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  • 标题:EST-PAC a web package for EST annotation and protein sequence prediction
  • 本地全文:下载
  • 作者:Yvan Strahm ; David Powell ; Christophe Lefèvre
  • 期刊名称:Source Code for Biology and Medicine
  • 印刷版ISSN:1751-0473
  • 电子版ISSN:1751-0473
  • 出版年度:2006
  • 卷号:1
  • 期号:1
  • 页码:2
  • DOI:10.1186/1751-0473-1-2
  • 语种:English
  • 出版社:BioMed Central
  • 摘要:With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics.
  • 关键词:Annotation Task ; Hide Markov Model Approach ; Predict Translation Product ; Predict Protein Code Sequence ; Extensive Data Mining
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