首页 | 本学科首页   官方微博 | 高级检索  
     


GenBlastA: enabling BLAST to identify homologous gene sequences
Authors:She Rong  Chu Jeffrey S-C  Wang Ke  Pei Jian  Chen Nansheng
Affiliation:School of Computing Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6 Canada.
Abstract:
BLAST is an extensively used local similarity search tool for identifying homologous sequences. When a gene sequence (either protein sequence or nucleotide sequence) is used as a query to search for homologous sequences in a genome, the search results, represented as a list of high-scoring pairs (HSPs), are fragments of candidate genes rather than full-length candidate genes. Relevant HSPs ("signals"), which represent candidate genes in the target genome sequences, are buried within a report that contains also hundreds to thousands of random HSPs ("noises"). Consequently, BLAST results are often overwhelming and confusing even to experienced users. For effective use of BLAST, a program is needed for extracting relevant HSPs that represent candidate homologous genes from the entire HSP report. To achieve this goal, we have designed a graph-based algorithm, genBlastA, which automatically filters HSPs into well-defined groups, each representing a candidate gene in the target genome. The novelty of genBlastA is an edge length metric that reflects a set of biologically motivated requirements so that each shortest path corresponds to an HSP group representing a homologous gene. We have demonstrated that this novel algorithm is both efficient and accurate for identifying homologous sequences, and that it outperforms existing approaches with similar functionalities.
Keywords:
本文献已被 PubMed 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号