Cargando…

Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy

To date, with well over 100 different types of RNA modifications associated with various molecular functions identified on diverse types of RNA molecules, the epitranscriptome has emerged to be an important layer for gene expression regulation. It is of crucial importance and increasing interest to...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Kunqi, Wei, Zhen, Liu, Hui, de Magalhães, João Pedro, Rong, Rong, Lu, Zhiliang, Meng, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022261/
https://www.ncbi.nlm.nih.gov/pubmed/30013979
http://dx.doi.org/10.1155/2018/2075173
_version_ 1783335642814480384
author Chen, Kunqi
Wei, Zhen
Liu, Hui
de Magalhães, João Pedro
Rong, Rong
Lu, Zhiliang
Meng, Jia
author_facet Chen, Kunqi
Wei, Zhen
Liu, Hui
de Magalhães, João Pedro
Rong, Rong
Lu, Zhiliang
Meng, Jia
author_sort Chen, Kunqi
collection PubMed
description To date, with well over 100 different types of RNA modifications associated with various molecular functions identified on diverse types of RNA molecules, the epitranscriptome has emerged to be an important layer for gene expression regulation. It is of crucial importance and increasing interest to understand how the epitranscriptome is regulated to facilitate different biological functions from a global perspective, which may be carried forward by finding biologically meaningful epitranscriptome modules that respond to upstream epitranscriptome regulators and lead to downstream biological functions; however, due to the intrinsic properties of RNA molecules, RNA modifications, and relevant sequencing technique, the epitranscriptome profiled from high-throughput sequencing approaches often suffers from various artifacts, jeopardizing the effectiveness of epitranscriptome modules identification when using conventional approaches. To solve this problem, we developed a convenient measurement weighting strategy, which can largely tolerate the artifacts of high-throughput sequencing data. We demonstrated on real data that the proposed measurement weighting strategy indeed brings improved performance in epitranscriptome module discovery in terms of both module accuracy and biological significance. Although the new approach is integrated with Euclidean distance measurement in a hierarchical clustering scenario, it has great potential to be extended to other distance measurements and algorithms as well for addressing various tasks in epitranscriptome analysis. Additionally, we show for the first time with rigorous statistical analysis that the epitranscriptome modules are biologically meaningful with different GO functions enriched, which established the functional basis of epitranscriptome modules, fulfilled a key prerequisite for functional characterization, and deciphered the epitranscriptome and its regulation.
format Online
Article
Text
id pubmed-6022261
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-60222612018-07-16 Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy Chen, Kunqi Wei, Zhen Liu, Hui de Magalhães, João Pedro Rong, Rong Lu, Zhiliang Meng, Jia Biomed Res Int Research Article To date, with well over 100 different types of RNA modifications associated with various molecular functions identified on diverse types of RNA molecules, the epitranscriptome has emerged to be an important layer for gene expression regulation. It is of crucial importance and increasing interest to understand how the epitranscriptome is regulated to facilitate different biological functions from a global perspective, which may be carried forward by finding biologically meaningful epitranscriptome modules that respond to upstream epitranscriptome regulators and lead to downstream biological functions; however, due to the intrinsic properties of RNA molecules, RNA modifications, and relevant sequencing technique, the epitranscriptome profiled from high-throughput sequencing approaches often suffers from various artifacts, jeopardizing the effectiveness of epitranscriptome modules identification when using conventional approaches. To solve this problem, we developed a convenient measurement weighting strategy, which can largely tolerate the artifacts of high-throughput sequencing data. We demonstrated on real data that the proposed measurement weighting strategy indeed brings improved performance in epitranscriptome module discovery in terms of both module accuracy and biological significance. Although the new approach is integrated with Euclidean distance measurement in a hierarchical clustering scenario, it has great potential to be extended to other distance measurements and algorithms as well for addressing various tasks in epitranscriptome analysis. Additionally, we show for the first time with rigorous statistical analysis that the epitranscriptome modules are biologically meaningful with different GO functions enriched, which established the functional basis of epitranscriptome modules, fulfilled a key prerequisite for functional characterization, and deciphered the epitranscriptome and its regulation. Hindawi 2018-06-14 /pmc/articles/PMC6022261/ /pubmed/30013979 http://dx.doi.org/10.1155/2018/2075173 Text en Copyright © 2018 Kunqi Chen et al. https://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Research Article
Chen, Kunqi
Wei, Zhen
Liu, Hui
de Magalhães, João Pedro
Rong, Rong
Lu, Zhiliang
Meng, Jia
Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy
title Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy
title_full Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy
title_fullStr Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy
title_full_unstemmed Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy
title_short Enhancing Epitranscriptome Module Detection from m(6)A-Seq Data Using Threshold-Based Measurement Weighting Strategy
title_sort enhancing epitranscriptome module detection from m(6)a-seq data using threshold-based measurement weighting strategy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022261/
https://www.ncbi.nlm.nih.gov/pubmed/30013979
http://dx.doi.org/10.1155/2018/2075173
work_keys_str_mv AT chenkunqi enhancingepitranscriptomemoduledetectionfromm6aseqdatausingthresholdbasedmeasurementweightingstrategy
AT weizhen enhancingepitranscriptomemoduledetectionfromm6aseqdatausingthresholdbasedmeasurementweightingstrategy
AT liuhui enhancingepitranscriptomemoduledetectionfromm6aseqdatausingthresholdbasedmeasurementweightingstrategy
AT demagalhaesjoaopedro enhancingepitranscriptomemoduledetectionfromm6aseqdatausingthresholdbasedmeasurementweightingstrategy
AT rongrong enhancingepitranscriptomemoduledetectionfromm6aseqdatausingthresholdbasedmeasurementweightingstrategy
AT luzhiliang enhancingepitranscriptomemoduledetectionfromm6aseqdatausingthresholdbasedmeasurementweightingstrategy
AT mengjia enhancingepitranscriptomemoduledetectionfromm6aseqdatausingthresholdbasedmeasurementweightingstrategy