Cargando…

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

Expression analysis 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. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis o...

Descripción completa

Detalles Bibliográficos
Autores principales: Bezuglov, Vitalik, Stupnikov, Alexey, Skakov, Ivan, Shtratnikova, Victoria, Pilsner, J. Richard, Suvorov, Alexander, Sergeyev, Oleg
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959513/
https://www.ncbi.nlm.nih.gov/pubmed/36835604
http://dx.doi.org/10.3390/ijms24044195
_version_ 1784895295574769664
author Bezuglov, Vitalik
Stupnikov, Alexey
Skakov, Ivan
Shtratnikova, Victoria
Pilsner, J. Richard
Suvorov, Alexander
Sergeyev, Oleg
author_facet Bezuglov, Vitalik
Stupnikov, Alexey
Skakov, Ivan
Shtratnikova, Victoria
Pilsner, J. Richard
Suvorov, Alexander
Sergeyev, Oleg
author_sort Bezuglov, Vitalik
collection PubMed
description Expression analysis 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. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length − 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (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.
format Online
Article
Text
id pubmed-9959513
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-99595132023-02-26 Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking Bezuglov, Vitalik Stupnikov, Alexey Skakov, Ivan Shtratnikova, Victoria Pilsner, J. Richard Suvorov, Alexander Sergeyev, Oleg Int J Mol Sci Article Expression analysis 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. Despite a range of proposed approaches, selecting and adapting a particular pipeline for transcriptomic analysis of sRNA remains a challenge. This paper focuses on the identification of the optimal pipeline configurations for each step of human sRNA analysis, including reads trimming, filtering, mapping, transcript abundance quantification and differential expression analysis. Based on our study, we suggest the following parameters for the analysis of human sRNA in relation to categorical analyses with two groups of biosamples: (1) trimming with the lower length bound = 15 and the upper length bound = Read length − 40% Adapter length; (2) mapping on a reference genome with bowtie aligner with one mismatch allowed (-v 1 parameter); (3) filtering by mean threshold > 5; (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. MDPI 2023-02-20 /pmc/articles/PMC9959513/ /pubmed/36835604 http://dx.doi.org/10.3390/ijms24044195 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bezuglov, Vitalik
Stupnikov, Alexey
Skakov, Ivan
Shtratnikova, Victoria
Pilsner, J. Richard
Suvorov, Alexander
Sergeyev, Oleg
Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking
title Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking
title_full Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking
title_fullStr Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking
title_full_unstemmed Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking
title_short Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking
title_sort approaches for srna analysis of human rna-seq data: comparison, benchmarking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959513/
https://www.ncbi.nlm.nih.gov/pubmed/36835604
http://dx.doi.org/10.3390/ijms24044195
work_keys_str_mv AT bezuglovvitalik approachesforsrnaanalysisofhumanrnaseqdatacomparisonbenchmarking
AT stupnikovalexey approachesforsrnaanalysisofhumanrnaseqdatacomparisonbenchmarking
AT skakovivan approachesforsrnaanalysisofhumanrnaseqdatacomparisonbenchmarking
AT shtratnikovavictoria approachesforsrnaanalysisofhumanrnaseqdatacomparisonbenchmarking
AT pilsnerjrichard approachesforsrnaanalysisofhumanrnaseqdatacomparisonbenchmarking
AT suvorovalexander approachesforsrnaanalysisofhumanrnaseqdatacomparisonbenchmarking
AT sergeyevoleg approachesforsrnaanalysisofhumanrnaseqdatacomparisonbenchmarking