Computational biology approaches for mapping transcriptional regulatory networks

Computational and Structural Biotechnology Journal - Tập 19 - Trang 4884-4895 - 2021
Violaine Saint-André1
1Hub de Bioinformatique et Biostatistique – Département Biologie Computationnelle, Institut Pasteur, Paris, France

Tài liệu tham khảo

Levine, 2005, Gene regulatory networks for development, Proc Natl Acad Sci U S A, 102, 4936, 10.1073/pnas.0408031102

Stolovitzky G, Monroe D, Califano A. Dialogue on reverse-engineering assessment and methods: The DREAM of high-throughput pathway inference. Ann. N. Y. Acad. Sci., vol. 1115, Blackwell Publishing Inc.; 2007, p. 1–22. https://doi.org/10.1196/annals.1407.021.

Thieffry, 1998, From specific gene regulation to genomic networks: A global analysis of transcriptional regulation in Escherichia coli, BioEssays, 20, 433, 10.1002/(SICI)1521-1878(199805)20:5<433::AID-BIES10>3.0.CO;2-2

Shen-Orr, 2002, Network motifs in the transcriptional regulation network of Escherichia coli, Nat Genet, 31, 64, 10.1038/ng881

Milo, 2002, Network motifs: simple building blocks of complex networks, Science (80-), 298, 824, 10.1126/science.298.5594.824

Lee, 2002, Transcriptional regulatory networks in Saccharomyces cerevisiae, Science, 298, 799, 10.1126/science.1075090

Odom, 2004, Control of pancreas and liver gene expression by HNF transcription factors, Science, 303, 1378, 10.1126/science.1089769

Boyer, 2005, Core transcriptional regulatory circuitry in human embryonic stem cells, Cell, 122, 947, 10.1016/j.cell.2005.08.020

Stergachis, 2014, Conservation of trans-acting circuitry during mammalian regulatory evolution, Nature, 515, 365, 10.1038/nature13972

Rosenfeld, 2002, Negative autoregulation speeds the response times of transcription networks, J Mol Biol, 323, 785, 10.1016/S0022-2836(02)00994-4

Alon, 2007, Network motifs: theory and experimental approaches, Nat Rev Genet, 8, 450, 10.1038/nrg2102

Thieffry, 2007, Dynamical roles of biological regulatory circuits, Brief Bioinform, 8, 220, 10.1093/bib/bbm028

Saint-André, 2016, Models of human core transcriptional regulatory circuitries, Genome Res, 26, 385, 10.1101/gr.197590.115

Mangan, 2006, The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli, J Mol Biol, 356, 1073, 10.1016/j.jmb.2005.12.003

Davidson, 2002, A genomic regulatory network for development, Science (80-), 295, 1669, 10.1126/science.1069883

Odom, 2006, Core transcriptional regulatory circuitry in human hepatocytes, Mol Syst Biol, 2, 10.1038/msb4100059

Sanda, 2012, Core transcriptional regulatory circuit controlled by the TAL1 complex in human T cell acute lymphoblastic leukemia, Cancer Cell, 22, 209, 10.1016/j.ccr.2012.06.007

Niwa H. The principles that govern transcription factor network functions in stem cells 2018. https://doi.org/10.1242/dev.157420.

Ihmels, 2002, Revealing modular organization in the yeast transcriptional network, Nat Genet, 31, 370, 10.1038/ng941

Berman, 2002, Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome, Proc Natl Acad Sci U S A, 99, 757, 10.1073/pnas.231608898

Thieffry, 1999, The modularity of biological regulatory networks, BioSystems, 50, 49, 10.1016/S0303-2647(98)00087-2

Bar-Joseph, 2003, Computational discovery of gene modules and regulatory networks, Nat Biotechnol, 21, 1337, 10.1038/nbt890

Dutkowski, 2013, A gene ontology inferred from molecular networks, Nat Biotechnol, 31, 38, 10.1038/nbt.2463

Gama-Castro, 2016, RegulonDB version 9.0: High-level integration of gene regulation, coexpression, motif clustering and beyond, Nucleic Acids Res, 44, D133, 10.1093/nar/gkv1156

Schulz, 2012, DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data, BMC Syst Biol, 6, 104, 10.1186/1752-0509-6-104

Didier G, Brun C, Baudot A. Identifying communities from multiplex biological networks. PeerJ 2015;2015. https://doi.org/10.7717/peerj.1525.

