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MethGo: a comprehensive tool for analyzing whole-genome bisulfite sequencing data
被引:17
作者:

Liao, Wen-Wei
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Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan

Yen, Ming-Ren
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Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan

Ju, Evaline
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机构:
Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan

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Lam, Larry
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Univ Calif Los Angeles, Dept Mol Cell & Dev Biol, Los Angeles, CA 90095 USA Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan

Chen, Pao-Yang
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h-index: 0
机构:
Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan
机构:
[1] Acad Sinica, Inst Plant & Microbial Biol, Taipei 11529, Taiwan
[2] Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA 15213 USA
[3] Univ Calif Los Angeles, Dept Mol Cell & Dev Biol, Los Angeles, CA 90095 USA
来源:
关键词:
DNA METHYLATION;
D O I:
10.1186/1471-2164-16-S12-S11
中图分类号:
Q81 [生物工程学(生物技术)];
Q93 [微生物学];
学科分类号:
071005 ;
0836 ;
090102 ;
100705 ;
摘要:
Background: DNA methylation is a major epigenetic modification regulating several biological processes. A standard approach to measure DNA methylation is bisulfite sequencing (BS-Seq). BS-Seq couples bisulfite conversion of DNA with next-generation sequencing to profile genome-wide DNA methylation at single base resolution. The analysis of BS-Seq data involves the use of customized aligners for mapping bisulfite converted reads and the bioinformatic pipelines for downstream data analysis. Results: Here we developed MethGo, a software tool designed for the analysis of data from whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS). MethGo provides both genomic and epigenomic analyses including: 1) coverage distribution of each cytosine; 2) global cytosine methylation level; 3) cytosine methylation level distribution; 4) cytosine methylation level of genomic elements; 5) chromosome-wide cytosine methylation level distribution; 6) Gene-centric cytosine methylation level; 7) cytosine methylation levels at transcription factor binding sites (TFBSs); 8) single nucleotide polymorphism (SNP) calling, and 9) copy number variation (CNV) calling. Conclusions: MethGo is a simple and effective tool for the analysis of BS-Seq data including both WGBS and RRBS. It contains 9 analyses in 5 major modules to profile (epi)genome. It profiles genome-wide DNA methylation in global and in gene level scale. It can also analyze the methylation pattern around the transcription factor binding sites, and assess genetic variations such as SNPs and CNVs. MethGo is coded in Python and is publically available at http://paoyangchen-laboratory.github.io/methgo/.
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