<p>You don't need to do anything, this is taken care of by the linear model - if you don't have any confounding effects.</p>
<p>If you do have confounding effects, e.g. all control samples on HiSeq and all treated on NovaSeq there's not a lot you can do anyway, other than be honest when you present your results.</p>
<p>Taken care of by the model means adding an extra 2 level factor for sequencer by sample, you need to do this anyway to take care of the batch effect between sequencer runs.</p>

CC BY-SA 4.0
2021-04-22T09:20:07.030Z
2021-04-22T09:20:07.030Z
Greg
<p>I have RNA samples but with different read lengths (Eg, HiSeq 2x125 and NovaSeq 2x150bp data). I would like to do DE analysis on these samples. What do you recommend? Do you recommend to trim the reads to 125bp for the NovaSeq to compare with the other batches from HiSeq? Or any other suggestions.</p>

CC BY-SA 4.0
2021-04-24T13:30:42.797Z
Does this answer your question? [Normalize RNA seq data from multiple runs for expression analysis](https://bioinformatics.stackexchange.com/questions/15765/normalize-rna-seq-data-from-multiple-runs-for-expression-analysis)
Fabio Marroni
<rna-seq>
RNAseq DE comparison with samples of different read length
# RNAseq DE comparison with samples of different read length

(Asked by: username_0 on 2021-04-22)

I have RNA samples but with different read lengths (Eg, HiSeq 2x125 and NovaSeq 2x150bp data). I would like to do DE analysis on these samples. What do you recommend? Do you recommend to trim the reads to 125bp for the NovaSeq to compare with the other batches from HiSeq? Or any other suggestions.

**Tags:** rna-seq
> **Comments:**
> **username_1**: Does this answer your question? [Normalize RNA seq data from multiple runs for expression analysis](https://bioinformatics.stackexchange.com/questions/15765/normalize-rna-seq-data-from-multiple-runs-for-expression-analysis)  

---

## 1 answers

### Answer by username_2 on 2021-04-22. Score: 0

You don't need to do anything, this is taken care of by the linear model - if you don't have any confounding effects.

If you do have confounding effects, e.g. all control samples on HiSeq and all treated on NovaSeq there's not a lot you can do anyway, other than be honest when you present your results.

Taken care of by the model means adding an extra 2 level factor for sequencer by sample, you need to do this anyway to take care of the batch effect between sequencer runs.


---

