Comparing five statistical methods of differential methylation identification using bisulfite sequencing data

DOI10.1515/SAGMB-2015-0078zbMath1343.92028OpenAlexW2319468801WikidataQ35934192 ScholiaQ35934192MaRDI QIDQ306686

Xiaoqing Yu, Shuying Sun

Publication date: 1 September 2016

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/sagmb-2015-0078

zbMATH Keywords

bisulfite sequencingdifferential methylationHMM-DMHMM-Fisher

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) Biochemistry, molecular biology (92C40)

Related Items (3)

HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher's exact test ⋮ HMM-DM: identifying differentially methylated regions using a hidden Markov model ⋮ A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation

Uses Software

• Bioconductor
• Minfi
• methylKit
• BEAT
• BSmooth
• MethylSig
• Bisulfighter
• HMM-DM
• MOABS
• IMA
• HMM-Fisher
• methyAnalysis

Cites Work

• HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher's exact test
• HMM-DM: identifying differentially methylated regions using a hidden Markov model
• Adaptive linear step-up procedures that control the false discovery rate
• False Discovery Rates for Spatial Signals
• Multiple Hypotheses Testing with Weights
• A Direct Approach to False Discovery Rates
• Statistical significance for genomewide studies

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