ArticleBioinformatics (Oxford, England)2012
Discovering transcription factor regulatory targets using gene expression and binding data.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 39 citations in OpenAlex.
- White-opaque switching inMicrobiology and molecular biology reviews : MMBR · 2024Review
- Evidence of widespread, independent sequence signature for transcription factor cobinding.Genome research · 2021Article
- Integrating binding and expression data to predict transcription factors combined function.BMC genomics · 2020Article
- Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.Genetics · 2020Article
- An integrative deep learning framework for classifying molecular subtypes of breast cancer.Computational and structural biotechnology journal · 2020Article
- Scalable Nonparametric Prescreening Method for Searching Higher-Order Genetic Interactions Underlying Quantitative Traits.Genetics · 2019Article
- Prediction of Gene Expression Patterns With Generalized Linear Regression Model.Frontiers in genetics · 2019Article
- An integrative method to decode regulatory logics in gene transcription.Nature communications · 2017Article
- A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer.Scientific reports · 2016Article
- Binding Sites in the EFG1 Promoter for Transcription Factors in a Proposed Regulatory Network: A Functional Analysis in the White and Opaque Phases of Candida albicans.G3 (Bethesda, Md.) · 2016Article
- Understanding transcriptional regulatory networks using computational models.Current opinion in genetics & development · 2016Review
- ChIP-Array 2: integrating multiple omics data to construct gene regulatory networks.Nucleic acids research · 2015Article
- Article
- Demystifying the secret mission of enhancers: linking distal regulatory elements to target genes.Critical reviews in biochemistry and molecular biology · 2015Review
- Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.Computational and structural biotechnology journal · 2015Review
- Genomic occupancy of Runx2 with global expression profiling identifies a novel dimension to control of osteoblastogenesis.Genome biology · 2014Article
- Transcriptome-wide signatures of tumor stage in kidney renal clear cell carcinoma: connecting copy number variation, methylation and transcription factor activity.Genome medicine · 2014Article
- Inferring transcription factor collaborations in gene regulatory networks.BMC systems biology · 2014Article
- Target analysis by integration of transcriptome and ChIP-seq data with BETA.Nature protocols · 2013Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
motivationIdentifying the target genes regulated by transcription factors (TFs) is the most basic step in understanding gene regulation. Recent advances in high-throughput sequencing technology, together with chromatin immunoprecipitation (ChIP), enable mapping TF binding sites genome wide, but it is not possible to infer function from binding alone. This is especially true in mammalian systems, where regulation often occurs through long-range enhancers in gene-rich neighborhoods, rather than proximal promoters, preventing straightforward assignment of a binding site to a target gene.
resultsWe present EMBER (Expectation Maximization of Binding and Expression pRofiles), a method that integrates high-throughput binding data (e.g. ChIP-chip or ChIP-seq) with gene expression data (e.g. DNA microarray) via an unsupervised machine learning algorithm for inferring the gene targets of sets of TF binding sites. Genes selected are those that match overrepresented expression patterns, which can be used to provide information about multiple TF regulatory modes. We apply the method to genome-wide human breast cancer data and demonstrate that EMBER confirms a role for the TFs estrogen receptor alpha, retinoic acid receptors alpha and gamma in breast cancer development, whereas the conventional approach of assigning regulatory targets based on proximity does not. Additionally, we compare several predicted target genes from EMBER to interactions inferred previously, examine combinatorial effects of TFs on gene regulation and illustrate the ability of EMBER to discover multiple modes of regulation. AVAILABILITY: All code used for this work is available at http://dinner-group.uchicago.edu/downloads.html.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.