Genome

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Visualization

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

IGV

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

Gviz

ChromHeatMap

Heat map plotting by genome coordinate.

ggbio

NOISeq package

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

rtracklayer

R interface to genome browsers and their annotation tracks

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

ssviz

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

Sushi

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

Copy Number

Copy number work flow using Bioconductor

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

Whole exome sequencing != whole genome sequencing

RNA seq

CentralDogmaMolecular.png

Introduction to Sequence Data Analysis

NIH only

Quality control

FastQC

Trim Galore!

QoRTs

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

Alignment

Bowtie

Extremely fast, general purpose short read aligner.

bowtie needs to have an index of the genome in order to perform its alignment functionality. For example, to build a bowtie index against UCSC hg19 

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

The reference genome index <genome.fa> can be generated by following Sean Davis' instruction. Note that genome sequence indexes (including Bowtie indexes) as well as GTF transcript annotation files for many commonly used reference genomes can be directly downloaded from http://tophat.cbcb.umd.edu/igenomes.shtml. Even the index file can be directly downloaded without going through Bowtie program, bowtie program is still needed by Tophat program where Tophat's job is to align the RNA-seq data to reference genome.

PS: these genome sequence indexes files are quite big; for example 21 GB for hg19.

BWA

Used by whole-exome sequencing. For example, http://bib.oxfordjournals.org/content/15/2/256.full and http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3865592/. Whole Exome Analysis.

Tophat

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

Linux part. 

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

Quick test of Tophat program 

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

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

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

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

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

STAR (Spliced Transcripts Alignment to a Reference)

Note that the readme file says HARDWARE/SOFTWARE REQUIREMENTS:

  • x86-64 compatible processors
  • 64 bit Linux or Mac OS X
  • 30GB of RAM for human genome

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

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

RNASequel

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

SAMtools

  • SAMtools: Primer / Tutorial by Ethan Cerami. It covers installing samtools, bcftools, generating simulated reads, align reads, convert sam to bam, sorting & indexing, identifying genomic variants, understand vcf format, visualize reads on a command line.

Cufflinks package

Transcriptome assembly and differential expression analysis for RNA-Seq.

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

Tuxedo protocol

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

Cufflinks - assemble reads into transcript

Installation 

# about 47MB on ver 2.2.1 for Linux binary version
wget http://cole-trapnell-lab.github.io/cufflinks/assets/downloads/cufflinks-2.2.1.Linux_x86_64.tar.gz
sudo tar xzvf ~/Downloads/cufflinks-2.2.1.Linux_x86_64.tar.gz  -C /opt/RNA-Seq/bin/
export PATH=$PATH:/opt/RNA-Seq/bin/cufflinks-2.2.1.Linux_x86_64/

# test
cufflinks -h

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

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

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

It can be used to calculate FPKM.

Cuffcompare - compares transcript assemblies to annotation

Cuffmerge - merges two or more transcript assemblies

Cuffdiff

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

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

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

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

CummeRbund

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

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

Finding differentially expressed genes

Big pictures

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

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

Readings

Youtube videos

Download the raw fastq data GSE19602 from GEO and uncompress fastq.bz2 to fastq (~700MB) file. NOTE: the data downloaded from ncbi is actually sra file format. We can use fastq_dump program in SRA_toolkit to convert sra to fastq. http://www.ncbi.nlm.nih.gov/Traces/sra/?view=software 

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

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

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

To run Galaxy locally, we do 

cd galaxy-dist
sh run.sh --reload

The command line will show Starting server in PID XXXX. serving on http://127.0.0.1:8080. We can use Ctrl + C to stop the Galaxy process.

