-02-04" "2015-04-02" "2015-06-01"
[61] "2015-08-21" "2015-11-01" "2016-01-28"
# Available cohorts
> (cohorts <- infoTCGA() %>% rownames() %>% sub("-counts", "", x=.))

 [1] "ACC"      "BLCA"     "BRCA"     "CESC"     "CHOL"     "COAD"    
 [7] "COADREAD" "DLBC"     "ESCA"     "FPPP"     "GBM"      "GBMLGG"  
[13] "HNSC"     "KICH"     "KIPAN"    "KIRC"     "KIRP"     "LAML"    
[19] "LGG"      "LIHC"     "LUAD"     "LUSC"     "MESO"     "OV"      
[25] "PAAD"     "PCPG"     "PRAD"     "READ"     "SARC"     "SKCM"    
[31] "STAD"     "STES"     "TGCT"     "THCA"     "THYM"     "UCEC"    
[37] "UCS"      "UVM" 
> dim(BRCA.clinical)
[1] 1098 3703
> dim(OV.clinical) # one row per sample
[1]  591 3626
> dim(LUAD.clinical)
[1]  522 3046
> OV.survInfo <- survivalTCGA(OV.clinical, extract.cols = "admin.disease_code") 
> dim(OV.survInfo)  # 576 x 4
> plot(survfit(Surv(times, patient.vital_status) ~ 1, 
       data = OV.survInfo), conf.int=F, mark.time=TRUE)

> BiocManager::install("RTCGA.mRNA")
> data(package = "RTCGA.mRNA")
> data(OV.mRNA, package = "RTCGA.mRNA")
> dim(OV.mRNA)  # one row per sample
[1]   561 17815
> OV.mRNA[1:2, 1:5]
           bcr_patient_barcode       ELMO2  CREB3L1    RPS11   PNMA1
1 TCGA-01-0628-11A-01R-0363-07 -0.24800000 -0.14150 1.370625 1.08775
2 TCGA-01-0639-11A-01R-0363-07  0.09508333 -0.04650 0.982875 1.38725
> OV.clinical[1:2, 18]
[1] "tcga-04-1331" "tcga-04-1332"

> match(substr(OV.mRNA[, 1], 6, 12), substr(OV.clinical[, 18], 6, 12)) %>% 
  na.omit() %>% length()
[1] 546

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

Virtual Appliances
Galaxy Docker Image
Quick & dirty Galaxy installation in a virtual machine
Downloadable Galaxy Virtual Machine In Vmware. The virtual machine is not there anymore but the documentation is still there.
https://wiki.galaxyproject.org/CitingGalaxy Citation
https://wiki.galaxyproject.org/Learn Search rna-seq or NGS as keywords.
https://biostar.usegalaxy.org/
Galaxy & DESeq2
Galaxy Demo -- RNAseq Pipeline as Example from BioIT Core.

Cufflink -> Cuffcompare or Cuffmerge -> Cuffdiff

Forum

recount2

A multi-experiment resource of analysis-ready RNA-seq gene and exon count datasets https://jhubiostatistics.shinyapps.io/recount/

recount3

RNACocktail

A comprehensive framework for accurate and efficient RNA-Seq analysis

Massive Mining of Publicly Available RNA-seq Data from Human and Mouse

ARCHS4: All RNA and ChIP-Seq Sample and Signature Search
ARCHS4 tutorial (youtube). The chrome extension did not work on GSE26880 (still no ARCHS4 icon on the GEO webpage after restart Chrome. Click the series matrix file link, it brings to a page to download 3 GPL series matrix. But the series matrix files still don't contain the expression)

Misc

Markets

Global And United States NGS-based RNA-seq Market 2017

Tutorial/Primer

Whole Transcriptome Profiling: An RNA-Seq Primer and Implications for Pharmacogenomics Research

Anders2013: Difference between revisions

Latest revision as of 15:31, 8 October 2024

Required tools

Installation

Change PATH variable

HTSeq and pysam

Data directory

Subdirectory

Pipeline

Download example data (22 files in SRA format)

Convert SRA to FASTQ (sra -> fastq)

FASTQ PAIR

Access quality control through ShortRead or FastQC

Read quality issues

Encoding

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

transcriptome_data

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)

Other visualization tools

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

Output from console

-r pos option and 'Maximum alignment buffer size exceeded error'

Performance

Error: 'pair_alignments' needs a sequence of paired-end alignments

summarizeOverlaps() from GenomicAlignments package

ReadCounter - htseq alternative

featureCount from Subread

DESeq2 normalization

Input

collapseReplicates

Normalized counts = raw counts/sizeFactor

High-Throughput Count Data

estimateSizeFactorsForMatrix()

estimateDispersions()

DESeq() and gene filtering

Combine two DESeq2 objects (dds) for analysis

Designs and contrasts

results(contrast)

results(alpha, independentFiltering)

Negative binomial regression and returned from results()

Fold-change with continuous variables

Normalization-based vs log-ratio transformation-based methods

Error with DESeq2

Source code

DESeq normalization 2010 ("median ratio")

edgeR normalization

DESeq2 for DE analysis

DESeqDataSet methods

Pre-filter

betaPrior is FALSE by default

lfcShrink

Getting NAs in p-values

Comparison

Wald vs Likelihood ratio test

DESeq2, edgeR, limma

DESeq2 paired multifactor test

t-test vs DESeq2

Time series

Interaction

File Format

fastq format