Differential gene expression analysis

In this exercise, we will analyze RNA-seq data to measure changes in gene expression levels between wild-type and mutant strains of the bacterium Listeria monocytogenes.

Download data files

Copy the data files for this example into your $SCRATCH space:

cds
cp -r /corral-repl/utexas/BioITeam/ngs_course/listeria_RNA_seq/data listeria_RNA_seq

File Name	Description	Sample
`SRR034450.fastq`	Single-end Illumina 36-bp reads	wild-type, biological replicate 1
`SRR034451.fastq`	Single-end Illumina 36-bp reads	ΔsigB mutant, biological replicate 1
`SRR034452.fastq`	Single-end Illumina 36-bp reads	wild-type, biological replicate 2
`SRR034453.fastq`	Single-end Illumina 36-bp reads	ΔsigB mutant, biological replicate 2
`NC_017544.1.fasta`	Reference Genome sequence (FASTA)	Listeria monocytogenes strain 10403S
`NC_017544.1.gff`	Reference Genome features (GFF)	Listeria monocytogenes strain 10403S

This data was submitted in the Short Read Archive to accompany this paper:

Oliver, H.F., et al. (2009) Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs. BMC Genomics 10:641. Pubmed

You can view the data in the ENA SRA here: http://www.ebi.ac.uk/ena/data/view/SRP001753

Installing Bioconductor modules for R

Many of the modules for doing statistical tests on NGS data have been written in the "R" language for statistical computing. If you're not familiar with R, then this section is likely to be a bit confusing. (You might be thinking "Stop with the new languages already guys! Uncle!") To orient you, we are going to run the R command, which launches the R shell inside our terminal. Like the bash shell that we were using, the R shell interprets commands, but now they are R commands rather than bash commands. The prompt changes from login1$ to > when you are in the R shell, to help clue you in to this fact.

R is the favorite language of pirates. R is a very common scripting language used in statistics. There are whole classes on it going on in other SSI classrooms as we speak! Inside the R universe, you have access to an incredibly large number of useful statistical functions (Fisher's exact test, nonlinear least-squares fitting, ANOVA ...). R also has advanced functionality for producing plots and graphs as output. We'll take advantage of all of this here. You are well on your way to becoming denizens of the polyglot bioinformatics community now.

Regrettably, R is a bit of it's own bizarro world, as far as how its commands work. (Futhermore, Googling "R" to get help can be very frustrating.) The conventions of most other programming and scripting languages seem to have been re-invented by someone who wanted to do everything their own way in R. Just like we wrote shell scripts in bash, you can write R scripts that carry out complicated analyses.

Basic rules of R:

Don't forget: it's q() to quit.
For help, type ?command. Try ?read.table. The q key gets you out of help, just like for a man page.
The left arrow \<- (less-than-dash) is the same as an equals sign \=.

Like other languages, R can be expanded by loading modules. The R equivalent of Bioperl or Biopython is Bioconductor. Bioconductor can do things for you like convert sequences, but where it really shines is in doing statistical tests (where is it second-to-none in this list of languages). Many functions for analyzing microarray data are implemented in R, and this strength has now carried over to the analysis of RNAseq data.

Here's how you install two modules that we will need for this exercise:

login1$ module load R
login1$ R

R version 2.14.0 (2011-10-31)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-unknown-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> source("http://bioconductor.org/biocLite.R")
...
> biocLite("DESeq")
...
> biocLite("edgeR")
...
> q()
Save workspace image? [y/n/c]: n

When you start R later, you can load these modules with just these commands:

login1$ R
> library("DESeq")
> library("edgeR")

These commands will work for any Bioconductor modules!

Align the reads

For RNA-seq analysis we're mainly counting the reads that align well, so we choose to use bowtie. (You could also use BWA or many other mappers.)

We've done this several times before, so you should be able to come up with the full command lines if you refer back to the original lesson. Be careful we are now mapping single-end reads, so you may have to look at the bowtie help to figure out how to do that!

You will need to first build the index file, just once and in "interactive mode" is fine. Then, you will need to submit a commands file with four lines to the TACC queue.

Please give the final output files the names: SRR034450.sam, SRR034451.sam, SRR034452.sam, SRR034453.sam.

I just want a little hint

Remember, bowtie-build once then bowtie for each separate sample.

