Mapping and Counting¶

RNA-seq generate gene expression information by quantifying the number of transcripts (per gene) in a sample. This is accomplished by counting the number of transcripts that have been sequenced - the more active a gene is, the more transcripts will be in a sample, and the more reads will be generated from that transcript.

For RNA-seq, we need to align or map each read back to the genome, to see which gene produced it. - Highly expressed genes will generate lots of transcripts, so there will be lots of reads that map back to the position of that transcript in the genome. - The per-gene data we work with in an RNA-seq experiment are counts: the number of reads from each sample that originated from that gene.

Preparation of the genome¶

To be able to map (align) sequencing reads on the genome, the genome needs to be indexed first. In this workshop we will use HISAT2. Note for speed reason, the reads will be aligned on the chr5 of the Yeast genome.

cd ~RNA_seq/Genome

#to list what is in your directory:
ls

    Saccharomyces_cerevisiae.R64-1-1.99.gtf  Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa

# index file:
hisat2-build -p 4 -f Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa Saccharomyces_cerevisiae.R64-1-1.dna.toplevel

ls 

Saccharomyces_cerevisiae.R64-1-1.99.gtf              Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.4.ht2  Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.8.ht2
Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.1.ht2  Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.5.ht2  Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa
Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.2.ht2  Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.6.ht2
Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.3.ht2  Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.7.ht2

How many files were created during the indexing process?

Alignment on the genome¶

Now that the genome is prepared. Sequencing reads can be aligned.

Information required:

• Where the sequence information is stored (e.g. fastq files ...) ?
• What kind of sequencing: Single End or Paired end ?
• Where are stored the indexes and the genome?

• Where will the mapping files be stored?

• Now, lets move one folder up (into the RNA_seq folder):

cd ..
 ls

    Genome  Intro   MultiQC QC  Raw  Trimmed

Let's map one of our sample to the genome

hisat2 -x Genome/Saccharomyces_cerevisiae.R64-1-1.dna.toplevel -U Raw/SRR014335-chr1.fastq -S SRR014335.sam

!!! quote ""

    * **-x** The basename of the index for the reference genome. 
    * **-U** Comma-separated list of files containing unpaired reads to be aligned
    * **-S** File to write SAM alignments to. By default, alignments are written to the “standard out” or “stdout” filehandle  


