Literature DB >> 22955619

Architecture of the human regulatory network derived from ENCODE data.

Mark B Gerstein1,2,3, Anshul Kundaje4, Manoj Hariharan5, Stephen G Landt5, Koon-Kiu Yan1,2, Chao Cheng1,2, Xinmeng Jasmine Mu1, Ekta Khurana1,2, Joel Rozowsky2, Roger Alexander1,2, Renqiang Min1,2,6, Pedro Alves1, Alexej Abyzov1,2, Nick Addleman5, Nitin Bhardwaj1,2, Alan P Boyle5, Philip Cayting5, Alexandra Charos7, David Z Chen2, Yong Cheng5, Declan Clarke8, Catharine Eastman5, Ghia Euskirchen5, Seth Frietze9, Yao Fu1, Jason Gertz10, Fabian Grubert5, Arif Harmanci1,2, Preti Jain10, Maya Kasowski5, Phil Lacroute5, Jing Jane Leng1, Jin Lian11, Hannah Monahan7, Henriette O'Geen12, Zhengqing Ouyang5, E Christopher Partridge10, Dorrelyn Patacsil5, Florencia Pauli10, Debasish Raha7, Lucia Ramirez5, Timothy E Reddy10, Brian Reed7, Minyi Shi5, Teri Slifer5, Jing Wang1, Linfeng Wu5, Xinqiong Yang5, Kevin Y Yip1,2,13, Gili Zilberman-Schapira1, Serafim Batzoglou4, Arend Sidow14, Peggy J Farnham9, Richard M Myers10, Sherman M Weissman11, Michael Snyder5.   

Abstract

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

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Year:  2012        PMID: 22955619      PMCID: PMC4154057          DOI: 10.1038/nature11245

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  46 in total

1.  Chromatin structure analyses identify miRNA promoters.

Authors:  Fatih Ozsolak; Laura L Poling; Zhengxin Wang; Hui Liu; X Shirley Liu; Robert G Roeder; Xinmin Zhang; Jun S Song; David E Fisher
Journal:  Genes Dev       Date:  2008-11-15       Impact factor: 11.361

2.  Chromatin poises miRNA- and protein-coding genes for expression.

Authors:  Artem Barski; Raja Jothi; Suresh Cuddapah; Kairong Cui; Tae-Young Roh; Dustin E Schones; Keji Zhao
Journal:  Genome Res       Date:  2009-08-27       Impact factor: 9.043

3.  Heritable individual-specific and allele-specific chromatin signatures in humans.

Authors:  Ryan McDaniell; Bum-Kyu Lee; Lingyun Song; Zheng Liu; Alan P Boyle; Michael R Erdos; Laura J Scott; Mario A Morken; Katerina S Kucera; Anna Battenhouse; Damian Keefe; Francis S Collins; Huntington F Willard; Jason D Lieb; Terrence S Furey; Gregory E Crawford; Vishwanath R Iyer; Ewan Birney
Journal:  Science       Date:  2010-03-18       Impact factor: 47.728

4.  Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels.

Authors:  Nitin Bhardwaj; Koon-Kiu Yan; Mark B Gerstein
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-29       Impact factor: 11.205

5.  A map of human genome variation from population-scale sequencing.

Authors:  Gonçalo R Abecasis; David Altshuler; Adam Auton; Lisa D Brooks; Richard M Durbin; Richard A Gibbs; Matt E Hurles; Gil A McVean
Journal:  Nature       Date:  2010-10-28       Impact factor: 49.962

Review 6.  Transcriptional regulatory circuits: predicting numbers from alphabets.

Authors:  Harold D Kim; Tal Shay; Erin K O'Shea; Aviv Regev
Journal:  Science       Date:  2009-07-24       Impact factor: 47.728

Review 7.  A census of human transcription factors: function, expression and evolution.

Authors:  Juan M Vaquerizas; Sarah K Kummerfeld; Sarah A Teichmann; Nicholas M Luscombe
Journal:  Nat Rev Genet       Date:  2009-04       Impact factor: 53.242

