MOABS: Model based analysis of bisulfite sequencing data

Deqiang Sun; Yuanxin Xi; Benjamin Rodriguez; Hyun J. Park; Pan Tong; Mira Meong; Margaret A. Goodell; Wei Li

doi:10.1186/gb-2014-15-2-r38

MOABS: Model based analysis of bisulfite sequencing data

Deqiang Sun, Yuanxin Xi, Benjamin Rodriguez, Hyun J. Park, Pan Tong, Mira Meong, Margaret A. Goodell, Wei Li

Research output: Contribution to journal › Article › peer-review

201 Scopus citations

Abstract

Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher's exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.

Original language	English (US)
Article number	R38
Journal	Genome biology
Volume	15
Issue number	2
DOIs	https://doi.org/10.1186/gb-2014-15-2-r38
State	Published - Feb 24 2014
Externally published	Yes

ASJC Scopus subject areas

Ecology, Evolution, Behavior and Systematics
Genetics
Cell Biology

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10.1186/gb-2014-15-2-r38

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@article{2ce30fc374544be8a2144cac5a660ee0,

title = "MOABS: Model based analysis of bisulfite sequencing data",

abstract = "Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher's exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.",

author = "Deqiang Sun and Yuanxin Xi and Benjamin Rodriguez and Park, {Hyun J.} and Pan Tong and Mira Meong and Goodell, {Margaret A.} and Wei Li",

note = "Funding Information: We are grateful to Wei Xie for sharing the mouse methylome data, and Grant A. Challen for critical reading of this manuscript. This work was supported by CPRIT RP110471-C3 and NIH R01HG007538 (to WL).",

year = "2014",

month = feb,

day = "24",

doi = "10.1186/gb-2014-15-2-r38",

language = "English (US)",

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journal = "Genome biology",

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TY - JOUR

T1 - MOABS

T2 - Model based analysis of bisulfite sequencing data

AU - Sun, Deqiang

AU - Xi, Yuanxin

AU - Rodriguez, Benjamin

AU - Park, Hyun J.

AU - Tong, Pan

AU - Meong, Mira

AU - Goodell, Margaret A.

AU - Li, Wei

N1 - Funding Information: We are grateful to Wei Xie for sharing the mouse methylome data, and Grant A. Challen for critical reading of this manuscript. This work was supported by CPRIT RP110471-C3 and NIH R01HG007538 (to WL).

PY - 2014/2/24

Y1 - 2014/2/24

N2 - Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher's exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.

AB - Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher's exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.

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MOABS: Model based analysis of bisulfite sequencing data

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