Ribo-seq Trimming Issue: Massive Read Loss in Paired-end Mode

Question

[Samreen A]

Hi everyone,

I’m analyzing a Ribo-seq breast tissue dataset (GSE210793) and noticed a major drop in read count when trimming as paired-end compared to single-end.

Here are my results:

for sample SRR24150276

Before trimming (R1 raw):

Total Sequences 52,295,695
Total Bases 7.8 Gbp
Sequence length: 150
%GC 66

After trimming (paired-end mode):

Total Sequences 1,524
Total Bases 40.8 kbp
Sequence length 18–32
%GC 53

After trimming (R1 only, single-end mode):

Total Sequences 50,887,136
Total Bases 1.3 Gbp
Sequence length 18–32
%GC 52

When I trim the data as paired-end, almost all reads are discarded. But if I run trimming on R1 only (single-end) using the same Cutadapt parameters, I retain most reads and the FastQC results look normal.

I’ve read that in Ribo-seq, Read 2 (R2) often doesn’t carry meaningful information since Ribo-seq fragments are short (~30 nt). The following paper also suggests focusing on R1 only: https://pmc.ncbi.nlm.nih.gov/articles/PMC6066590/

Could anyone confirm if it’s okay to proceed with only R1 reads for Ribo-seq downstream analysis (alignment, P-site estimation, etc.)? Or is there a recommended way to handle this kind of paired-end data?

Thanks in advance!

Answer 1

[Kevin Blighe]

Yes, it's standard and recommended to analyze Ribo-seq data using only R1 reads in single-end mode. The short footprint lengths (~28-30 nt) make R2 redundant or uninformative, as it often overlaps with R1 or captures adapters/low-quality sequence. Your trimming results and FastQC look good—proceed with R1 for alignment, P-site calling, etc. If needed, tools like ribotools or plastid handle this seamlessly.

Kevin