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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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 |
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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 |
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