Inference of cell type specific regulatory networks on mammalian lineages.
暂无分享,去创建一个
[1] Neva C. Durand,et al. A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping , 2014, Cell.
[2] Robert Patro,et al. Identification of alternative topological domains in chromatin , 2014, Algorithms for Molecular Biology.
[3] Michael Q. Zhang,et al. Integrative analysis of 111 reference human epigenomes , 2015, Nature.
[4] R. Stewart,et al. Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm , 2016, Genome Biology.
[5] Howard Y. Chang,et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position , 2013, Nature Methods.
[6] Alireza F. Siahpirani,et al. A prior-based integrative framework for functional transcriptional regulatory network inference , 2016, Nucleic acids research.
[7] Terence P. Speed,et al. Bayesian Inference of Signaling Network Topology in a Cancer Cell Line , 2012, Bioinform..
[8] Nathan C. Sheffield,et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. , 2011, Genome research.
[9] V. Beneš,et al. Epigenetic program and transcription factor circuitry of dendritic cell development , 2015, Nucleic acids research.
[10] Job Dekker,et al. Long-range chromosomal interactions and gene regulation. , 2008, Molecular bioSystems.
[11] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[12] M. Severgnini,et al. Dynamic Transcriptional and Epigenetic Regulation of Human Epidermal Keratinocyte Differentiation , 2016, Stem cell reports.
[13] Tongbin Li,et al. Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data , 2015, BMC Bioinformatics.
[14] E. Gusmão,et al. Analysis of computational footprinting methods for DNase sequencing experiments , 2016, Nature Methods.
[15] M. Lupien,et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits , 2014, Genome research.
[16] Michael Q. Zhang,et al. Epigenomic Analysis of Multilineage Differentiation of Human Embryonic Stem Cells , 2013, Cell.
[17] Fabian J Theis,et al. Decoding the Regulatory Network for Blood Development from Single-Cell Gene Expression Measurements , 2015, Nature Biotechnology.
[18] Ting Wang,et al. Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation , 2013, Genome research.
[19] A. Boulesteix,et al. Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach , 2005, Theoretical Biology and Medical Modelling.
[20] Diogo M. Camacho,et al. Wisdom of crowds for robust gene network inference , 2012, Nature Methods.
[21] Jesse R. Dixon,et al. Topological Domains in Mammalian Genomes Identified by Analysis of Chromatin Interactions , 2012, Nature.
[22] E. Marco,et al. Predicting chromatin organization using histone marks , 2015, Genome Biology.
[23] Francisco de A. T. de Carvalho,et al. Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models , 2012, Bioinform..
[24] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[25] Jie Wang,et al. Unsupervised pattern discovery in human chromatin structure through genomic segmentation , 2013, BCB.
[26] J. Wysocka,et al. Modification of enhancer chromatin: what, how, and why? , 2013, Molecular cell.
[27] Or Zuk,et al. Identification of transcriptional regulators in the mouse immune system , 2013, Nature Immunology.
[28] Morteza Mohammad Noori,et al. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features , 2014, PLoS Comput. Biol..
[29] C. Leslie,et al. Memory of Inflammation in Regulatory T Cells , 2016, Cell.
[30] K. Pollard,et al. Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin , 2016, Nature Genetics.
[31] Michael J. Ziller,et al. Dissecting neural differentiation regulatory networks through epigenetic footprinting , 2014, Nature.
[32] Jing Guo,et al. Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development , 2015, Bioinform..
[33] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.
[34] Peter J. Park,et al. hiHMM: Bayesian non-parametric joint inference of chromatin state maps , 2015, Bioinform..
[35] Mariano J. Alvarez,et al. Interrogation of a Context‐Specific Transcription Factor Network Identifies Novel Regulators of Pluripotency , 2015, Stem cells.
[36] Fabian J. Theis,et al. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data , 2015, Bioinform..
[37] H. Gronemeyer,et al. Reconstructed cell fate–regulatory programs in stem cells reveal hierarchies and key factors of neurogenesis , 2016, Genome research.
[38] William Stafford Noble,et al. Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression , 2014, bioRxiv.
[39] L. Mirny,et al. Iterative Correction of Hi-C Data Reveals Hallmarks of Chromosome Organization , 2012, Nature Methods.