Just give me the answer...

module load bowtie
bowtie-build NC_017544.1.fasta NC_017544.1

Now create a commands file that looks like this:

bowtie --sam NC_017544.1 SRR034450.fastq SRR034450.sam
bowtie --sam NC_017544.1 SRR034451.fastq SRR034451.sam
bowtie --sam NC_017544.1 SRR034452.fastq SRR034452.sam
bowtie --sam NC_017544.1 SRR034453.fastq SRR034453.sam

Create the launcher script:

module load python
launcher_creator.py -n bowtie -e you@somewhere.com

Run it:

qsub launcher.sge

Convert alignments to BAM

Edit your commands file so that you convert all of these files from SAM to sorted and indexed BAM.

Linux expert tip: you can string together commands all on one line, so that they are sent to the same core one after another by separating them on the line with ;.

Note the use of the variable $FILE, which means that is the only part of the line that we have to change. This is a mini-use of shell scripting.

FILE=SRR034450 && samtools import NC_017544.1.fasta $FILE.sam $FILE.unsorted.bam && samtools sort $FILE.unsorted.bam $FILE && samtools index $FILE.bam
FILE=SRR034451 ; samtools import NC_017544.1.fasta $FILE.sam $FILE.unsorted.bam ; samtools sort $FILE.unsorted.bam $FILE ; samtools index $FILE.bam
FILE=SRR034452 ; samtools import NC_017544.1.fasta $FILE.sam $FILE.unsorted.bam ; samtools sort $FILE.unsorted.bam $FILE ; samtools index $FILE.bam
FILE=SRR034453 ; samtools import NC_017544.1.fasta $FILE.sam $FILE.unsorted.bam ; samtools sort $FILE.unsorted.bam $FILE ; samtools index $FILE.bam

Re-create the launcher script and submit this new job to the queue.:

module load samtools
launcher_creator.py -n samtools -e you@somewhere.com
qsub launcher.sge

Optional Exercise

Is this a strand-specific RNA-seq library? Try using IGV to view some of the BAM file data and examine the reads mapped to each gene.

Count reads mapping to genes using bedtools

bedtools is a great utility for working with sequence features and mapped reads in BAM, BED, VCF, and GFF formats.

We are going to use it to count the number of reads that map to each gene in the genome. Load the module and check out the help for bedtools and the multicov specific command that we are going to use:

module load bedtools
bedtools
bedtools multicov

The multicov command takes a feature file (GFF) and counts how many reads in. By default it counts how many reads overlap the feature on either strand, but it can be made specific with an option.

Our GFF file has a lot of redundant features that describe a gene multiple times, so we are going to trim it just to have "gene" features using grep.

grep '^NC_017544[[:space:]]*GenBank[[:space:]]*gene' NC_017544.1.gff > NC_017544.1.genes.gff

What is this doing? It's taking all the lines that begin with (^), then "NC_017544", then any number of spaces or tabs, then "GenBank", then any number of spaces or tabs, then "gene". Use head to see the before and after.

head -n 50 NC_017544.1.gff
head -n 50 NC_017544.1.genes.gff

In order to use the bedtools command on our data, submit this commands file to the TACC queue:

bedtools multicov -bams SRR034450.bam SRR034451.bam SRR034452.bam SRR034453.bam -bed NC_017544.1.genes.gff > gene_counts.gff

hg2. Analyze differential gene expression (DESeq)

[http://bioconductor.org/packages/devel/bioc/html/DESeq.html Manual and Instructions]

Using DESeq

login1$ R
...
> library("DESeq")
> combined = read.csv("combined.csv", header=T, row.names=1)
> design <- data.frame(
  row.names = colnames( combined ),
  condition = c( "Anc", "Anc", "EL", "EL", "EW", "EW"),
  libType = c( "single-end", "single-end", "single-end",
  "single-end", "single-end", "single-end" ) )
> design
> conds <- factor(design$condition)
> cds <- newCountDataSet( combined, conds )
> cds <- estimateSizeFactors( cds )
> sizeFactors( cds )
> res <- nbinomTest( cds, "EL", "EW" )
> write.csv(res, "EL-vs-EW.csv")

Additional Exercises

In an actual RNAseq analysis, you might want to trim stray adaptor sequences from your data using a tool like the FASTX-Toolkit or FAR before aligning.