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

  1. Upload one fastq data. Click 'refresh' icon on the history panel to ensure the data is uploaded. Don't use 'refresh' button on the browser; it will cause an error for the current process.
  2. FASTQ Groomer. Convert the data to Galaxy needs. NGS: QC and manipulation => Illumina FASTQ. FASTQ quality scores type: Sanger. (~10 minutes). This part uses CPU not memory.
  3. Open a new browser tab and go to ftp://ftp.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudomolecules/version_6.1/all.dir/. Right click the file all.cDNA and copy link location. In Galaxy click 'Upload File from your computer' paste URL to URL/Text entry.
  4. Scroll down Galaxy and select NGS:Mapping -> Map with BWA. PS. for some reason, BWA is not available. So I use Bowtie2 instead. The output of Bowtie2 is bam file.
    1. For reference genome, choose 'Use one from the history'. Galaxy automatically find the reference file 'ftp://ftp.plantbiology....' from history.
    2. Library mate-paired => Single-end.
    3. FASTQ file => 2: FASTQ Groomer on data 1.
    4. BWA settings to use => Commonly Used.
    5. Execute (~ 15 minutes)
  5. We can view the alignment file (sam format) created from BWA by using UCSV or IGV (input is bam or bai format). We now use NGS: SAM Tools to convert sam file to bam file. Click 'SAM-to-BAM converts SAM format to BAM format' tool.
    1. Choose the source for the reference list => History
    2. Converts SAM file => 4: Map with BWA on data 2 and data 3.
    3. Using reference file => 3:ftp://ftp.plantbiology.....
    4. Execute (~5 minutes)
  6. We want to create bai file which is a shortcut to IGV. It breaks the data into smaller accessible chunks. So when you pull up a certain cDNA, it goes straight to the subset. Go to the history, click the pencil icon (Edit Attributes) on the file SAM-to-BAM on data 3 and data 4.
    1. Look at 'Convert to new format' section. Go ahead and click 'Convert'. (< 1 minute). This will create another file.
    2. Use browser and go to ftp website to download all.cDNA file to desktop. The desktop should contain 3 files - all.cDNA, rice.bam and rice.bai files for IGV.
  7. Goto http://www.broadinstitute.org/software/igv/download to download IGV which is a java-based application. I need to install java machine first by install openjdk-7-jdk. IGV by default will launch 'Human hg18' genome. Launch IGV by cd IGV_2.2.13;java -Xmx750m -jar igv.jar. I found the IGV input requires sam+bai OR bam+bai. So we need to click the pencil icon to create bai file first before we want to upload sam or bam file to IGV.
    1. Goto File => Import Genome. Call it 'rice' and select 'all.cDNA' sequence file. Click 'Save' button.
    2. Goto File => Upload from File => rice.bam.
    3. Top right panel is cDNA
    4. Middle right panel has a lot of 'boxes' which is a read. If we zoom in, we can see some read points to left (backward) while some points to right (forward). On the top is a histogram. For example, a base may be covered by a lot of reads then the histogram will show the high frequence.
    5. If we keep zoom in, we can see color at the Bottom right panel. Keeping zoom in, we can see the base G, C, T, A themselves.
    6. Using IGV, we can 1. examine coverage.
    7. We can 2. check 'alternative splicing'. (not for this cDNA)
    8. We can 3. examine SNPs for base change. If we see gray color (dark gray is hight quality read, light gray means low quality read), it means they are perfect match. If we see color, it means there is a change. For example, a read is 'C' but in fact it should be 'A'. If a case has many high quality reads, and half of them are 'G' but the reference genome shows 'A'. This is most likely a SNP. This is heterogeisity.
  8. Tophat - align RNA seq data to genomic DNA
    1. Suppose we have use Galaxy to upload 2 data. One is SRR034580 and we have run FASTQ Groomer on data 1. The second data is SRR034584 and we also have run FASTQ Groomer on data 2. We also have uploaded reference genome sequence.
    2. Goto Galaxy and find NGS: RNA Analysis => Tophat.
    3. reference genome => Use one from the history
    4. RNA-Seq FASTQ file => 2; FASTQ Groomer on data 1.
    5. Execute. This will create 2 files. One is splice junctions and the other is accepted_hits. We queue the job and run another Tophat with the 2nd 'groomer'ed data file. We are going to work on accepted_hits file.
    6. While the queue are running, we can click on 'pencil' icon on 'accepted_hits' job and run the utlity 'Convert to new format' (Bam to Bai). We should do this for both 'accepted_hits' files.
    7. For some reason, the execution failed: An error occurred with this dataset: TopHat v2.0.7 TopHat v2.0.7 Error indexing reference sequence /bin/sh: 1: bowtie-build: not found.
  9. Cufflinks. We will estimate transcript abundance by using FPKM (RPKM).
    1. SAM or BAM file of alignmed RNA-Seq reads => tophat on data 2.. accepted_hits
    2. Use Reference Annotation - No (choose Yes if we want annotation. This requires GTF format. See http://genome.ucsc.edu/FAQ/FAQformat.html#format4. We don't have it for rice.)
    3. Execute. This will create 3 files. Gene expression, transcript expression and assembled transcripts.
    4. We also run Cufflinks for 2nd accepted_hits file. (~ 25 minutes)
  10. Cuffcompare. Compare one to each other.
    1. GTF file produced by Cufflinks => assembled transcript from the 1st data
    2. Use another GTF file produced by Cufflinks => Yes. It automatically find the other one.
    3. Execute. (< 10 minutes). This will create 7 files. Transcript accuracy, tmap file & refmap flie from each assembled transcripts, combined transcripts and transcript tracking.
    4. We are interested in combined transcripts file (to use in Cuffdiff).
  11. Cuffdiff.
    1. Transcripts => combined transcripts.
    2. SAM or BAM file of aligned RNA-Seq reads => 1st accepted_hits
    3. SAM or BAM file or aligned RNA-Seq reads => 2nd accepted_hits
    4. Execute. This will generate 11 files. Isoform expression, gene expression, TSS groups expression, CDS Expression FPKM Tracking, isoform FPKM tracking, gene FPKM tracking, TSS groups FPKM tracking, CDS FPKM tracking, splicing diff, promoters diff, CDS diff. We are interested in 'gene expression' file. We can save it and open it in Excel.
  12. IGV - 2 RNA-Seq datasets aligned to genomic DNA using Tophat
    1. Load the reference genome rice (see above)
    2. Upload from file => rice4.bam. Upload from file => rice5.bam.
    3. Alternative RNA splicing.

