Inference of cell type specific regulatory networks on mammalian lineages

De Smet, 2010, Advantages and limitations of current network inference methods, Nat Rev Microbiol, 8, 717, 10.1038/nrmicro2419 Friedman, 2004, Inferring cellular networks using probabilistic graphical models, Science, 303, 799, 10.1126/science.1094068 Bonneau, 2006, The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo, Genome Biol, 7, R36, 10.1186/gb-2006-7-5-r36 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 Margolin, 2006, ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinform, 7, S7, 10.1186/1471-2105-7-S1-S7 Haury, 2012, Tigress: trustful inference of gene regulation using stability selection, BMC Syst Biol, 6, 145, 10.1186/1752-0509-6-145 Huynh-Thu, 2010, Inferring regulatory networks from expression data using tree-based methods, PLoS One, 5, e12776, 10.1371/journal.pone.0012776 Marbach, 2012, Wisdom of crowds for robust gene network inference, Nat Methods, 9, 796, 10.1038/nmeth.2016 Pe’er, 2006, Minreg: a scalable algorithm for learning parsimonious regulatory networks in yeast and mammals, J Mach Learn Res, 7, 167 Joshi, 2009, Module networks revisited: computational assessment and prioritization of model predictions, Bioinformatics, 25, 490, 10.1093/bioinformatics/btn658 Ciofani, 2012, A validated regulatory network for Th17 cell specification, Cell, 151, 289, 10.1016/j.cell.2012.09.016 Yosef, 2013, Dynamic regulatory network controlling Th17 cell differentiation, Nature, 496, 461, 10.1038/nature11981 Kushwaha, 2015, Interrogation of a context-specific transcription factor network identifies novel regulators of pluripotency, Stem Cells, 33, 367, 10.1002/stem.1870 Plaisier, 2016, Causal mehanistic regulatory network for glioblastoma deciphered using systems genetics network analysis, Cell Syst, 3, 172, 10.1016/j.cels.2016.06.006 Greenfield, 2013, Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks, Bioinformatics, 29, 1060, 10.1093/bioinformatics/btt099 Siahpirani, 2016, A prior-based integrative framework for functional transcriptional regulatory network inference, Nucleic Acids Res, 45, e41 Hill, 2012, Bayesian inference of signaling network topology in a cancer cell line, Bioinformatics, 28, 2804, 10.1093/bioinformatics/bts514 Petralia, 2015, Integrative random forest for gene regulatory network inference, Bioinformatics, 31, i197, 10.1093/bioinformatics/btv268 Liao, 2003, Network component analysis: reconstruction of regulatory signals in biological systems, Proc Natl Acad Sci, 100, 15522, 10.1073/pnas.2136632100 Boulesteix, 2005, Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach, Theor Biol Med Model, 2, 23, 10.1186/1742-4682-2-23 Arrieta-Ortiz, 2015, An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network, Mol Syst Biol, 11, 839, 10.15252/msb.20156236 Gitter Trapnell, 2015, Defining cell types and states with single-cell genomics, Genome Res, 25, 1491, 10.1101/gr.190595.115 Bacher, 2016, Design and computational analysis of single-cell RNA-sequencing experiments, Genome Biol, 17, 63, 10.1186/s13059-016-0927-y Moignard, 2015, Decoding the regulatory network of early blood development from single-cell gene expression measurements, Nat Biotechnol, 33, 269, 10.1038/nbt.3154 Chen, 2015, Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development, Bioinformatics, 31, 1060, 10.1093/bioinformatics/btu777 Schütte, 2016, An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability, Elife, 5, e11469, 10.7554/eLife.11469 Ocone, 2015, Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data, Bioinformatics, 31, i89, 10.1093/bioinformatics/btv257 Xu, 2014, Construction and validation of a regulatory network for pluripotency and self-renewal of mouse embryonic stem