ArticlePLoS computational biology2013
Assessing computational methods for transcription factor target gene identification based on ChIP-seq data.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Common factors among three types of cells aged in mice.Biogerontology · 2023Article
- Double DAP-seq uncovered synergistic DNA binding of interacting bZIP transcription factors.Nature communications · 2023Article
- Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets.Genome research · 2023Article
- Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma.Pharmaceutics · 2022Article
- Identification of direct transcriptional targets of NFATC2 that promote β cell proliferation.The Journal of clinical investigation · 2021Article
- EnhFFL: A database of enhancer mediated feed-forward loops for human and mouse.Precision clinical medicine · 2021Article
- AnnoLnc: A One-Stop Portal to Systematically Annotate Novel Human Long Noncoding RNAs.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback.Scientific reports · 2020Article
- A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks.BMC bioinformatics · 2020Article
- Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data.Genome biology · 2020Article
- Terminal neuron localization to the upper cortical plate is controlled by the transcription factor NEUROD2.Scientific reports · 2019Article
- IGF2BP1 promotes SRF-dependent transcription in cancer in a m6A- and miRNA-dependent manner.Nucleic acids research · 2019Article
- Genome-wide identification of FOXL2 binding and characterization of FOXL2 feminizing action in the fetal gonads.Human molecular genetics · 2018Article
- Microenvironmental control of breast cancer subtype elicited through paracrine platelet-derived growth factor-CC signaling.Nature medicine · 2018Article
- Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context.Cell reports · 2018Article
- Complexity and conservation of regulatory landscapes underlie evolutionary resilience of mammalian gene expression.Nature ecology & evolution · 2018Article
- CisMapper: predicting regulatory interactions from transcription factor ChIP-seq data.Nucleic acids research · 2017Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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