Now we need to align all the rest of the samples.

```bash
pwd

    /home/shared/TRAINING1/RNA_seq/

mkdir Mapping
ls

    Genome  Mapping  QC  Raw  SRR014335.sam  Trimmed

let's use a for loop to process our samples:

cd Trimmed
ls

    SRR014335-chr1.fastq  SRR014336-chr1.fastq  SRR014337-chr1.fastq  SRR014339-chr1.fastq  SRR014340-chr1.fastq  SRR014341-chr1.fastq

for filename in *
do
base=$(basename ${filename} .trimmed.fastq)
hisat2 -p 4 -x ../Genome/Saccharomyces_cerevisiae.R64-1-1.dna.toplevel -U $filename -S ../Mapping/${base}.sam --summary-file ../Mapping/${base}_summary.txt
done

Now we can explore our SAM files.

cd ../Mapping
ls

SRR014335-chr1.sam          SRR014336-chr1_summary.txt  SRR014339-chr1.sam          SRR014340-chr1_summary.txt
SRR014335-chr1_summary.txt  SRR014337-chr1.sam          SRR014339-chr1_summary.txt  SRR014341-chr1.sam
SRR014336-chr1.sam          SRR014337-chr1_summary.txt  SRR014340-chr1.sam          SRR014341-chr1_summary.txt

Converting SAM files to BAM files¶

The SAM file, is a tab-delimited text file that contains information for each individual read and its alignment to the genome. While we do not have time to go into detail about the features of the SAM format, the paper by Heng Li et al. provides a lot more detail on the specification.

The compressed binary version of SAM is called a BAM file. We use this version to reduce size and to allow for indexing, which enables efficient random access of the data contained within the file.

A quick look into the sam file¶

less SRR014335-chr1.sam 

We will convert the SAM file to BAM format using the samtools program with the view command

for filename in *.sam
do
base=$(basename ${filename} .sam)
samtools view -S -b ${filename} -o ${base}.bam
done

# Note, when you run this you may get a notification output saying "fail to read the header from SRR014335-chr1.log.sam". Is this an issue? What is happening here? This is a good opportunity to think about what your code is doing and what files you have.

ls

SRR014335-chr1.bam  SRR014336-chr1.bam  SRR014337-chr1.bam  SRR014339-chr1.bam  SRR014340-chr1.bam  SRR014341-chr1.bam
SRR014335-chr1.sam  SRR014336-chr1.sam  SRR014337-chr1.sam  SRR014339-chr1.sam  SRR014340-chr1.sam  SRR014341-chr1.sam

Next we sort the BAM file using the sort command from samtools. -o tells the command where to write the output.

SAM/BAM files can be sorted in multiple ways, e.g. by location of alignment on the chromosome, by read name, etc. It is important to be aware that different alignment tools will output differently sorted SAM/BAM, and different downstream tools require differently sorted alignment files as input.

for filename in *.bam
do
base=$(basename ${filename} .bam)
samtools sort -o ${base}_sorted.bam ${filename}
done

You can use samtools to learn more about the bam file as well.

Some stats on your mapping:¶

samtools flagstat SRR014335-chr1_sorted.bam 

161257 + 0 in total (QC-passed reads + QC-failed reads)
125090 + 0 primary
36167 + 0 secondary
0 + 0 supplementary
0 + 0 duplicates
0 + 0 primary duplicates
146368 + 0 mapped (90.77% : N/A)
110201 + 0 primary mapped (88.10% : N/A)
0 + 0 paired in sequencing
0 + 0 read1
0 + 0 read2
0 + 0 properly paired (N/A : N/A)
0 + 0 with itself and mate mapped
0 + 0 singletons (N/A : N/A)
0 + 0 with mate mapped to a different chr
0 + 0 with mate mapped to a different chr (mapQ>=5)

MultiQC: HISAT2 output¶

• The HISAT2 output data can also be incorporated into the MultiQC report the next time it is run.

cd ~/RNA_seq/MultiQC
cp ../Mapping/*summary* ./
multiqc .

Please note: running HISAT2 with either option --summary-file or older versions (< v2.1.0) gives log output identical to Bowtie2. These logs are indistinguishable and summary statistics will appear in MultiQC reports labelled as Bowtie2.

Read Summarization¶

Sequencing reads often need to be assigned to genomic features of interest after they are mapped to the reference genome. This process is often called read summarization or read quantification. Read summarization is required by a number of downstream analyses such as gene expression analysis and histone modification analysis. The output of read summarization is a count table, in which the number of reads assigned to each feature in each library is recorded.

Counting¶

• We need to do some counting!
• Want to generate count data for each gene (actually each exon) - how many reads mapped to each exon in the genome, from each of our samples?
• Once we have that information, we can start thinking about how to determine which genes were differentially expressed in our study.

Subread and FeatureCounts¶

• The featureCounts tool from the Subread package can be used to count how many reads aligned to each genome feature (exon).
• Need to specify the annotation information (.gtf file) You can process all the samples at once:

cd ~/RNA_seq

mkdir Counts
cd Counts
featureCounts -a ../Genome/Saccharomyces_cerevisiae.R64-1-1.99.gtf -o ./yeast_counts.txt -T 2 -t exon -g gene_id ../Mapping/*sorted.bam

MultiQC: featureCounts output¶

• If the samples are processed individually, the output data can be incorporated into the MultiQC report the next time it is run.

cd ../MultiQC
cp ../Counts/* .
multiqc .

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