[40] Kathleen Marchal,et al. Module networks revisited: computational assessment and prioritization of model predictions , 2009, Bioinform..
[41] Amos Tanay,et al. MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals , 2006, J. Mach. Learn. Res..
[42] N. Friedman,et al. Chromatin state dynamics during blood formation , 2014, Science.
[43] Ben D. MacArthur,et al. Single-Cell Analyses of ESCs Reveal Alternative Pluripotent Cell States and Molecular Mechanisms that Control Self-Renewal , 2015, Stem cell reports.
[44] Vladimir A Bondarenko,et al. Chromatin structure can strongly facilitate enhancer action over a distance , 2006, Proceedings of the National Academy of Sciences.
[45] J. L. Mateo,et al. Characterization of the neural stem cell gene regulatory network identifies OLIG2 as a multifunctional regulator of self-renewal , 2015, Genome research.
[46] Howard Y. Chang,et al. Lineage-specific and single cell chromatin accessibility charts human hematopoiesis and leukemia evolution , 2016, Nature Genetics.
[47] B. Göttgens,et al. Integrated genome-scale analysis of the transcriptional regulatory landscape in a blood stem/progenitor cell model. , 2016, Blood.
[48] Michael Q. Zhang,et al. De novo deciphering three-dimensional chromatin interaction and topological domains by wavelet transformation of epigenetic profiles , 2016, Nucleic acids research.
[49] Shenglin Mei,et al. Modeling cis-regulation with a compendium of genome-wide histone H3K27ac profiles , 2016, Genome research.
[50] J. Dekker,et al. Capturing Chromosome Conformation , 2002, Science.
[51] Alex P. Reynolds,et al. Genome-scale mapping of DNase I hypersensitivity. , 2013, Current protocols in molecular biology.
[52] Richard Bonneau,et al. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo , 2006, Genome Biology.
[53] Shane J. Neph,et al. Developmental Fate and Cellular Maturity Encoded in Human Regulatory DNA Landscapes , 2013, Cell.
[54] Yan Li,et al. A high-resolution map of three-dimensional chromatin interactome in human cells , 2013, Nature.
[55] Benjamin J. Raphael,et al. Identification of hierarchical chromatin domains , 2016, Bioinform..
[56] Mario L. Arrieta-Ortiz,et al. An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network , 2015, Molecular systems biology.
[57] Chee-Hong Wong,et al. Integrative epigenomic analysis reveals unique epigenetic signatures involved in unipotency of mouse female germline stem cells , 2016, Genome Biology.
[58] Sushmita Roy,et al. A multi-task graph-clustering approach for chromosome conformation capture data sets identifies conserved modules of chromosomal interactions , 2016, Genome Biology.
[59] S. Teichmann,et al. Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.
[60] Michael J. Ziller,et al. Transcription factor binding dynamics during human ESC differentiation , 2015, Nature.
[61] Chiara Sabatti,et al. Network component analysis: Reconstruction of regulatory signals in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[62] K. Tan,et al. Global view of enhancer–promoter interactome in human cells , 2014, Proceedings of the National Academy of Sciences.
[63] Riet De Smet,et al. Advantages and limitations of current network inference methods , 2010, Nature Reviews Microbiology.
[64] David R. Kelley,et al. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.
[65] A. Regev,et al. Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.
[66] Mariella G. Filbin,et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma , 2016, Nature.
[67] Richard Bonneau,et al. Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks , 2013, Bioinform..
[68] P. Geurts,et al. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.
[69] Rhonda Bacher,et al. Design and computational analysis of single-cell RNA-sequencing experiments , 2016, Genome Biology.
[70] Patrick J. Paddison,et al. Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis. , 2016, Cell systems.
[71] Manolis Kellis,et al. ChromHMM: automating chromatin-state discovery and characterization , 2012, Nature Methods.
[72] Christina S. Leslie,et al. Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation , 2015, Nature Genetics.
[73] J. Collins,et al. Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.
[74] D. Duboule,et al. Topology of mammalian developmental enhancers and their regulatory landscapes , 2013, Nature.
[75] Pei Wang,et al. Integrative random forest for gene regulatory network inference , 2015, Bioinform..
[76] S. Q. Xie,et al. Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation , 2015, Molecular systems biology.