Cao, 2013, Going the distance for protein function prediction: a new distance metric for protein interaction networks) going the distance for protein function prediction: a new distance metric for protein interaction networks, PLoS ONE, 8, e76339, 10.1371/journal.pone.0076339

Ernst, 2007, Reconstructing dynamic regulatory maps, Mol Syst Biol, 3, 74, 10.1038/msb4100115

Mordelet F, Vert JP. SIRENE: Supervised inference of regulatory networks. Bioinformatics, vol. 24, Bioinformatics; 2008. https://doi.org/10.1093/bioinformatics/btn273.

Lemmens, 2009, DISTILLER: A data integration framework to reveal condition dependency of complex regulons in Escherichia coli, Genome Biol, 10, R27, 10.1186/gb-2009-10-3-r27

Ernst, 2008, A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli, PLoS Comput Biol, 4, e1000044, 10.1371/journal.pcbi.1000044

Woo, 2015, Elucidating compound mechanism of action by network perturbation analysis, Cell, 162, 441, 10.1016/j.cell.2015.05.056

Segal, 2003, Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data, Nat Genet, 34, 166, 10.1038/ng1165

Huynh-Thu, 2010, Inferring regulatory networks from expression data using tree-based methods, PLoS ONE, 5, e12776, 10.1371/journal.pone.0012776

Moerman T, Aibar Santos S, Bravo González-Blas C, Simm J, Moreau Y, Aerts J, et al. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics 2019;35:2159–61. https://doi.org/10.1093/bioinformatics/bty916.

Magnusson R, Gustafsson M. LiPLike: towards gene regulatory network predictions of high certainty. Bioinformatics 2020;36:2522–9. https://doi.org/10.1093/bioinformatics/btz950.

Novershtern, 2011, Densely interconnected transcriptional circuits control cell states in human hematopoiesis, Cell, 144, 296, 10.1016/j.cell.2011.01.004

Choobdar, 2019, Assessment of network module identification across complex diseases, Nat Methods, 16, 843, 10.1038/s41592-019-0509-5

Boyle, 2014, Comparative analysis of regulatory information and circuits across distant species, Nature, 512, 453, 10.1038/nature13668

Marbach, 2016, Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases, Nat Methods, 13, 366, 10.1038/nmeth.3799

Dunham, 2012, An integrated encyclopedia of DNA elements in the human genome, Nature, 489, 57, 10.1038/nature11247

Boix, 2021, Regulatory genomic circuitry of human disease loci by integrative epigenomics, Nature, 590, 300, 10.1038/s41586-020-03145-z

Pérez-Rico, 2017, Comparative analyses of super-enhancers reveal conserved elements in vertebrate genomes, Genome Res, 27, 259, 10.1101/gr.203679.115

Babu, 2004, Structure and evolution of transcriptional regulatory networks, Curr Opin Struct Biol, 14, 283, 10.1016/j.sbi.2004.05.004

Graf, 2009, Forcing cells to change lineages, Nature, 462, 587, 10.1038/nature08533

Gerstein, 2012, Architecture of the human regulatory network derived from ENCODE data, Nature, 489, 91, 10.1038/nature11245

Galagan, 2013, The Mycobacterium tuberculosis regulatory network and hypoxia, Nature, 499, 178, 10.1038/nature12337

Guelzim, 2002, Topological and causal structure of the yeast transcriptional regulatory network, Nat Genet, 31, 60, 10.1038/ng873

Forghani, 2019, Radiomics and artificial intelligence for biomarker and prediction model development in oncology, Comput Struct Biotechnol J, 17, 995, 10.1016/j.csbj.2019.07.001

Margolin, 2006, Reverse engineering cellular networks, Nat Protoc, 1, 662, 10.1038/nprot.2006.106

Faith, 2007, Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles, PLoS Biol, 5, e8, 10.1371/journal.pbio.0050008

