Literature DB >> 32051932

Poly-Enrich: count-based methods for gene set enrichment testing with genomic regions.

Christopher T Lee1, Raymond G Cavalcante2, Chee Lee2, Tingting Qin2, Snehal Patil2, Shuze Wang2, Zing T Y Tsai2, Alan P Boyle2, Maureen A Sartor1,2.   

Abstract

Gene set enrichment (GSE) testing enhances the biological interpretation of ChIP-seq data and other large sets of genomic regions. Our group has previously introduced two GSE methods for genomic regions: ChIP-Enrich for narrow regions and Broad-Enrich for broad regions. Here, we introduce Poly-Enrich, which has wider applicability, additional capabilities and models the number of peaks assigned to a gene using a generalized additive model with a negative binomial family to determine gene set enrichment, while adjusting for gene locus length. As opposed to ChIP-Enrich, Poly-Enrich works well even when nearly all genes have a peak, illustrated by using Poly-Enrich to characterize pathways and types of genic regions enriched with different families of repetitive elements. By comparing Poly-Enrich and ChIP-Enrich results with ENCODE ChIP-seq data, we found that the optimal test depends more on the pathway being regulated than on properties of the transcription factors. Using known transcription factor functions, we discovered clusters of related biological processes consistently better modeled with Poly-Enrich. This suggests that the regulation of certain processes may be modified by multiple binding events, better modeled by a count-based method. Our new hybrid method automatically uses the optimal method for each gene set, with correct FDR-adjustment.
© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

Entities:  

Year:  2020        PMID: 32051932      PMCID: PMC7003681          DOI: 10.1093/nargab/lqaa006

Source DB:  PubMed          Journal:  NAR Genom Bioinform        ISSN: 2631-9268


  28 in total

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Authors:  Fan Hsu; W James Kent; Hiram Clawson; Robert M Kuhn; Mark Diekhans; David Haussler
Journal:  Bioinformatics       Date:  2006-02-24       Impact factor: 6.937

4.  GREAT improves functional interpretation of cis-regulatory regions.

Authors:  Cory Y McLean; Dave Bristor; Michael Hiller; Shoa L Clarke; Bruce T Schaar; Craig B Lowe; Aaron M Wenger; Gill Bejerano
Journal:  Nat Biotechnol       Date:  2010-05-02       Impact factor: 54.908

5.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

6.  The Molecular Signatures Database (MSigDB) hallmark gene set collection.

Authors:  Arthur Liberzon; Chet Birger; Helga Thorvaldsdóttir; Mahmoud Ghandi; Jill P Mesirov; Pablo Tamayo
Journal:  Cell Syst       Date:  2015-12-23       Impact factor: 10.304

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

Authors:  Ryan P Welch; Chee Lee; Paul M Imbriano; Snehal Patil; Terry E Weymouth; R Alex Smith; Laura J Scott; Maureen A Sartor
Journal:  Nucleic Acids Res       Date:  2014-05-30       Impact factor: 16.971

Review 8.  Enhancers: five essential questions.

Authors:  Len A Pennacchio; Wendy Bickmore; Ann Dean; Marcelo A Nobrega; Gill Bejerano
Journal:  Nat Rev Genet       Date:  2013-04       Impact factor: 53.242

9.  Mobile elements in the human genome: implications for disease.

Authors:  Szilvia Solyom; Haig H Kazazian
Journal:  Genome Med       Date:  2012-02-24       Impact factor: 11.117

10.  KEGG: new perspectives on genomes, pathways, diseases and drugs.

Authors:  Minoru Kanehisa; Miho Furumichi; Mao Tanabe; Yoko Sato; Kanae Morishima
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  6 in total

1.  Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data.

Authors:  Tingting Qin; Christopher Lee; Shiting Li; Raymond G Cavalcante; Peter Orchard; Heming Yao; Hanrui Zhang; Shuze Wang; Snehal Patil; Alan P Boyle; Maureen A Sartor
Journal:  Genome Biol       Date:  2022-04-26       Impact factor: 17.906

2.  Tissue and sex-specific programming of DNA methylation by perinatal lead exposure: implications for environmental epigenetics studies.

Authors:  Laurie K Svoboda; Kari Neier; Kai Wang; Raymond G Cavalcante; Christine A Rygiel; Zing Tsai; Tamara R Jones; Siyu Liu; Jaclyn M Goodrich; Claudia Lalancette; Justin A Colacino; Maureen A Sartor; Dana C Dolinoy
Journal:  Epigenetics       Date:  2020-11-08       Impact factor: 4.528

3.  Sex-Specific Alterations in Cardiac DNA Methylation in Adult Mice by Perinatal Lead Exposure.

Authors:  Laurie K Svoboda; Kai Wang; Tamara R Jones; Justin A Colacino; Maureen A Sartor; Dana C Dolinoy
Journal:  Int J Environ Res Public Health       Date:  2021-01-12       Impact factor: 4.614

4.  Nine quick tips for pathway enrichment analysis.

Authors:  Davide Chicco; Giuseppe Agapito
Journal:  PLoS Comput Biol       Date:  2022-08-11       Impact factor: 4.779

5.  Testing Proximity of Genomic Regions to Transcription Start Sites and Enhancers Complements Gene Set Enrichment Testing.

Authors:  Christopher Lee; Kai Wang; Tingting Qin; Maureen A Sartor
Journal:  Front Genet       Date:  2020-03-06       Impact factor: 4.599

6.  Sex-Specific Programming of Cardiac DNA Methylation by Developmental Phthalate Exposure.

Authors:  Laurie K Svoboda; Kai Wang; Raymond G Cavalcante; Kari Neier; Justin A Colacino; Maureen A Sartor; Dana C Dolinoy
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  6 in total

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