Literature DB >> 24878920

ChIP-Enrich: gene set enrichment testing for ChIP-seq data.

Ryan P Welch1, Chee Lee2, Paul M Imbriano3, Snehal Patil2, Terry E Weymouth4, R Alex Smith2, Laura J Scott5, Maureen A Sartor6.   

Abstract

Gene set enrichment testing can enhance the biological interpretation of ChIP-seq data. Here, we develop a method, ChIP-Enrich, for this analysis which empirically adjusts for gene locus length (the length of the gene body and its surrounding non-coding sequence). Adjustment for gene locus length is necessary because it is often positively associated with the presence of one or more peaks and because many biologically defined gene sets have an excess of genes with longer or shorter gene locus lengths. Unlike alternative methods, ChIP-Enrich can account for the wide range of gene locus length-to-peak presence relationships (observed in ENCODE ChIP-seq data sets). We show that ChIP-Enrich has a well-calibrated type I error rate using permuted ENCODE ChIP-seq data sets; in contrast, two commonly used gene set enrichment methods, Fisher's exact test and the binomial test implemented in Genomic Regions Enrichment of Annotations Tool (GREAT), can have highly inflated type I error rates and biases in ranking. We identify DNA-binding proteins, including CTCF, JunD and glucocorticoid receptor α (GRα), that show different enrichment patterns for peaks closer to versus further from transcription start sites. We also identify known and potential new biological functions of GRα. ChIP-Enrich is available as a web interface (http://chip-enrich.med.umich.edu) and Bioconductor package.
© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entities:  

Mesh:

Substances:

Year:  2014        PMID: 24878920      PMCID: PMC4117744          DOI: 10.1093/nar/gku463

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  45 in total

1.  LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data.

Authors:  Maureen A Sartor; George D Leikauf; Mario Medvedovic
Journal:  Bioinformatics       Date:  2008-11-27       Impact factor: 6.937

2.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nat Protoc       Date:  2009       Impact factor: 13.491

3.  Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder.

Authors:  Peter Holmans; Elaine K Green; Jaspreet Singh Pahwa; Manuel A R Ferreira; Shaun M Purcell; Pamela Sklar; Michael J Owen; Michael C O'Donovan; Nick Craddock
Journal:  Am J Hum Genet       Date:  2009-06-18       Impact factor: 11.025

4.  EBV Immortalization of human B lymphocytes separated from small volumes of cryo-preserved whole blood.

Authors:  M M Amoli; D Carthy; H Platt; W E R Ollier
Journal:  Int J Epidemiol       Date:  2008-04       Impact factor: 7.196

5.  Direct effect of glucocorticoids on lipolysis in adipocytes.

Authors:  Chong Xu; Jinhan He; Hongfeng Jiang; Luxia Zu; Wenjie Zhai; Shenshen Pu; Guoheng Xu
Journal:  Mol Endocrinol       Date:  2009-05-14

6.  Fluocinolone inhibits VEGF expression via glucocorticoid receptor in human retinal pigment epithelial (ARPE-19) cells and TNF-alpha-induced angiogenesis in chick chorioallantoic membrane (CAM).

Authors:  Surya P Ayalasomayajula; Paul Ashton; Uday B Kompella
Journal:  J Ocul Pharmacol Ther       Date:  2009-04       Impact factor: 2.671

7.  Gene set control analysis predicts hematopoietic control mechanisms from genome-wide transcription factor binding data.

Authors:  Anagha Joshi; Rebecca Hannah; Evangelia Diamanti; Berthold Göttgens
Journal:  Exp Hematol       Date:  2012-12-04       Impact factor: 3.084

8.  Variable locus length in the human genome leads to ascertainment bias in functional inference for non-coding elements.

