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Why do most papers with bioinformatic results present genes as though there is only 1 isoform in the results, unless the paper specifically addresses alternate isoforms? (RNA-seq question)

Hello!

I was recently working on a ribosomal profiling project, essential you use RNA-seq but only on the RNA attached to ribosomes at a given time. So the first thing I did was align this data to mitochondrial DNA and ribosomal DNA to filter these 2 sets of data out. Then I went ahead and aligned this data that didnt map to either of those to the mm9 annotation, from there I used homer to quantify the repeats and get my data. When I quantified and aligned I was using the mm9-UCSC gene annotation GTF file. However, this gives data from a number of alternative splicing events, so if I have Gene A, I have actually 3 sets of data for the gene called Gene A. Now these 3 sets of data in my set are actually very similar and rarely vary in total count. The PROBLEM is when I look at bioinformatics papers that report this kind of data I see Gene A, and it is only listed 1 time with the graph they got from their results no mention of the isoforms or alternate spliced exons. Like what am I missing do they average these 3 genes or are they using some sort of GTF that doesn't include these or am I losing my mind.

Also I am curious about "scoring" and if this is related to my problem, I received data for about 55,000 genes (most of which had multiple splicing events) at varying levels, however I have heard that the data from some of these may not be reliable if they are not scored correctly. How do I fix this or am I just wrong here?

rna-seq assembly alignment next-gen software error

Please consider using a more concise title.

Most of the time read counting is done at the exon level but the counts are summarized at gene_id level. That is why you see only one count per gene.

How can I summarize the counts at the gene ID level?

Nevermind, I think I figured it out. They just report all three isoforms on one graph and call it a gene_set.

With featureCounts there is an option (-g gene_id) to summarize counts at gene level.

Thanks for your help friend!

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