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|Title:||Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data||Authors:||Sikora-Wohlfeld W.
Reproducibility of Results
Sequence Analysis, DNA
|Issue Date:||2013||Publisher:||Public Library of Science||Citation:||Sikora-Wohlfeld W., Ackermann M., Christodoulou E.G., Singaravelu K., Beyer A. (2013). Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data. PLoS Computational Biology 9 (11) : e1003342. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1003342||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. © 2013 Sikora-Wohlfeld et al.||Source Title:||PLoS Computational Biology||URI:||https://scholarbank.nus.edu.sg/handle/10635/165401||ISSN:||1553734X||DOI:||10.1371/journal.pcbi.1003342|
|Appears in Collections:||Staff Publications|
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