Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Approaches for sRNA Analysis of RNA-seq Data: Comparison, Benchmarking

Vitalik Bezuglov
ORCID logo ,
Alexey Stupnikov
* ORCID logo ,
Ivan Skakov
,
Victoria Shtratnikova
ORCID logo ,
Jonathan Richard Pilsner
,
Alexander Suvorov
,
Oleg Sergeyev
* ORCID logo
Version 1 : Received: 7 December 2022 / Approved: 9 December 2022 / Online: 9 December 2022 (10:07:27 CET)
Version 2 : Received: 9 February 2023 / Approved: 10 February 2023 / Online: 10 February 2023 (11:26:06 CET)

A peer-reviewed article of this Preprint also exists.

Bezuglov, V.; Stupnikov, A.; Skakov, I.; Shtratnikova, V.; Pilsner, J.R.; Suvorov, A.; Sergeyev, O. Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking. Int. J. Mol. Sci. 2023, 24, 4195. Bezuglov, V.; Stupnikov, A.; Skakov, I.; Shtratnikova, V.; Pilsner, J.R.; Suvorov, A.; Sergeyev, O. Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking. Int. J. Mol. Sci. 2023, 24, 4195.

Abstract

Analysis of the expression activity of small noncoding RNA (sRNA), including microRNA, piwi-interacting RNA, small rRNA-derived RNA, and tRNA-derived small RNA, is a novel and quickly developing field. The nature of sRNA calls for bioinformatical approaches, adapted for the specific structure of the data. Despite a number of approaches proposed, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This article is dedicated to identifying the optimal pipeline configurations for each step of sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. For categorical factors and two groups of biosamples, we suggest approaches for the most crucial stages of sRNA analysis pipeline such as: (1) trimming with the lower length bound = 15 and the upper length bound = Readlength−40%Adapterlength; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; and (4) analyzing differential expression with DESeq2 with adjusted p-value < 0.05 or limma with p-value < 0.05 if there is very little signal and few transcripts.

Keywords

sRNA analysis; small RNA; microRNA; piRNA; tRNA-derived small RNA; RNA-seq; small RNA fragments; benchmarking; differential expression analysis

Subject

Biology and Life Sciences, Biochemistry and Molecular Biology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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