CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data | Zendy

Xi Chen | Zendy; Jinghua Gu | Zendy; Xiao Wang | Zendy; Jin-Gyoung Jung | Zendy; TianLi Wang | Zendy; Leena HilakiviClarke | Zendy; Robert Clarke | Zendy; Jianhua Xuan | Zendy

Open Access

CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data

Author(s) -

Xi Chen,

Jinghua Gu,

Xiao Wang,

Jin-Gyoung Jung,

TianLi Wang,

Leena HilakiviClarke,

Robert Clarke,

Jianhua Xuan

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx827

Subject(s) - rna seq , computer science , scale (ratio) , sampling (signal processing) , computational biology , software , chip , data mining , biology , gene , gene expression , transcriptome , genetics , telecommunications , cartography , detector , programming language , geography

NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIP-seq and time-course RNA-seq data.

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