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
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;
Conference_Titel :
Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on
Conference_Location :
Hanzhou, Zhejiang
Print_ISBN :
0-7695-2581-4
DOI :
10.1109/IMSCCS.2006.47