edX course

PH525.5x Case Study: RNA-seq data analysis. The course notes are forming a book. Check out https://github.com/genomicsclass/labs and http://genomicsclass.github.io/book/.

Variant calling

Overview/papers

Workflow

  • Sequence reads -> Quality control -> Mapping -> Variant calling -> Filtering & Annotation -> Querying.

Software

Variant detector/discovery, genotyping

vcf format

grep -c -v "^#" XXX.vcf
  • Count number of indels
grep -c "INDEL" XXX.vcf

samtools mpileup

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

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

GATK

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

bcftools

bcftools — utilities for variant calling and manipulating VCFs and their binary counterparts BCFs.

Installation 

wget https://github.com/samtools/bcftools/releases/download/1.2/bcftools-1.2.tar.bz2
sudo tar jxf bcftools-1.2.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/bcftools-1.2/
sudo make # create bcftools, plot-vcfstats, vcfutils.pl commands

Example 

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

htslib

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

Installation 

wget https://github.com/samtools/htslib/releases/download/1.2.1/htslib-1.2.1.tar.bz2
sudo tar jxf htslib-1.2.1.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/htslib-1.2.1/
sudo make  # create tabix, htsfile, bgzip commands

Example 

export PATH=/opt/RNA-Seq/bin/bcftools-1.2/:$PATH
export PATH=/opt/RNA-Seq/bin/htslib-1.2.1/:$PATH
bgzip -c var.raw.vcf > var.raw.vcf.gz
tabix var.raw.vcf.gz  # need to index all vcf files (very fast in this step)
bcftools annotate -c ID -a common_all_20150603.vcf.gz var.raw.vcf.gz > var_annot.vcf # 2 min

vcftools

wget http://sourceforge.net/projects/vcftools/files/vcftools_0.1.12b.tar.gz/download \
     -o vcftools_0.1.12b.tar.gz
tar -xzvf vcftools_0.1.12b.tar.gz
sudo mv vcftools_0.1.12b /opt/RNA-Seq/bin/
export PERL5LIB=/opt/RNA-Seq/bin/vcftools_0.1.12b/perl/
/opt/RNA-Seq/bin/vcftools_0.1.12b/
make
export PATH=$PATH:/opt/RNA-Seq/bin/vcftools_0.1.12b/bin
ls bin
# fill-aa       vcf-annotate   vcf-convert      vcf-phased-join   vcf-subset
# fill-an-ac    vcf-compare    vcf-fix-ploidy   vcf-query         vcftools
# fill-fs       vcf-concat     vcf-indel-stats  vcf-shuffle-cols  vcf-to-tab
# fill-ref-md5  vcf-consensus  vcf-isec         vcf-sort          vcf-tstv
# man1          vcf-contrast   vcf-merge        vcf-stats         vcf-validator

