Cargando…

Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications

Analysis of RNA by deep-sequencing approaches has found widespread application in modern biology. In addition to measurements of RNA abundance under various physiological conditions, such techniques are now widely used for mapping and quantification of RNA modifications. Transfer RNA (tRNA) molecule...

Descripción completa

Detalles Bibliográficos
Autores principales: PICHOT, Florian, MARCHAND, Virginie, HELM, Mark, MOTORIN, Yuri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827920/
https://www.ncbi.nlm.nih.gov/pubmed/33435213
http://dx.doi.org/10.3390/genes12010081
_version_ 1783640884618723328
author PICHOT, Florian
MARCHAND, Virginie
HELM, Mark
MOTORIN, Yuri
author_facet PICHOT, Florian
MARCHAND, Virginie
HELM, Mark
MOTORIN, Yuri
author_sort PICHOT, Florian
collection PubMed
description Analysis of RNA by deep-sequencing approaches has found widespread application in modern biology. In addition to measurements of RNA abundance under various physiological conditions, such techniques are now widely used for mapping and quantification of RNA modifications. Transfer RNA (tRNA) molecules are among the frequent targets of such investigation, since they contain multiple modified residues. However, the major challenge in tRNA examination is related to a large number of duplicated and point-mutated genes encoding those RNA molecules. Moreover, the existence of multiple isoacceptors/isodecoders complicates both the analysis and read mapping. Existing databases for tRNA sequencing provide near exhaustive listings of tRNA genes, but the use of such highly redundant reference sequences in RNA-seq analyses leads to a large number of ambiguously mapped sequencing reads. Here we describe a relatively simple computational strategy for semi-automatic collapsing of highly redundant tRNA datasets into a non-redundant collection of reference tRNA sequences. The relevance of the approach was validated by analysis of experimentally obtained tRNA-sequencing datasets for different prokaryotic and eukaryotic model organisms. The data demonstrate that non-redundant tRNA reference sequences allow improving unambiguous mapping of deep sequencing data.
format Online
Article
Text
id pubmed-7827920
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-78279202021-01-25 Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications PICHOT, Florian MARCHAND, Virginie HELM, Mark MOTORIN, Yuri Genes (Basel) Article Analysis of RNA by deep-sequencing approaches has found widespread application in modern biology. In addition to measurements of RNA abundance under various physiological conditions, such techniques are now widely used for mapping and quantification of RNA modifications. Transfer RNA (tRNA) molecules are among the frequent targets of such investigation, since they contain multiple modified residues. However, the major challenge in tRNA examination is related to a large number of duplicated and point-mutated genes encoding those RNA molecules. Moreover, the existence of multiple isoacceptors/isodecoders complicates both the analysis and read mapping. Existing databases for tRNA sequencing provide near exhaustive listings of tRNA genes, but the use of such highly redundant reference sequences in RNA-seq analyses leads to a large number of ambiguously mapped sequencing reads. Here we describe a relatively simple computational strategy for semi-automatic collapsing of highly redundant tRNA datasets into a non-redundant collection of reference tRNA sequences. The relevance of the approach was validated by analysis of experimentally obtained tRNA-sequencing datasets for different prokaryotic and eukaryotic model organisms. The data demonstrate that non-redundant tRNA reference sequences allow improving unambiguous mapping of deep sequencing data. MDPI 2021-01-10 /pmc/articles/PMC7827920/ /pubmed/33435213 http://dx.doi.org/10.3390/genes12010081 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
PICHOT, Florian
MARCHAND, Virginie
HELM, Mark
MOTORIN, Yuri
Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications
title Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications
title_full Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications
title_fullStr Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications
title_full_unstemmed Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications
title_short Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications
title_sort non-redundant trna reference sequences for deep sequencing analysis of trna abundance and epitranscriptomic rna modifications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827920/
https://www.ncbi.nlm.nih.gov/pubmed/33435213
http://dx.doi.org/10.3390/genes12010081
work_keys_str_mv AT pichotflorian nonredundanttrnareferencesequencesfordeepsequencinganalysisoftrnaabundanceandepitranscriptomicrnamodifications
AT marchandvirginie nonredundanttrnareferencesequencesfordeepsequencinganalysisoftrnaabundanceandepitranscriptomicrnamodifications
AT helmmark nonredundanttrnareferencesequencesfordeepsequencinganalysisoftrnaabundanceandepitranscriptomicrnamodifications
AT motorinyuri nonredundanttrnareferencesequencesfordeepsequencinganalysisoftrnaabundanceandepitranscriptomicrnamodifications