[77] Alexander R. Pico,et al. Dynamic and Coordinated Epigenetic Regulation of Developmental Transitions in the Cardiac Lineage , 2012, Cell.
[78] Le Song,et al. Time-Varying Dynamic Bayesian Networks , 2009, NIPS.
[79] Jing Liang,et al. Chromatin architecture reorganization during stem cell differentiation , 2015, Nature.
[80] Benjamin J. Strober,et al. A method to predict the impact of regulatory variants from DNA sequence , 2015, Nature Genetics.
[81] G. Crawford,et al. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. , 2010, Cold Spring Harbor protocols.
[82] Richard Bonneau,et al. A Validated Regulatory Network for Th17 Cell Specification , 2012, Cell.
[83] Wei Wu,et al. TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages , 2011, Bioinform..
[84] Chris Wiggins,et al. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.
[85] Daniel Marbach,et al. Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases , 2016, Nature Methods.
[86] Cameron S. Osborne,et al. The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements , 2015, Genome research.
[87] Cole Trapnell,et al. Defining cell types and states with single-cell genomics , 2015, Genome research.
[88] Thomas M. Norman,et al. Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens , 2016, Cell.
[89] Myong-Hee Sung,et al. DNase footprint signatures are dictated by factor dynamics and DNA sequence. , 2014, Molecular cell.
[90] Wei Wang,et al. Constructing 3D interaction maps from 1D epigenomes , 2016, Nature Communications.
[91] K. Hansen,et al. Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data , 2015, Genome Biology.
[92] William Stafford Noble,et al. Analysis methods for studying the 3D architecture of the genome , 2015, Genome Biology.
[93] E. Birney,et al. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. , 2011, Genome research.
[94] A. Kundaje,et al. Learning Regulatory Programs That Accurately Predict Differential Expression with MEDUSA , 2007, Annals of the New York Academy of Sciences.
[95] Ho-Ryun Chung,et al. Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome , 2015, Genome Biology.
[96] Emery H Bresnick,et al. jMOSAiCS: joint analysis of multiple ChIP-seq datasets , 2013, Genome Biology.
[97] Avi Ma'ayan,et al. Construction and Validation of a Regulatory Network for Pluripotency and Self-Renewal of Mouse Embryonic Stem Cells , 2014, PLoS Comput. Biol..
[98] Jason Piper,et al. Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data , 2013, Nucleic acids research.
[99] M. Huss,et al. Distinct transcription factor complexes act on a permissive chromatin landscape to establish regionalized gene expression in CNS stem cells , 2016, Genome research.
[100] Jean-Philippe Vert,et al. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection , 2012, BMC Systems Biology.
[101] A. Regev,et al. Dynamic regulatory network controlling Th17 cell differentiation , 2013, Nature.
[102] Alireza F. Siahpirani,et al. A predictive modeling approach for cell line-specific long-range regulatory interactions , 2015, Nucleic acids research.
[103] Tatsunori B. Hashimoto,et al. A synergistic DNA logic predicts genome-wide chromatin accessibility , 2016, Genome research.
[104] Nathan C. Sheffield,et al. The accessible chromatin landscape of the human genome , 2012, Nature.
[105] Wouter de Laat,et al. Getting the genome in shape: the formation of loops, domains and compartments , 2015, Genome Biology.
[106] O. Stegle,et al. Deep learning for computational biology , 2016, Molecular systems biology.
[107] Yu Zhang,et al. Jointly characterizing epigenetic dynamics across multiple human cell types , 2016, Nucleic acids research.
[108] S. Emmott,et al. Defining an essential transcription factor program for naïve pluripotency , 2014, Science.
[109] Christina S. Leslie,et al. SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps , 2015, PLoS Comput. Biol..
[110] Julia A. Lasserre,et al. Histone modification levels are predictive for gene expression , 2010, Proceedings of the National Academy of Sciences.
[111] Nir Friedman,et al. Inferring Cellular Networks Using Probabilistic Graphical Models , 2004, Science.
[112] James B. Brown,et al. Modeling gene expression using chromatin features in various cellular contexts , 2012, Genome Biology.
[113] I. Amit,et al. Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq , 2016, Cell.
[114] W. Ouwehand,et al. An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability , 2016, eLife.