# Mapping reads to the transcriptome with TopHat¶

Now that we have some quality-controlled reads and a new reference transcriptome, we’re going to map the reads to the reference genome, using the new reference transcriptome. We’ll again be using the TopHat software

module load TopHat2/2.0.12


And now run TopHat:

cd ~/rnaseq
tophat -p 4 \
-G cuffmerge_all/nostrand.gtf \
--transcriptome-index=\$HOME/RNAseq-semimodel/tophat/merged \
-o tophat_female_repl1 \
~/RNAseq-semimodel/reference/Gallus_gallus/UCSC/galGal3/Sequence/Bowtie2Index/genome \
female_repl1_R1.qc.fq.gz female_repl1_R2.qc.fq.gz


This will take about 15 minutes.

Questions:

• How do we pick the transcriptome/genome?

## Viewing the mapped reads percentage¶

Let’s look at these numbers specifically:

less tophat_female_repl1/align_summary.txt


## Making gene counts¶

Now that we know which reads go with which gene, we’ll use htseq-count.

First, load the PySAM and HTSeq software packages:

module load HTSeq/0.6.1


And next, run HTSeq:

htseq-count --format=bam --stranded=no --order=pos \
tophat_female_repl1/accepted_hits.bam \
cuffmerge_all/nostrand.gtf > female_repl1_counts.txt


When this is done, type:

less female_repl1_counts.txt


(again, use ‘q’ to exit). These are your gene counts.

Note, these are raw gene counts - the number of reads that map to each feature (gene, in this case). They are not normalized by length of gene. According to this post on seqanswers, both DEseq and edgeR want exactly this kind of information!

Questions:

• what are the ‘TCONS...’ names?
• what do these parameters mean?
• what parameters does HTSeq take?
• why are we using so many programs?