Authors:  Leila Taher; Ivan Ovcharenko
Journal:  Bioinformatics       Date:  2009-01-25       Impact factor: 6.937

9.  Model-based analysis of ChIP-Seq (MACS).

Authors:  Yong Zhang; Tao Liu; Clifford A Meyer; Jérôme Eeckhoute; David S Johnson; Bradley E Bernstein; Chad Nusbaum; Richard M Myers; Myles Brown; Wei Li; X Shirley Liu
Journal:  Genome Biol       Date:  2008-09-17       Impact factor: 13.583

10.  Design and analysis of ChIP-seq experiments for DNA-binding proteins.

Authors:  Peter V Kharchenko; Michael Y Tolstorukov; Peter J Park
Journal:  Nat Biotechnol       Date:  2008-11-16       Impact factor: 54.908

View more
  56 in total

1.  Full-length annotation with multistrategy RNA-seq uncovers transcriptional regulation of lncRNAs in cotton.

Authors:  Xiaomin Zheng; Yanjun Chen; Yifan Zhou; Keke Shi; Xiao Hu; Danyang Li; Hanzhe Ye; Yu Zhou; Kun Wang
Journal:  Plant Physiol       Date:  2021-02-25       Impact factor: 8.340

2.  A distinct epigenetic program underlies the 1;7 translocation in myelodysplastic syndromes.

Authors:  Anair Graciela Lema Fernandez; Barbara Crescenzi; Valentina Pierini; Valeria Di Battista; Gianluca Barba; Fabrizia Pellanera; Danika Di Giacomo; Giovanni Roti; Rocco Piazza; Emmalee R Adelman; Maria E Figueroa; Cristina Mecucci
Journal:  Leukemia       Date:  2019-03-28       Impact factor: 11.528

3.  Conserved and species-specific transcription factor co-binding patterns drive divergent gene regulation in human and mouse.

Authors:  Adam G Diehl; Alan P Boyle
Journal:  Nucleic Acids Res       Date:  2018-02-28       Impact factor: 16.971

4.  Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction.

Authors:  Andrew D Rouillard; Zichen Wang; Avi Ma'ayan
Journal:  Comput Biol Chem       Date:  2015-10       Impact factor: 2.877

5.  The transcriptional corepressor CBFA2T3 inhibits all-trans-retinoic acid-induced myeloid gene expression and differentiation in acute myeloid leukemia.

Authors:  Nickolas Steinauer; Chun Guo; Jinsong Zhang
Journal:  J Biol Chem       Date:  2020-05-20       Impact factor: 5.157

6.  Aging Human Hematopoietic Stem Cells Manifest Profound Epigenetic Reprogramming of Enhancers That May Predispose to Leukemia.

Authors:  Hsuan-Ting Huang; Alejandro Roisman; Emmalee R Adelman; André Olsson; Antonio Colaprico; Tingting Qin; R Coleman Lindsley; Rafael Bejar; Nathan Salomonis; H Leighton Grimes; Maria E Figueroa
Journal:  Cancer Discov       Date:  2019-05-13       Impact factor: 39.397

7.  Age-related epigenome-wide DNA methylation and hydroxymethylation in longitudinal mouse blood.

Authors:  Joseph Kochmanski; Elizabeth H Marchlewicz; Raymond G Cavalcante; Maureen A Sartor; Dana C Dolinoy
Journal:  Epigenetics       Date:  2018-08-23       Impact factor: 4.528

8.  Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.

Authors:  Raphaël Mourad; Olivier Cuvier
Journal:  PLoS Comput Biol       Date:  2016-05-20       Impact factor: 4.475

9.  Zinc Finger Homeodomain Factor Zfhx3 Is Essential for Mammary Lactogenic Differentiation by Maintaining Prolactin Signaling Activity.

Authors:  Dan Zhao; Gui Ma; Xiaolin Zhang; Yuan He; Mei Li; Xueying Han; Liya Fu; Xue-Yuan Dong; Tamas Nagy; Qiang Zhao; Li Fu; Jin-Tang Dong
Journal:  J Biol Chem       Date:  2016-04-20       Impact factor: 5.157

10.  A Mouse Model of X-linked Intellectual Disability Associated with Impaired Removal of Histone Methylation.

Authors:  Shigeki Iwase; Emily Brookes; Saurabh Agarwal; Aimee I Badeaux; Hikaru Ito; Christina N Vallianatos; Giulio Srubek Tomassy; Tomas Kasza; Grace Lin; Andrew Thompson; Lei Gu; Kenneth Y Kwan; Chinfei Chen; Maureen A Sartor; Brian Egan; Jun Xu; Yang Shi
Journal:  Cell Rep       Date:  2016-01-21       Impact factor: 9.423

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.