Tzfadia, 2016, CoExpNetViz: Comparative co-expression networks construction and visualization tool, Front Plant Sci, 6, 1, 10.3389/fpls.2015.01194

Lachmann, 2016, ARACNe-AP: Gene network reverse engineering through adaptive partitioning inference of mutual information, Bioinformatics, 32, 2233, 10.1093/bioinformatics/btw216

Marbach, 2012, Wisdom of crowds for robust gene network inference, Nat Methods, 9, 796, 10.1038/nmeth.2016

Aibar, 2017, SCENIC: Single-cell regulatory network inference and clustering, Nat Methods, 14, 1083, 10.1038/nmeth.4463

Yu, 2004, Advances to Bayesian network inference for generating causal networks from observational biological data, Bioinformatics, 20, 3594, 10.1093/bioinformatics/bth448

Joshi, 2009, Module networks revisited: computational assessment and prioritization of model predictions, Bioinformatics, 25, 490, 10.1093/bioinformatics/btn658

Saint-André, 2011, Histone H3 lysine 9 trimethylation and HP1γ favor inclusion of alternative exons, Nat Struct Mol Biol, 18, 337, 10.1038/nsmb.1995

Saha, 2016, Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Co-expression networks reveal tissue-specific, Regul Transcr Splicing, 078741

Gardner, 2003, Inferring genetic networks and identifying compound mode of action via expression profiling, Science (80-), 301, 102, 10.1126/science.1081900

Kauffman, 1973, Control circuits for determination and transdetermination, Science (80-), 181, 310, 10.1126/science.181.4097.310

Naldi, 2009, Logical modelling of regulatory networks with GINsim 2.3, BioSystems, 97, 134, 10.1016/j.biosystems.2009.04.008

Naldi, 2018, The CoLoMoTo interactive notebook: Accessible and reproducible computational analyses for qualitative biological networks, Front Physiol, 9, 10.3389/fphys.2018.00680

Batt, 2012, Genetic network analyzer: A tool for the qualitative modeling and simulation of bacterial regulatory networks, Methods Mol Biol, 804, 439, 10.1007/978-1-61779-361-5_22

Feizi, 2013, Network deconvolution as a general method to distinguish direct dependencies in networks, Nat Biotechnol, 31, 726, 10.1038/nbt.2635

Ren, 2000, Genome-wide location and function of DNA binding proteins, Science (80-), 290, 2306, 10.1126/science.290.5500.2306

Zeitlinger, 2007, Whole-genome ChIP-chip analysis of Dorsal, Twist, and Snail suggests integration of diverse patterning processes in the Drosophila embryo, Genes Dev, 21, 385, 10.1101/gad.1509607

Kheradpour, 2007, Reliable prediction of regulator targets using 12 Drosophila genomes, Genome Res, 17, 1919, 10.1101/gr.7090407

Marbach, 2012, Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks, Genome Res, 22, 1334, 10.1101/gr.127191.111

Yip, 2010, Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data, PLoS ONE, 5, e8121, 10.1371/journal.pone.0008121

Kemmeren, 2014, Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors, Cell, 157, 740, 10.1016/j.cell.2014.02.054

Shannon, 2003, Cytoscape: A software Environment for integrated models of biomolecular interaction networks, Genome Res, 13, 2498, 10.1101/gr.1239303

Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, Lau E, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 2014;343:1246949. https://doi.org/10.1126/science.1246949.

Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science (80-) 2018;362. https://doi.org/10.1126/science.aat8464.

Krzywinski, 2009, Circos: An information aesthetic for comparative genomics, Genome Res, 19, 1639, 10.1101/gr.092759.109

Mohammadi, 2018, A geometric approach to characterize the functional identity of single cells, Nat Commun, 9, 10.1038/s41467-018-03933-2

Moignard, 2015, Decoding the regulatory network of early blood development from single-cell gene expression measurements, Nat Biotechnol, 33, 269, 10.1038/nbt.3154

Matsumoto H, Kiryu H, Furusawa C, Ko MSH, Ko SBH, Gouda N, et al. SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics 2017;33:2314–21. https://doi.org/10.1093/bioinformatics/btx194.

