ArticleNucleic acids research2016
Weighted enrichment method for prediction of transcription regulators from transcriptome and global chromatin immunoprecipitation data.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
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- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Importin α4 deficiency induces psychiatric disorder-related behavioral deficits and neuroinflammation in mice.Translational psychiatry · 2024Article
- Dynamic and static control of the off-target interactions of antisense oligonucleotides using toehold chemistry.Nature communications · 2023Article
- Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds.Animals : an open access journal from MDPI · 2023Article
- C10orf99/GPR15L Regulates Proinflammatory Response of Keratinocytes and Barrier Formation of the Skin.Frontiers in immunology · 2022Article
- The Cxxc1 subunit of the Trithorax complex directs epigenetic licensing of CD4+ T cell differentiation.The Journal of experimental medicine · 2021Article
- Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements.Nature genetics · 2020Article
- Chronic circadian misalignment accelerates immune senescence and abbreviates lifespan in mice.Scientific reports · 2020Article
- Transcriptome analysis of sevoflurane exposure effects at the different brain regions.PloS one · 2020Article
- Promoter-Level Transcriptome Identifies Stemness Associated With Relatively High Proliferation in Pancreatic Cancer Cells.Frontiers in oncology · 2020Article
- GWAS of mosaic loss of chromosome Y highlights genetic effects on blood cell differentiation.Nature communications · 2019Article
- Co-activation of macrophages and T cells contribute to chronic GVHD in human IL-6 transgenic humanised mouse model.EBioMedicine · 2019Article
- REGGAE: a novel approach for the identification of key transcriptional regulators.Bioinformatics (Oxford, England) · 2018Article
- Evolution of Brain Active Gene Promoters in Human Lineage Towards the Increased Plasticity of Gene Regulation.Molecular neurobiology · 2018Article
- Three-step transcriptional priming that drives the commitment of multipotent progenitors toward B cells.Genes & development · 2018Article
- RegulatorTrail: a web service for the identification of key transcriptional regulators.Nucleic acids research · 2017Article
- Tailor-made transcriptional biosensors for optimizing microbial cell factories.Journal of industrial microbiology & biotechnology · 2017Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting responsible transcription regulators on the basis of transcriptome data is one of the most promising computational approaches to understanding cellular processes and characteristics. Here, we present a novel method employing vast amounts of chromatin immunoprecipitation (ChIP) experimental data to address this issue. Global high-throughput ChIP data was collected to construct a comprehensive database, containing 8 578 738 binding interactions of 454 transcription regulators. To incorporate information about heterogeneous frequencies of transcription factor (TF)-binding events, we developed a flexible framework for gene set analysis employing the weighted t-test procedure, namely weighted parametric gene set analysis (wPGSA). Using transcriptome data as an input, wPGSA predicts the activities of transcription regulators responsible for observed gene expression. Validation of wPGSA with published transcriptome data, including that from over-expressed TFs, showed that the method can predict activities of various TFs, regardless of cell type and conditions, with results totally consistent with biological observations. We also applied wPGSA to other published transcriptome data and identified potential key regulators of cell reprogramming and influenza virus pathogenesis, generating compelling hypotheses regarding underlying regulatory mechanisms. This flexible framework will contribute to uncovering the dynamic and robust architectures of biological regulation, by incorporating high-throughput experimental data in the form of weights.
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