8.  An atlas of combinatorial transcriptional regulation in mouse and man.

Authors:  Timothy Ravasi; Harukazu Suzuki; Carlo Vittorio Cannistraci; Shintaro Katayama; Vladimir B Bajic; Kai Tan; Altuna Akalin; Sebastian Schmeier; Mutsumi Kanamori-Katayama; Nicolas Bertin; Piero Carninci; Carsten O Daub; Alistair R R Forrest; Julian Gough; Sean Grimmond; Jung-Hoon Han; Takehiro Hashimoto; Winston Hide; Oliver Hofmann; Atanas Kamburov; Mandeep Kaur; Hideya Kawaji; Atsutaka Kubosaki; Timo Lassmann; Erik van Nimwegen; Cameron Ross MacPherson; Chihiro Ogawa; Aleksandar Radovanovic; Ariel Schwartz; Rohan D Teasdale; Jesper Tegnér; Boris Lenhard; Sarah A Teichmann; Takahiro Arakawa; Noriko Ninomiya; Kayoko Murakami; Michihira Tagami; Shiro Fukuda; Kengo Imamura; Chikatoshi Kai; Ryoko Ishihara; Yayoi Kitazume; Jun Kawai; David A Hume; Trey Ideker; Yoshihide Hayashizaki
Journal:  Cell       Date:  2010-03-05       Impact factor: 41.582

9.  Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture.

Authors:  Raja Jothi; S Balaji; Arthur Wuster; Joshua A Grochow; Jörg Gsponer; Teresa M Przytycka; L Aravind; M Madan Babu
Journal:  Mol Syst Biol       Date:  2009-08-18       Impact factor: 11.429

10.  Genome-wide occupancy of SREBP1 and its partners NFY and SP1 reveals novel functional roles and combinatorial regulation of distinct classes of genes.

Authors:  Brian D Reed; Alexandra E Charos; Anna M Szekely; Sherman M Weissman; Michael Snyder
Journal:  PLoS Genet       Date:  2008-07-25       Impact factor: 5.917

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  722 in total

1.  Subgroups at high risk for ischaemic heart disease:identification and validation in 67 000 individuals from the general population.

Authors:  Ruth Frikke-Schmidt; Anne Tybjærg-Hansen; Greg Dyson; Christiane L Haase; Marianne Benn; Børge G Nordestgaard; Charles F Sing
Journal:  Int J Epidemiol       Date:  2014-10-30       Impact factor: 7.196

2.  Transcription factors, coregulators, and epigenetic marks are linearly correlated and highly redundant.

Authors:  Tobias Ahsendorf; Franz-Josef Müller; Ved Topkar; Jeremy Gunawardena; Roland Eils
Journal:  PLoS One       Date:  2017-12-07       Impact factor: 3.240

3.  Robust Identification of Developmentally Active Endothelial Enhancers in Zebrafish Using FANS-Assisted ATAC-Seq.

Authors:  Aurelie Quillien; Mary Abdalla; Jun Yu; Jianhong Ou; Lihua Julie Zhu; Nathan D Lawson
Journal:  Cell Rep       Date:  2017-07-18       Impact factor: 9.423

Review 4.  Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.

Authors:  C Gaiteri; Y Ding; B French; G C Tseng; E Sibille
Journal:  Genes Brain Behav       Date:  2013-12-10       Impact factor: 3.449

Review 5.  Practicing pathology in the era of big data and personalized medicine.

Authors:  Jiang Gu; Clive R Taylor
Journal:  Appl Immunohistochem Mol Morphol       Date:  2014-01

6.  Tools and best practices for data processing in allelic expression analysis.

Authors:  Stephane E Castel; Ami Levy-Moonshine; Pejman Mohammadi; Eric Banks; Tuuli Lappalainen
Journal:  Genome Biol       Date:  2015-09-17       Impact factor: 13.583

7.  Novel identification of STAT1 as a crucial mediator of ETV6-NTRK3-induced tumorigenesis.

Authors:  Jinah Park; Junil Kim; Bora Park; Kyung-Min Yang; Eun Jin Sun; Cristina E Tognon; Poul H Sorensen; Seong-Jin Kim
Journal:  Oncogene       Date:  2018-02-02       Impact factor: 9.867

Review 8.  Pathways to disease from natural variations in human cytoplasmic tRNAs.

Authors:  Jeremy T Lant; Matthew D Berg; Ilka U Heinemann; Christopher J Brandl; Patrick O'Donoghue
Journal:  J Biol Chem       Date:  2019-01-14       Impact factor: 5.157

Review 9.  Synthetic biology in mammalian cells: next generation research tools and therapeutics.

Authors:  Florian Lienert; Jason J Lohmueller; Abhishek Garg; Pamela A Silver
Journal:  Nat Rev Mol Cell Biol       Date:  2014-01-17       Impact factor: 94.444

Review 10.  Using the ENCODE Resource for Functional Annotation of Genetic Variants.

Authors:  Michael J Pazin
Journal:  Cold Spring Harb Protoc       Date:  2015-03-11
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