Can Different Read Length Samples Be Used For Expression Analysis?

Question

[pinkiii1984v]

Hi,

I am working on the RNA-seq (Hiseq PE) data and my focus currently is expression analysis (gene and isoforms). Few of the samples have poor quality bases at the end of the reads and other samples are fine. My question is if I trim the poor quality bases (say like 10 bases), should I apply trimming on all the samples or is it alright to apply it only on the samples which have these poor quality bases. And can these samples with different read lengths be used for expression analysis?

Thank you

[Irsan]

Why don't you trim the reads from all your samples based on the corresponding quality scores. Reads that have good quality will not suffer from trimming. For example try trimmomatic, download binary here, source here

Answer 1

[Ashutosh Pandey]

It is alright to apply read trimming thing only for those samples whose reads have low quality bases in the end. Samples with different read lengths can be used for expression analysis as in the end you will be using RPKM normalised values that will normalize the read counts for genes by dividing the number of reads mapped by the length of the coding region and number of total reads mapped for that sample. This way even if trimming the low quality reads have narrowed down the number of reads mapped (it may happen that trimming made the read really small so that it can't be confidently aligned against the genome and discarded. Though it will rarely happen as you have long read Hiseq PE data) , the RPKM will take care of it.