Some example 

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

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

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

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

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

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

Online course on Variant calling

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

Variant Annotation

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

dbSNP

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

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

Query dbSNP

An example: 

wget https://github.com/samtools/bcftools/releases/download/1.2/bcftools-1.2.tar.bz2
sudo tar jxf bcftools-1.2.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/bcftools-1.2/
sudo make

wget https://github.com/samtools/htslib/releases/download/1.2.1/htslib-1.2.1.tar.bz2
sudo tar jxf htslib-1.2.1.tar.bz2 -C /opt/RNA-Seq/bin/
cd /opt/RNA-Seq/bin/htslib-1.2.1/
sudo make  # create tabix, htsfile, bgzip commands

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

wget ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/VCF/common_all_20150603.vcf.gz.tbi
wget ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/VCF/common_all_20150603.vcf.gz
cd TNBC1
mv ~/Downloads/common_all_20150603.vcf.gz* .
bgzip -c var.raw.vcf > var.raw.vcf.gz
tabix var.raw.vcf.gz
bcftools annotate -c ID -a common_all_20150603.vcf.gz var.raw.vcf.gz > var_annot.vcf

Any found in dbSNP? 

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

SnpEff & SnpSift

SnpEff: Genetic variant annotation and effect prediction toolbox.

  1. Input: vcf & reference genome database (eg GRCh38.79).
  2. Output: vcf & <snpEff_summary.html> & < snpEff_genes.txt> files.
wget http://iweb.dl.sourceforge.net/project/snpeff/snpEff_latest_core.zip
sudo unzip snpEff_latest_core.zip -d /opt/RNA-Seq/bin
export PATH=/opt/RNA-Seq/bin/snpEff/:$PATH

# Next we want to download snpEff database.
# Instead of using the command line (very slow < 1MB/s),
#   java -jar /opt/RNA-Seq/bin/snpEff/snpEff.jar databases | grep GRCh
#   java -jar /opt/RNA-Seq/bin/snpEff/snpEff.jar download GRCh38.79
# we just go to the file using the the web browser
#   http://sourceforge.net/projects/snpeff/files/databases/v4_1/  # 525MB
mv ~/Downloads/snpEff_v4_1_GRCh38.79.zip .
unzip snpEff_v4_1GRCh38.79.zip
sudo java -Xmx4G -jar /opt/RNA-Seq/bin/snpEff/snpEff.jar \
                      -i vcf -o vcf GRCh38.79 var_annot.vcf > var_annot_snpEff.vcf

SnpSift: SnpSift helps filtering and manipulating genomic annotated files (VCF). Once you annotated your files using SnpEff, you can use SnpSift to help you filter large genomic datasets in order to find the most significant variants

COSMIC

De novo genome assembly

Single Cell RNA-Seq

The best RNA-Seq analysis interface

Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective

Monitor Software Version Change

Circos Plot

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

R and Bioconductor packages

  • Some basics of biomaRt (and GenomicRanges)
  • Annotating Ranges Represent common sequence data types (e.g., from BAM, gff, bed, and wig files) as genomic ranges for simple and advanced range-based queries.
library(VariantAnnotation)
library(AnnotationHub)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(TxDb.Mmusculus.UCSC.mm10.ensGene)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(BSgenome.Hsapiens.UCSC.hg19)

Some workflows

RNA-Seq workflow

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

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

Sequence analysis

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

Accessing Annotation Data

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

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

RNA-Seq Data Analysis using R/Bioconductor

limma

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

easyRNASeq

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

ShortRead

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

Rsamtools

The Rsamtools package provides an interface to BAM files.

