title: Assessing computational methods for transcription factor target gene identification based on ChIP-seq data.
abstract: Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial. Numerous target gene scoring methods have been used in the past. However, their suitability for the task and their performance remain unclear, because a thorough comparative assessment of these methods is still lacking. Here we present a systematic evaluation of computational methods for defining TF targets based on ChIP-seq data. We validated predictions based on 68 ChIP-seq studies using a wide range of genomic expression data and functional information. We demonstrate that peak-to-gene assignment is the most crucial step for correct target gene prediction and propose a parameter-free method performing most consistently across the evaluation tests.
authors.affiliation: Biotechnology Center, TU Dresden, Dresden, Germany.
authors.forename: Weronika
authors.initials: W
authors.lastname: Sikora-Wohlfeld
authors.forename: Marit
authors.initials: M
authors.lastname: Ackermann
authors.forename: Eleni G
authors.initials: EG
authors.lastname: Christodoulou
authors.forename: Kalaimathy
authors.initials: K
authors.lastname: Singaravelu
authors.forename: Andreas
authors.initials: A
authors.lastname: Beyer
mesh_terms: D000465:Algorithms; D000818:Animals; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D030541:Databases, Genetic; D016678:Genome; D023281:Genomics / Q000379:methods*; D051379:Mice; D015233:Models, Statistical; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000737:chemistry* / Q000235:genetics*
chemical_list: D014157:Transcription Factors