cells, PLoS Comput Biol, 10, e1003777, 10.1371/journal.pcbi.1003777 Papatsenko, 2015, Single-cell analyses of ESCs reveal alternative pluripotent cell states and molecular mechanisms that control self-renewal, Stem Cell Rep, 5, 207, 10.1016/j.stemcr.2015.07.004 Dunn, 2014, Defining an essential transcription factor program for naïve pluripotency, Science, 344, 1156, 10.1126/science.1248882 Chu, 2016, Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm, Genome Biol, 17, 173, 10.1186/s13059-016-1033-x Tirosh, 2016, Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma, Nature, 539, 309, 10.1038/nature20123 Stegle, 2015, Computational and analytical challenges in single-cell transcriptomics, Nat Rev Genet, 16, 133, 10.1038/nrg3833 Wagner, 2016, Revealing the vectors of cellular identity with single-cell genomics, Nat Biotechnol, 34, 1145, 10.1038/nbt.3711 Tsankov, 2015, Transcription factor binding dynamics during human ES cell differentiation, Nature, 518, 344, 10.1038/nature14233 Wamstad, 2012, Dynamic and coordinated epigenetic regulation of developmental transitions in the cardiac lineage, Cell, 151, 206, 10.1016/j.cell.2012.07.035 Xie, 2013, Epigenomic analysis of multilineage differentiation of human embryonic stem cells, Cell, 153, 1134, 10.1016/j.cell.2013.04.022 Lin, 2015, Epigenetic program and transcription factor circuitry of dendritic cell development, Nucleic Acids Res, 43, 9680 Mateo, 2015, Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal, Genome Res, 25, 41, 10.1101/gr.173435.114 Ziller, 2015, Dissecting neural differentiation regulatory networks through epigenetic footprinting, Nature, 518, 355, 10.1038/nature13990 Hagey, 2016, Distinct transcription factor complexes act on a permissive chromatin landscape to establish regionalized gene expression in CNS stem cells, Genome Res, 26, 908, 10.1101/gr.203513.115 Cavazza, 2016, Dynamic transcriptional and epigenetic regulation of human epidermal keratinocyte differentiation, Stem Cell Rep, 6, 618, 10.1016/j.stemcr.2016.03.003 Corces, 2016, Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution, Nat Genet, 48, 1193, 10.1038/ng.3646 van der Veeken, 2016, Memory of inflammation in regulatory T cells, Cell, 166, 977, 10.1016/j.cell.2016.07.006 Zhang, 2016, Integrative epigenomic analysis reveals unique epigenetic signatures involved in unipotency of mouse female germline stem cells, Genome Biol, 17, 162, 10.1186/s13059-016-1023-z Lara-Astiaso, 2014, Immunogenetics. Chromatin state dynamics during blood formation, Science, 345, 943, 10.1126/science.1256271 Roadmap Epigenomics Consortium, 2015, Integrative analysis of 111 reference human epigenomes, Nature, 518, 317, 10.1038/nature14248 Thurman, 2012, The accessible chromatin landscape of the human genome, Nature, 489, 75, 10.1038/nature11232 Stergachis, 2013, Developmental fate and cellular maturity encoded in human regulatory DNA landscapes, Cell, 154, 888, 10.1016/j.cell.2013.07.020 Dixon, 2015, Chromatin architecture reorganization during stem cell differentiation, Nature, 518, 331336, 10.1038/nature14222 Jin, 2013, A high-resolution map of the three-dimensional chromatin interactome in human cells, Nature, 503, 290, 10.1038/nature12644 Schoenfelder, 2015, The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements, Genome Res, 25, 582, 10.1101/gr.185272.114 Fraser, 2015, Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation, Mol Syst Biol, 11, 852852, 10.15252/msb.20156492 Ernst, 2012, ChromHMM: automating chromatin-state discovery and characterization, Nat Methods, 9, 215, 10.1038/nmeth.1906 Hoffman, 2012, Unsupervised pattern discovery in human chromatin structure through genomic segmentation, Nat Methods, 9, 473, 10.1038/nmeth.1937 Yu, 