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

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

IRanges

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

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

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

flank

The example is obtained from ?IRanges::flank. 

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

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

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

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

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

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

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

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

Both IRanges and GenomicRanges packages provide the flank function.

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

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

GenomicRanges

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

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

The package defines some classes

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

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

GenomicAlignments

Counting reads with summarizeOverlaps vignette

library(GenomicAlignments)
library(DESeq)
library(edgeR)

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

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

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

assays(olap)$counts
#       sm_treated1.bam sm_untreated1.bam
#  [1,]               0                 0
#  [2,]               0                 0
#  [3,]               0                 0
#  [4,]               0                 0
#  [5,]               5                 1
#  [6,]               5                 0
#  [7,]               2                 0
#  [8,]             376               104
#  [9,]               0                 0
# [10,]               0                 0
# [11,]               0                 0

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

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

A modified R code that works is 

###################################################
### code chunk number 11: gff (eval = FALSE)
###################################################
library(rtracklayer)
fl <- paste0("ftp://ftp.ensembl.org/pub/release-62/",
             "gtf/drosophila_melanogaster/",
             "Drosophila_melanogaster.BDGP5.25.62.gtf.gz")
gffFile <- file.path(tempdir(), basename(fl))
download.file(fl, gffFile)
gff0 <- import(gffFile, asRangedData=FALSE)

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

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

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

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

expdata = MIAME(
              name="pasilla knockdown",
              lab="Genetics and Developmental Biology, University of 
                  Connecticut Health Center",
              contact="Dr. Brenton Graveley",
              title="modENCODE Drosophila pasilla RNA Binding Protein RNAi 
                  knockdown RNA-Seq Studies",
              pubMedIds="20921232",
              url="http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE18508",
              abstract="RNA-seq of 3 biological replicates of from the Drosophila
                  melanogaster S2-DRSC cells that have been RNAi depleted of mRNAs 
                  encoding pasilla, a mRNA binding protein and 4 biological replicates 
                  of the the untreated cell line.")

design <- data.frame(
              condition=c("untreated", "untreated"),
              replicate=c(1,1),
              type=rep("single-read", 2), stringsAsFactors=TRUE)
library(DESeq)
geneCDS <- newCountDataSet(
                  countData=assay(genehits),
                  conditions=design)

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

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

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

Rsubread

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

featureCounts vs HTSeq-count

Inference

DESeq or edgeR

  • DESeq2 method
  • DESeq2 with a large number of samples -> use DESEq2 to normalize the data and then use do a Wilcoxon rank-sum test on the normalized counts, for each gene separately, or, even better, use a permutation test. See this post. Or consider the limma-voom method instead, which will handle 1000 samples in a few seconds without the need for extra memory.
  • edgeR normalization factor post. Normalization factors are computed using the trimmed mean of M-values (TMM) method; see the paper by Robinson & Oshlack 2010 for more details. Briefly, M-values are defined as the library size-adjusted log-ratio of counts between two libraries. The most extreme 30% of M-values are trimmed away, and the mean of the remaining M-values is computed. This trimmed mean represents the log-normalization factor between the two libraries. The idea is to eliminate systematic differences in the counts between libraries, by assuming that most genes are not DE.
  • Can I feed TCGA normalized count data to EdgeR?
  • counts() function and normalized counts.

prebs

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

DEXSeq

Inference of differential exon usage in RNA-Seq

rSeqNP

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

pasilla and pasillaBamSubset Data

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

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

BitSeq

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

ReportingTools

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

sequences

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

QuasR

Bioinformatics paper

CRAN packages

rbamtools

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

refGenome

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

WhopGenome

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

Simulate RNA-Seq

http://en.wikipedia.org/wiki/List_of_RNA-Seq_bioinformatics_tools#RNA-Seq_simulators

SimSeq

Bioinformatics

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

empiricalFDR.DESeq2

http://biorxiv.org/content/early/2014/12/05/012211

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

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

polyester

http://biorxiv.org/content/early/2014/12/05/012211

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

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

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

RSEM

GEO

See the internal link at R-GEO.

