A novel Bayesian network inference algorithm for integrative analysis of heterogeneous deep sequencing data

• 来源期刊: 细胞研究（英文版）
• 年，卷（期）: 2013,（3）
• 页码范围: 440-443
• 页数: 4页
• 语种: 英文
• DOI: 10.1038/cr.2013.8

摘要

Dear Editor, Next Generation Sequencing (NGS) technology has enabled sequencing millions of short DNA tags in a single pass.NGS-based techniques such as ChIP-Seq/BS-Seq (Chromatin Immunoprecipitation/Bisulfite conversion followed by deep sequencing) have become predominant approaches for genome-wide quantification of transcription factor binding sites,histone modifications/variants and DNA methylation [1].The rapidly increasing volume of ChIP-Seq and other deep sequencing data calls for the urgent need of developing analytical tools for processing these data and extracting meaningful biological knowledge from them.Till now,a number of software tools that are designed to map tag sequences to the genome [2] or to find "peak" chromosomal regions with enriched mapped tags [3] have been readily available,yet tools that target the primary goal of generating testable biological hypotheses directly from NGS data barely exist.