ArticleProceedings of the National Academy of Sciences of the United States of America2011
Construction of regulatory networks using expression time-series data of a genotyped population.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 86 citations in OpenAlex.
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- Peroxiredoxins and Immune Infiltrations in Colon Adenocarcinoma: Their Negative Correlations and Clinical Significances, an In Silico Analysis.Journal of Cancer · 2020Article
- Integration of Multiple Data Sources for Gene Network Inference Using Genetic Perturbation Data.Journal of computational biology : a journal of computational molecular cell biology · 2019Article
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- Rice Genome-Scale Network Integration Reveals Transcriptional Regulators of Grass Cell Wall Synthesis.Frontiers in plant science · 2019Article
- MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data.BMC systems biology · 2018Article
- Saccharomyces cerevisiae adapted to grow in the presence of low-dose rapamycin exhibit altered amino acid metabolism.Cell communication and signaling : CCS · 2018Article
- Temporal genetic association and temporal genetic causality methods for dissecting complex networks.Nature communications · 2018Article
- InDePTH: detection of hub genes for developing gene expression networks under anticancer drug treatment.Oncotarget · 2018Article
- Genexpi: a toolset for identifying regulons and validating gene regulatory networks using time-course expression data.BMC bioinformatics · 2018Article
- Biologically anchored knowledge expansion approach uncovers KLF4 as a novel insulin signaling regulator.PloS one · 2018Article
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- fastBMA: scalable network inference and transitive reduction.GigaScience · 2017Article
- A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.PLoS computational biology · 2017Article
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Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.
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Registered trials
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.