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Comparative and Functional Genomics
Volume 5 (2004), Issue 3, Pages 276-280
http://dx.doi.org/10.1002/cfg.394
Conference review

New Computational Tools for Brassica Genome Research

1Plant Biotechnology Centre, Primary Industries Research Victoria, Department of Primary Industries, La Trobe University, Bundoora 3086, Victoria, Australia
2Victorian Bioinformatics Consortium, Plant Biotechnology Centre, La Trobe University, Bundoora 3086, Victoria, Australia

Received 26 January 2004; Accepted 6 February 2004

Copyright © 2004 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the increasing quantities of Brassica genomic data being entered into the public domain and in preparation for the complete Brassica genome sequencing effort, there is a growing requirement for the structuring and detailed bioinformatic analysis of Brassica genomic information within a user-friendly database. At the Plant Biotechnology Centre, Melbourne, Australia, we have developed a series of tools and computational pipelines to assist in the processing and structuring of genomic data, to aid its application to agricultural biotechnology research. These tools include a sequence database, ASTRA, a sequence processing pipeline incorporating annotation against GenBank, SwissProt and Arabidopsis Gene Ontology (GO) data and tools for molecular marker discovery and comparative genome analysis. All sequences are mined for simple sequence repeat (SSR) molecular markers using ‘SSR primer’ and mapped onto the complete Arabidopsis thaliana genome by sequence comparison. The database may be queried using a text-based search of sequence annotation or GO terms, BLAST comparison against resident sequences, or by the position of candidate orthologues within the Arabidopsis genome. Tools have also been developed and applied to the discovery of single nucleotide polymorphism (SNP) molecular markers and the in silico mapping of Brassica BAC end sequences onto the Arabidopsis genome. Planned extensions to this resource include the integration of gene expression data and the development of an EnsEMBL-based genome viewer.