Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1003342
Title: Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data
Authors: Sikora-Wohlfeld W.
Ackermann M.
Christodoulou E.G. 
Singaravelu K.
Beyer A.
Keywords: transcription factor
article
chromatin immunoprecipitation
computer analysis
gene expression
gene function
gene identification
gene targeting
genetic association
genome
protein binding
sequence analysis
validation process
Algorithms
Animals
Binding Sites
Chromatin Immunoprecipitation
Databases, Genetic
Genome
Genomics
Mice
Models, Statistical
Reproducibility of Results
Sequence Analysis, DNA
Transcription Factors
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
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