ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Author(s)

Silva, Tiago C. ^{[1, 2]} ; Coetzee, Simon G. ^[2] ; Gull, Nicole ^[2] ; Yao, Lijing ^[3] ; Hazelett, Dennis J. ^[2] ; Noushmehr, Houtan ^{[1, 4]} ; Lin, De-Chen ^[5] ; Berman, Benjamin R. ^{[2, 5]}

Affiliation

^[1] Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Genet, Ribeirao Preto - Brazil

^[2] Cedars Sinai Med Ctr, Ctr Bioinformat & Funct Genom, Dept Biomed Sci, Los Angeles, CA 90048 - USA

^[3] Bioinformat Res & Early Dev Roche Sequencing Solu, Belmont, CA - USA

^[4] Henry Ford Hosp, Dept Neurosurg, Detroit, MI 48202 - USA

^[5] Cedars Sinai Med Ctr, Dept Med, Los Angeles, CA 90048 - USA

• Document type: Journal article
• Source: Bioinformatics; v. 35, n. 11, p. 1974-1977, JUN 1 2019.
• Web of Science Citations: 5

Abstract

Motivation: DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. Results: We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use predefined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. (AU)

• FAPESP's process: 16/01389-7 - Bioinformatic tool to integrate and understand aberrant epigenomic and genomic changes associated with cancer: methods, development and analysis
• Grantee: Tiago Chedraoui Silva
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 15/07925-5 - Open source software statistical tools to aid in analyzing and integrating large cancer epigenomic datasets in order to decipher and understand regulatory networks
• Grantee: Houtan Noushmehr
• Support Opportunities: Research Grants - Young Investigators Grants