2013, Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation, Genome Res, 23, 352, 10.1101/gr.144949.112 Zeng, 2013, jMOSAiCS: joint analysis of multiple chip-seq datasets, Genome Biol, 14, R38, 10.1186/gb-2013-14-4-r38 Mammana, 2015, Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome, Genome Biol, 16, 151, 10.1186/s13059-015-0708-z Sohn, 2015, hihmm: Bayesian non-parametric joint inference of chromatin state maps, Bioinformatics, 31, 2066, 10.1093/bioinformatics/btv117 Zhang, 2016, Jointly characterizing epigenetic dynamics across multiple human cell types, Nucleic Acids Res, 44, 6721, 10.1093/nar/gkw278 Libbrecht, 2015, Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression, Genome Res, 25, 544, 10.1101/gr.184341.114 Song, 2010, DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells, Cold Spring Harb Protoc, 2010, 10.1101/pdb.prot5384 John, 2013, Genome-scale mapping of DNase I hypersensitivity, Curr Protoc Mol Biol, 10.1002/0471142727.mb2127s103 Buenrostro, 2013, Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position, Nat Methods, 10, 1213, 10.1038/nmeth.2688 Gusmao, 2016, Analysis of computational footprinting methods for DNase sequencing experiments, Nat Methods, 13, 303, 10.1038/nmeth.3772 Boyle, 2011, High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells, Genome Res, 21, 456, 10.1101/gr.112656.110 Piper, 2013, Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data, Nucleic Acids Res, 41, e201, 10.1093/nar/gkt850 Sung, 2014, DNase footprint signatures are dictated by factor dynamics and DNA sequence, Mol Cell, 56, 275, 10.1016/j.molcel.2014.08.016 Setty, 2015, SeqGL identifies context-dependent binding signals in genome-wide regulatory element maps, PLoS Comput Biol, 11, e1004271, 10.1371/journal.pcbi.1004271 Ghandi, 2014, Enhanced regulatory sequence prediction using gapped k-mer features, PLoS Comput Biol, 10, e1003711, 10.1371/journal.pcbi.1003711 Lee, 2015, A method to predict the impact of regulatory variants from DNA sequence, Nat Genet, 47, 955, 10.1038/ng.3331 Hashimoto, 2016, A synergistic DNA logic predicts genome-wide chromatin accessibility, Genome Res, 26, 1430, 10.1101/gr.199778.115 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Angermueller, 2016, Deep learning for computational biology, Mol Syst Biol, 12, 878, 10.15252/msb.20156651 Zhou, 2015, Predicting effects of noncoding variants with deep learning-based sequence model, Nat Methods, 12, 931, 10.1038/nmeth.3547 Alipanahi, 2015, Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning, Nat Biotechnol, 33, 831, 10.1038/nbt.3300 Kelley, 2016, Basset: learning the regulatory code of the accessible geome with deep convolutional neural networks, Genome Res, 26, 990, 10.1101/gr.200535.115 Calo, 2013, Modification of enhancer chromatin: what, how, and why?, Mol Cell, 49, 825, 10.1016/j.molcel.2013.01.038 Rubtsov, 2006, Chromatin structure can strongly facilitate enhancer action over a distance, Proc Natl Acad Sci, 103, 17690, 10.1073/pnas.0603819103 Miele, 2008, Long-range chromosomal interactions and gene regulation, Mol Biosyst, 4, 1046, 10.1039/b803580f de Laat, 2013, Topology of mammalian developmental enhancers and their regulatory landscapes, Nature, 502, 499, 10.1038/nature12753 Dekker, 2002, Capturing chromosome conformation, Science, 295, 1306, 10.1126/science.1067799 Ay, 2015, Analysis methods for studying the 3D architecture of the genome, Genome Biol, 16, 10.1186/s13059-015-0745-7 Zhu, 2016, Constructing 3D interaction maps from 1D epigenomes, Nat Commun, 7, 10812+, 10.1038/ncomms10812 Corradin, 2014, Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits, Genome Res, 24, 1, 10.1101/gr.164079.113 Marbach, 2016, Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases, Nat Methods, 13, 