title: Goldmine integrates information placing genomic ranges into meaningful biological contexts.
abstract: Bioinformatic analysis often produces large sets of genomic ranges that can be difficult to interpret in the absence of genomic context. Goldmine annotates genomic ranges from any source with gene model and feature contexts to facilitate global descriptions and candidate loci discovery. We demonstrate the value of genomic context by using Goldmine to elucidate context dynamics in transcription factor binding and to reveal differentially methylated regions (DMRs) with context-specific functional correlations. The open source R package and documentation for Goldmine are available at http://jeffbhasin.github.io/goldmine.
authors.affiliation: Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Bhasin
authors.affiliation: Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA tinga@ccf.org.
authors.forename: Angela H
authors.initials: AH
authors.lastname: Ting
mesh_terms: D019295:Computational Biology / Q000379:methods*; D019175:DNA Methylation / Q000235:genetics*; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D058977:Molecular Sequence Annotation / Q000379:methods; D011485:Protein Binding / Q000235:genetics; D012984:Software