For the CDS only analysis, HTSeq-count (intersection strict) was again used; however, we excluded reads that mapped to coordinates within the 5′ and 3′ UTRs for summarization.
Studies in other fields, such as metagenomics, dealing with high similarity DNA sequences may also find benefits from strict alignment and intersection with uniquely mappable locations.
For all analyses (save the case where we analyzed CDS only; see Additional file 1: Table S2), we summarized these reads to all exons of genes based on the coordinates on the hg19/GRCh37 gene annotations provided from Ensembl using the python script HTSeq-count (intersection strict).
Therefore, it might be that the strict use of the intersection defines better clusters that the use of the median, which is a mere approximation to the intersection point.
Reads aligning to exons were counted by HTSeq [ 43], using the "intersection-strict" mode.
Read counts per sample were obtained using HTseq version 0.5.4p1 using "intersection-strict" mode, whereby the whole read must map to a single transcript to be counted.
Because of the "intersection-strict" mode applied to HTSeq, some of the reads that map ambiguously to a gene were removed and we were left with 155,799,857 reads that mapped to 20,388 genes.
Total read counts per transcript were estimated in htseq-count (http://www-huber.embl.de/users/anders/HTSeq/doc/count.html, last accessed May 23 , 2014 applying the intersection-strict mode.
Reads excluded from tabulation included those ambiguously associated with more than one feature (i.e., −m intersection-strict) as well as any reads with quality score lower than 10 (−a 10) [ 35].
Sequences aligned to the human genome were assigned to features and counted by HTSeq version 0.4.6 with parameters: -m intersection-strict -s no -t exon (Additional file 7: Table S3).
