RNA-Seq Data Analysis in R

RNA-Seq data analysis can be complicated. Softwares with graphical user interface like CLC Workbench, have made RNA-Seq data analysis quite easier.However, they are expensive and in most of the cases you might not be able to tweak your analysis in the exact way you want. Another important aspect of learning RNA-Seq analysis is understanding the algorithms behind the analysis.To this end, I decided to run a small simulation to understand how RNA-Seq analysis algorithms work.It is amazing how a single R package can do things like read aligning, read mapping and read counts in few lines of codes.

Install Rsubread package in R.


Load package

library (Rsubread)

For this simulation I created a small .fasta file by pulling some of the sequences from the Senecavirus A genome. I created a fasta file with a few contigs each containing about 70-100 basepairs , and named each contig as read 1, read 2 and so on. And I also pulled some sequences from the Zika virus which are names as Zika1 and Zika2. I will be aligning my reads to Senecavirus A genome. So, Zika virus reads should not be counted by Rsubread while aligning.

Sequences extracted:


This fasta file needs to be changed into fastq format. There are many tools available to convert fasta file to fastq format. I used reformat.sh script which is a part of bbmap. You can find details about bbmap and reformat.sh script elsewhere. The general syntax is as follow:

./reformat.sh in= meta.fasta out=meta.fastq qfake=35

#meta.fasta is my input file, meta.fastq is the output file and we are assigning quality score of 34 to all the basepairs.

Now, lets go back to R. We have loaded our package already. First, we need to build index of our reference files. Use the same Senecavirus A whole genome file which you used to extract reads in the above example. The file can be in fasta format. Then run follwing command.


In my case it would be

buildindex(basename= “seneca”, reference= “sva.fasta”)

Now, I can align reads in meta.fastq to the index file which I created above.


I saw that all the reads that were in meta.fastq belonging to Senecavirus A were aligned while , all the Zika virus reads were ignored.The output will be in .BAM format.

Now the tricky part. We need a annotated file in GTF or GFF format to count the features or genes aligned. For viruses, in most of the cases you don’t find well-annotated GTF or GFF files. Rsubread package allows you to create such files in tabular format which they call it SAF format.

So, lets use follwing code to create one SAF file for this analysis.

ann <- data.frame(
  GeneID        Chr Start  End Strand
1  gene1 KX778101.1   100  500      +
2  gene1 KX778101.1  1000 1800      +
3  gene2 KX778101.1  3000 4000      -
4  gene2 KX778101.1  5000 5500      -

Here you have to use Genebank accession number of virus genome as Chr. Beacuse .BAM file that we created by aligning to Senecavirus A genome have accession number liked to each reads. Other parameters can be changed.

Now, final step is to count reads.

fc_SE <- featureCounts("alignResults.BAM",annot.ext=ann)

You can see how many features were counted on the basis of information you provided in SAF table. You will see all the sequences that we extracted from the Senecavirus A genome have been been counted while there will not be any counts for Zika virus sequences.

Hope this will help you to understand how Rsubread package works. If you have any confusion about using Rsubread package, they have very good documentation on Bioconductor.

Image credit: Unsplash

Lok Raj Joshi
Lok Raj Joshi
Postdoctoral Research Fellow

My research interests include AAV gene therapy, vectored vaccine development, viral pathogenesis, diagnostic assay development,reverse genetics, viral bioinformatics.