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A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data
BACKGROUND: The recent advent of the state-of-art high throughput sequencing technology, known as Methylated RNA Immunoprecipitation combined with RNA sequencing (MeRIP-seq) revolutionizes the area of mRNA epigenetics and enables the biologists and biomedical researchers to have a global view of N(6...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001242/ https://www.ncbi.nlm.nih.gov/pubmed/27556597 http://dx.doi.org/10.1186/s12864-016-2913-x |
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author | Cui, Xiaodong Meng, Jia Zhang, Shaowu Rao, Manjeet K. Chen, Yidong Huang, Yufei |
author_facet | Cui, Xiaodong Meng, Jia Zhang, Shaowu Rao, Manjeet K. Chen, Yidong Huang, Yufei |
author_sort | Cui, Xiaodong |
collection | PubMed |
description | BACKGROUND: The recent advent of the state-of-art high throughput sequencing technology, known as Methylated RNA Immunoprecipitation combined with RNA sequencing (MeRIP-seq) revolutionizes the area of mRNA epigenetics and enables the biologists and biomedical researchers to have a global view of N(6)-Methyladenosine (m(6)A) on transcriptome. Yet there is a significant need for new computation tools for processing and analysing MeRIP-Seq data to gain a further insight into the function and m(6)A mRNA methylation. RESULTS: We developed a novel algorithm and an open source R package (http://compgenomics.utsa.edu/metcluster) for uncovering the potential types of m(6)A methylation by clustering the degree of m(6)A methylation peaks in MeRIP-Seq data. This algorithm utilizes a hierarchical graphical model to model the reads account variance and the underlying clusters of the methylation peaks. Rigorous statistical inference is performed to estimate the model parameter and detect the number of clusters. MeTCluster is evaluated on both simulated and real MeRIP-seq datasets and the results demonstrate its high accuracy in characterizing the clusters of methylation peaks. Our algorithm was applied to two different sets of real MeRIP-seq datasets and reveals a novel pattern that methylation peaks with less peak enrichment tend to clustered in the 5′ end of both in both mRNAs and lncRNAs, whereas those with higher peak enrichment are more likely to be distributed in CDS and towards the 3′end of mRNAs and lncRNAs. This result might suggest that m(6)A’s functions could be location specific. CONCLUSIONS: In this paper, a novel hierarchical graphical model based algorithm was developed for clustering the enrichment of methylation peaks in MeRIP-seq data. MeTCluster is written in R and is publicly available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2913-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5001242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50012422016-09-06 A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data Cui, Xiaodong Meng, Jia Zhang, Shaowu Rao, Manjeet K. Chen, Yidong Huang, Yufei BMC Genomics Research BACKGROUND: The recent advent of the state-of-art high throughput sequencing technology, known as Methylated RNA Immunoprecipitation combined with RNA sequencing (MeRIP-seq) revolutionizes the area of mRNA epigenetics and enables the biologists and biomedical researchers to have a global view of N(6)-Methyladenosine (m(6)A) on transcriptome. Yet there is a significant need for new computation tools for processing and analysing MeRIP-Seq data to gain a further insight into the function and m(6)A mRNA methylation. RESULTS: We developed a novel algorithm and an open source R package (http://compgenomics.utsa.edu/metcluster) for uncovering the potential types of m(6)A methylation by clustering the degree of m(6)A methylation peaks in MeRIP-Seq data. This algorithm utilizes a hierarchical graphical model to model the reads account variance and the underlying clusters of the methylation peaks. Rigorous statistical inference is performed to estimate the model parameter and detect the number of clusters. MeTCluster is evaluated on both simulated and real MeRIP-seq datasets and the results demonstrate its high accuracy in characterizing the clusters of methylation peaks. Our algorithm was applied to two different sets of real MeRIP-seq datasets and reveals a novel pattern that methylation peaks with less peak enrichment tend to clustered in the 5′ end of both in both mRNAs and lncRNAs, whereas those with higher peak enrichment are more likely to be distributed in CDS and towards the 3′end of mRNAs and lncRNAs. This result might suggest that m(6)A’s functions could be location specific. CONCLUSIONS: In this paper, a novel hierarchical graphical model based algorithm was developed for clustering the enrichment of methylation peaks in MeRIP-seq data. MeTCluster is written in R and is publicly available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2913-x) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-22 /pmc/articles/PMC5001242/ /pubmed/27556597 http://dx.doi.org/10.1186/s12864-016-2913-x Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Cui, Xiaodong Meng, Jia Zhang, Shaowu Rao, Manjeet K. Chen, Yidong Huang, Yufei A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data |
title | A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data |
title_full | A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data |
title_fullStr | A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data |
title_full_unstemmed | A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data |
title_short | A hierarchical model for clustering m(6)A methylation peaks in MeRIP-seq data |
title_sort | hierarchical model for clustering m(6)a methylation peaks in merip-seq data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001242/ https://www.ncbi.nlm.nih.gov/pubmed/27556597 http://dx.doi.org/10.1186/s12864-016-2913-x |
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