Gene set analysis

Hypergeometric test

Misc

Merge different datasets (different genechips)

Normalization

How to use UCSC Table Browser

  • An instruction from BitSeq software

UCSC version & NCBI release corresponding

Gene Annotation

How many DNA strands are there in humans?

How many base pairs in human

  • chromosome 22 has the smallest number of bps (~50 million).
  • chromosome 1 has the largest number of bps (245 base pairs).

NGS technology

DNA methylation

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

i=normalIndex[1]
plot(density(meth[,i],from=0,to=1),main="",ylim=c(0,3),type="n")
for(i in normalIndex){
  lines(density(meth[,i],from=0,to=1),col=1)
}
### Add the cancer samples
for(i in cancerlIndex){
  lines(density(meth[,i],from=0,to=1),col=2)
}

# finding regions of the genome that are different between cancer and normal samples
library(limma)
X<-model.matrix(~pd$Status)
fit<-lmFit(meth,X)
eb <- ebayes(fit)

# plot of the region surrounding the top hit
library(GenomicRanges)
i <- which.min(eb$p.value[,2])
middle <- gr[i,]
Index<-gr%over%(middle+10000)
cols=ifelse(pd$Status=="normal",1,2)
chr=as.factor(seqnames(gr))
pos=start(gr)

plot(pos[Index],fit$coef[Index,2],type="b",xlab="genomic location",ylab="difference")
matplot(pos[Index],meth[Index,],col=cols,xlab="genomic location")
# http://www.ncbi.nlm.nih.gov/pubmed/22422453

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

library(bumphunter)
cl=clusterMaker(chr,pos,maxGap=500)
table(table(cl)) ##shows the number of regions with 1,2,3, ... points in them
＃consider two example regions＃
...

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

Sequence + Expression

RNASeq + ChipSeq

Labs

Terms

RNA sequencing 101

Web

Books

strand-specific vs non-strand specific experiment

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

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

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

FASTQ

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

FASTA <=> FASTQ conversion

Convert FASTA to FASTQ without quality scores

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

Convert FASTA to FASTQ with quality score file

See the links on the above post.

Convert FASTQ to FASTA using Seqtk

Use the Seqtk program; see this post.

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

RPKM (Mortazavi et al. 2008)

Reads per Kilobase of Exon per Million of Mapped reads.

Idea

  • The more we sequence, the more reads we expect from each gene. This is the most relevant correction of this method.
  • Longer transcript are expected to generate more reads. The latter is only relevant for comparisons among different genes which we rarely perform!

Calculation

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

Formula 

(tag count  * 1,000,000) / (total number of tags * kilobase of transcript)
= tag count / (total number of tags * transcript length) * 1.0e6

source("http://www.bioconductor.org/biocLite.R")
biocLite("edgeR")
library(edgeR)

set.seed(1234)
y <- matrix(rnbinom(20,size=1,mu=10),5,4)
     [,1] [,2] [,3] [,4]
[1,]    0    0    5    0
[2,]    6    2    7    3
[3,]    5   13    7    2
[4,]    3    3    9   11
[5,]    1    2    1   15

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

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

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

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

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

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

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

Critics

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

FPKM (Trapnell et al. 2010)

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

RPKM, FPKM and TPM

This is for the read normalization 

P -- per
K -- kilobase (related to gene length)
M -- million (related to sequencing depth)

TMM (Robinson and Oshlack, 2010)

Trimmed Means of M values (EdgeR).

Coverage

~20x coverage ----> reads per transcript = transcriptlength/readlength * 20

C = L N / G

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

SAM/Sequence Alignment Format and BAM format specification

Germline vs Somatic mutation

Germline: inherit from parents. See the Wikipedia page.

Driver vs passenger mutation

https://en.wikipedia.org/wiki/Somatic_evolution_in_cancer

Other software

Partek

dCHIP

MeV

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

IPA from Ingenuity

Login: There are web started version https://analysis.ingenuity.com/pa and Java applet version https://analysis.ingenuity.com/pa/login/choice.jsp. We can double click the file <IpaApplication.jnlp> in my machine's download folder.

Features:

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

Start Here 

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

Resource:

Notes:

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

Screenshots:

IngenuityAnalysisOutput.png

DAVID Bioinformatics Resource

qpcR

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

GSEA

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