A novel Bayesian network inference algorithm for integrative analysis of heterogeneous deep sequencing data
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.
23
grants from the National Natural Science Foundation of China30890033 and 91019019;the Ministry of Science and Technology of China2011CB504206;Chinese Academy of Sciences KSCX2-EW-R-02,KSCX2-EW-J-15 and XDA01010303 to J-D JH,and from Beijing Jiaotong University K12RC00090 to YL
2013-05-02（万方平台首次上网日期，不代表论文的发表时间）
共4页
440-443