366, 10.1038/nmeth.3799 He, 2014, Global view of enhancer-promoter interactome in human cell, Proc Natl Acad Sci, 111, E2191, 10.1073/pnas.1320308111 Roy, 2015, A predictive modeling approach for cell line-specific long-range regulatory interactions, Nucleic Acids Res, 43, 8694, 10.1093/nar/gkv865 Whalen, 2016, Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin, Nat Genet, 48, 488, 10.1038/ng.3539 Chen, 2016, De novo deciphering three-dimensional chromatin interaction and topological domains by wavelet transformation of epigenetic profiles, Nucleic Acids Res, 44, e106, 10.1093/nar/gkw225 Bouwman, 2015, Getting the genome in shape: the formation of loops, domains and compartments, Genome Biol, 16, 10.1186/s13059-015-0730-1 Imakaev, 2012, Iterative correction of Hi-C data reveals hallmarks of chromosome organization, Nat Methods, 9, 999, 10.1038/nmeth.2148 Rao, 2014, A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping, Cell, 159, 1665, 10.1016/j.cell.2014.11.021 Fortin, 2015, Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data, Genome Biol, 16, 180, 10.1186/s13059-015-0741-y Dixon, 2012, Topological domains in mammalian genomes identified by analysis of chromatin interactions, Nature, 485, 376380, 10.1038/nature11082 Filippova, 2014, Identification of alternative topological domains in chromatin, Algorithms Mol Biol, 9, 14, 10.1186/1748-7188-9-14 Weinreb, 2015, Identification of hierarchical chromatin domains, Bioinformatics, 32, 1601, 10.1093/bioinformatics/btv485 Huang, 2015, Predicting chromatin organization using histone marks, Genome Biol, 16, 162, 10.1186/s13059-015-0740-z Wilson, 2016, Integrated genome-scale analysis of the transcriptional regulatory landscape in a blood stem/progenitor cell model, Blood, 127, e12, 10.1182/blood-2015-10-677393 Fotuhi Siahpirani, 2016, A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions, Genome Biol, 17, 114, 10.1186/s13059-016-0962-8 Song, 2011, Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity, Genome Res, 21, 17571767, 10.1101/gr.121541.111 Kundaje, 2007, Learning regulatory programs that accurately predict differential expression with MEDUSA, Ann N Y Acad Sci, 1115, 178, 10.1196/annals.1407.020 Karlić, 2010, Histone modification levels are predictive for gene expression, Proc Natl Acad Sci, 107, 2926, 10.1073/pnas.0909344107 Dong, 2012, Modeling gene expression using chromatin features in various cellular contexts, Genome Biol, 13, R53, 10.1186/gb-2012-13-9-r53 do Rego, 2012, Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models, Bioinformatics, 28, 2297, 10.1093/bioinformatics/bts362 González, 2015, Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation, Nat Genet, 47, 1249, 10.1038/ng.3402 Wang, 2016, Modeling cis-regulation with a compendium of genome-wide histone H3K27ac profiles, Genome Res, 10.1101/gr.201574.115 Gong, 2015, Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data, BMC Bioinform, 16, 74, 10.1186/s12859-015-0460-0 Mendoza-Parra, 2016, Reconstructed cell fate-regulatory programs in stem cells reveal hierarchies and key factors of neurogenesis, Genome Res, 26, 1505, 10.1101/gr.208926.116 Parikh, 2011, Treegl: reverse engineering tree-evolving gene networks underlying developing biological lineages, Bioinformatics, 27, i196, 10.1093/bioinformatics/btr239 Jojic, 2013, Identification of transcriptional regulators in the mouse immune system, Nat Immunol, 14, 633, 10.1038/ni.2587 Song, 2009, Time-varying dynamic Bayesian networks, 1732 Dixit, 2016, Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens, Cell, 167, 1853, 10.1016/j.cell.2016.11.038 Jaitin, 2016, Dissecting immune circuits by linking CRISPR-pooled screens with Single-Cell RNA-seq, Cell, 167, 1883, 10.1016/j.cell.2016.11.039