Abstract:
BACKGROUND:Comparative genomics has become an essential approach for identifying homologous gene candidates and their functions, and for studying genome evolution. There are many tools available for genome comparisons. Unfortunately, most of them are not applicable for the identification of unique genes and the inference of phylogenetic relationships in a given set of genomes. RESULTS:GenomeBlast is a Web tool developed for comparative analysis of multiple small genomes. A new parameter called "coverage" was introduced and used along with sequence identity to evaluate global similarity between genes. With GenomeBlast, the following results can be obtained: (1) unique genes in each genome; (2) homologous gene candidates among compared genomes; (3) 2D plots of homologous gene candidates along the all pairwise genome comparisons; and (4) a table of gene presence/absence information and a genome phylogeny. We demonstrated the functions in GenomeBlast with an example of multiple herpesviral genome analysis and illustrated how GenomeBlast is useful for small genome comparison. CONCLUSION:We developed a Web tool for comparative analysis of small genomes, which allows the user not only to identify unique genes and homologous gene candidates among multiple genomes, but also to view their graphical distributions on genomes, and to reconstruct genome phylogeny. GenomeBlast runs on a Linux server with 4 CPUs and 4 GB memory. The online version of GenomeBlast is available to public by using a Web browser with the URL http://bioinfo-srv1.awh.unomaha.edu/genomeblast/.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Lu G,Jiang L,Helikar RM,Rowley TW,Zhang L,Chen X,Moriyama ENdoi
10.1186/1471-2105-7-S4-S18subject
Has Abstractpub_date
2006-12-12 00:00:00pages
S18issn
1471-2105pii
1471-2105-7-S4-S18journal_volume
7 Suppl 4pub_type
杂志文章abstract:BACKGROUND:Identifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the process of identifying unknown interactions between drugs and target proteins. In recent studies, handcrafted features, similarity metrics and machine learning methods have...
journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2020-07-06 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2006-05-18 00:00:00
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pub_type: 杂志文章
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更新日期:2012-01-01 00:00:00
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更新日期:2016-10-06 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2014-07-11 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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pub_type: 杂志文章
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更新日期:2016-01-11 00:00:00
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pub_type: 杂志文章
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更新日期:2018-06-27 00:00:00
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journal_title:BMC bioinformatics
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pub_type: 杂志文章
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pub_type: 杂志文章
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更新日期:2008-01-30 00:00:00
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更新日期:2014-12-10 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2010-09-29 00:00:00
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pub_type: 杂志文章
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更新日期:2010-04-29 00:00:00
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更新日期:2004-05-07 00:00:00
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pub_type: 杂志文章
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更新日期:2004-01-21 00:00:00
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pub_type: 杂志文章
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更新日期:2013-03-22 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2012-01-01 00:00:00
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更新日期:2019-07-31 00:00:00
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journal_title:BMC bioinformatics
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更新日期:2010-09-23 00:00:00
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journal_title:BMC bioinformatics
pub_type: 杂志文章
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更新日期:2007-11-01 00:00:00
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更新日期:2018-12-28 00:00:00