Forum:Is trimming always better?

Question

[Mozart]

Hello there,

It may sound naive but sometimes people I know tend to avoid trimming step (i.e. using Trimmomatic) when

1. the software which convert raw BCL data manages to remove adapters and
2. alignment rate is more than 50%

What do you think?

Thanks

[Mozart]

sorry for resurrecting this old post but I would like to clarify a couple of things here.

I am trying to avoid trimming step also because when I look at the untrimmed fastq files there is no adapter contamination and Sequence Quality Histograms returned green check for all the fastq files.

percentage of pseudo alignment is between 82-85%. What do you recommend?

thanks

[GenoMax]

If you think results look reasonable you don't have to trim as @Devon already indicated (as long as you are not working with smallRNA, which you are not).

[Mozart]

Thanks a lot for clarifying this @genomax. I will keep this in mind for future experiments.

Answer 1

[Devon Ryan]

I rarely trim RNA-seq data, since the aligners will just soft-clip adapters anyway.

Note that (1) will just trim adapters, so if you have quality issues that are actually affecting alignment then you'd still need to trim. With (2), 50% alignment is VERY low. I start to wonder if alignment is <90% (at least with common model organisms).

[Mozart]

I see, thanks for clarifying this point.

Answer 2

[swbarnes2]

Another anecdote...my lab does Lexogen RNA-Seq preps, I align with STAR, then I use RSEM to count reads assigned to genes.

What I observed is that Lexogen preps, which seem to involve a poly-T, sometimes generate reads with poly-A ends at the ends. This is fine for STAR, it doesn't mind soft-clipping, but the transcriptome alignment file it prepares that I use as input into RSEM can't handle soft-clipping, so those reads ended up dropped from the transcriptome alignment file, so RSEM of course could not count them. So I always trim poly-A's from the end of RNASeq read, to help them end up in the RSEM-friendly transcription file.

Answer 3

[GenoMax]

If you are working with smallRNA data then you will almost certainly have to trim your data. There may be kit specific adapters/procedures to deal with. A small number of bases left over (even if you use bcl2fastqv2 to trim data). bbduk.sh from BBMap suite will remove those by doing a trim by overlap.

When alignment falls below a reasonable it is time to grab a few non-aligning reads and head to blast.