title: Open reading frames associated with cancer in the dark matter of the human genome.
abstract: BACKGROUND
  The uncharacterized proteins (open reading frames, ORFs) in the human genome offer an opportunity to discover novel targets for cancer. A systematic analysis of the dark matter of the human proteome for druggability and biomarker discovery is crucial to mining the genome. Numerous data mining tools are available to mine these ORFs to develop a comprehensive knowledge base for future target discovery and validation.
  
  
  METHODS
  Using the Genetic Association Database, the ORFs of the human dark matter proteome were screened for evidence of association with neoplasms. The Phenome-Genome Integrator tool was used to establish phenotypic association with disease traits including cancer. Batch analysis of the tools for protein expression analysis, gene ontology and motifs and domains was used to characterize the ORFs.
  
  
  RESULTS
  Sixty-two ORFs were identified for neoplasm association. The expression Quantitative Trait Loci (eQTL) analysis identified thirteen ORFs related to cancer traits. Protein expression, motifs and domain analysis and genome-wide association studies verified the relevance of these OncoORFs in diverse tumors. The OncoORFs are also associated with a wide variety of human diseases and disorders.
  
  
  CONCLUSIONS
  Our results link the OncoORFs to diverse diseases and disorders. This suggests a complex landscape of the uncharacterized proteome in human diseases. These results open the dark matter of the proteome to novel cancer target research.
authors.affiliation: Department of Biological Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, U.S.A.
authors.forename: Ana Paula
authors.initials: AP
authors.lastname: Delgado
authors.affiliation: Department of Biological Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, U.S.A.
authors.forename: Pamela
authors.initials: P
authors.lastname: Brandao
authors.affiliation: Department of Biological Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, U.S.A.
authors.forename: Maria Julia
authors.initials: MJ
authors.lastname: Chapado
authors.affiliation: Department of Biological Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, U.S.A.
authors.forename: Sheilin
authors.initials: S
authors.lastname: Hamid
authors.affiliation: Department of Biological Sciences, Charles E. Schmidt College of Science, Florida Atlantic University, Boca Raton, FL, U.S.A.
authors.forename: Ramaswamy
authors.initials: R
authors.lastname: Narayanan
mesh_terms: D020816:Amino Acid Motifs; D019295:Computational Biology; D030541:Databases, Genetic; D005260:Female; D056726:Genetic Association Studies; D015894:Genome, Human; D055106:Genome-Wide Association Study; D023281:Genomics; D006801:Humans; D008297:Male; D058977:Molecular Sequence Annotation; D009154:Mutation; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D016366:Open Reading Frames; D040901:Proteomics; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable
keywords: Phenome-genome; batch analysis; biomarkers; cancer association; druggable proteome; expression quantitative trait loci; genome-wide association; landscape of diseases; open reading frames; uncharacterized proteins