Assessing computational methods for transcription factor target gene identification based on ChIP-seq data

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 regulat...

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Veröffentlicht in:PLoS computational biology 2013-11, Vol.9 (11), p.e1003342-e1003342
Hauptverfasser: Sikora-Wohlfeld, Weronika, Ackermann, Marit, Christodoulou, Eleni G, Singaravelu, Kalaimathy, Beyer, Andreas
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Sprache:eng
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Zusammenfassung: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003342