• DocumentCode
    478744
  • Title

    Combining Comparative Genomics with de novo Motif Discovery to Identify Human Transcription Factor DNA-Binding Motifs

  • Author

    Mao, Linyong ; Zheng, W. Jim

  • Author_Institution
    Dept. of Biostat., Med. Univ. of South Carolina, Charleston, SC
  • Volume
    1
  • fYear
    2006
  • fDate
    20-24 June 2006
  • Firstpage
    158
  • Lastpage
    163
  • Abstract
    As more and more genomes are sequenced, comparative genomics approaches provide a methodology for identifying conserved regulatory elements that may be involved in gene regulation. In this study, we combined comparative genomics with de novo motif discovery to identify potential human transcription factor binding motifs that are overrepresented and conserved in the upstream regions of a set of co-regulated genes. We validated our approach by analyzing a well-characterized muscle specific gene set. Our approach also performed better than other existing programs, such as Toucan and Compare Prospector, based on the motif discovery results for the muscle data set
  • Keywords
    DNA; biology computing; data mining; genetics; muscle; de novo motif discovery algorithm; gene regulation; genomics; human transcription factor DNA-binding motifs; muscle data set; muscle specific gene set; Bioinformatics; Cancer; DNA; Genomics; Heuristic algorithms; Humans; Mice; Muscles; Sampling methods; Sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on
  • Conference_Location
    Hanzhou, Zhejiang
  • Print_ISBN
    0-7695-2581-4
  • Type

    conf

  • DOI
    10.1109/IMSCCS.2006.47
  • Filename
    4673540