Lin, 2020, Inferring TF activation order in time series scRNA-Seq studies, PLOS Comput Biol, 16, e1007644, 10.1371/journal.pcbi.1007644

Cantini, 2021, Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer, Nat Commun, 12, 1, 10.1038/s41467-020-20430-7

Pratapa, 2020, Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data, Nat Methods, 17, 147, 10.1038/s41592-019-0690-6

Lefebvre, 2010, A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers, Mol Syst Biol, 6, 377, 10.1038/msb.2010.31

Novarino, 2014, Exome sequencing links corticospinal motor neuron disease to common neurodegenerative disorders, Science (80-), 343, 506, 10.1126/science.1247363

Pierson, 2015, Sharing and specificity of co-expression networks across 35 human tissues, PLoS Comput Biol, 11, 1, 10.1371/journal.pcbi.1004220

Dixon, 2012, Topological domains in mammalian genomes identified by analysis of chromatin interactions, Nature, 485, 376, 10.1038/nature11082

Nora, 2012, Spatial partitioning of the regulatory landscape of the X-inactivation centre, Nature, 485, 381, 10.1038/nature11049

Neph, 2012, An expansive human regulatory lexicon encoded in transcription factor footprints, Nature, 489, 83, 10.1038/nature11212

Moore, 2020, Expanded encyclopaedias of DNA elements in the human and mouse genomes, Nature, 583, 699, 10.1038/s41586-020-2493-4

Hnisz, 2013, Super-enhancers in the control of cell identity and disease, Cell, 155, 934, 10.1016/j.cell.2013.09.053

Creyghton, 2010, Histone H3K27ac separates active from poised enhancers and predicts developmental state, Proc Natl Acad Sci U S A, 107, 21931, 10.1073/pnas.1016071107

Wang, 2018, Retinal cell type DNA methylation and histone modifications predict reprogramming efficiency and retinogenesis in 3D organoid cultures, Cell Rep, 22, 2601, 10.1016/j.celrep.2018.01.075

Bradner, 2017, Transcriptional addiction in cancer, Cell, 168, 629, 10.1016/j.cell.2016.12.013

Boija, 2021, Biomolecular condensates and cancer, Cancer Cell, 39, 174, 10.1016/j.ccell.2020.12.003

Nguyen NTT, Contreras-Moreira B, Castro-Mondragon JA, Santana-Garcia W, Ossio R, Robles-Espinoza CD, et al. RSAT 2018: Regulatory sequence analysis tools 20th anniversary. Nucleic Acids Res 2018;46:W209–14. https://doi.org/10.1093/nar/gky317.

Mariani, 2020, MedeA: Analysis of transcription factor binding motifs in accessible chromatin, Genome Res, 30, 736, 10.1101/gr.260877.120

Mariani, 2017, Identification of human lineage-specific transcriptional coregulators enabled by a glossary of binding modules and tunable genomic backgrounds, Cell Syst, 5, 187, 10.1016/j.cels.2017.06.015

Gheorghe M, Sandve GK, Khan A, Chèneby J, Ballester B, Mathelier A. A map of direct TF-DNA interactions in the human genome. Nucleic Acids Res 2019;47. https://doi.org/10.1093/nar/gky1210.

Yosef, 2013, Dynamic regulatory network controlling TH 17 cell differentiation, Nature, 496, 461, 10.1038/nature11981

Alculumbre, 2018, Diversification of human plasmacytoid predendritic cells in response to a single stimulus article, Nat Immunol, 19, 63, 10.1038/s41590-017-0012-z

Piasecka, 2018, Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges, Proc Natl Acad Sci, 115, E488, 10.1073/pnas.1714765115

Jiang, 2018, Genome-scale signatures of gene interaction from compound screens predict clinical efficacy of targeted cancer therapies, Cell Syst, 6, 343, 10.1016/j.cels.2018.01.009

Kalinin, 2018, Deep learning in pharmacogenomics: From gene regulation to patient stratification, Pharmacogenomics, 19, 629, 10.2217/pgs-2018-0008