Research article

Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project

Gautam Aggarwal¹, EA Worthey¹, Paul D McDonagh² and Peter J Myler^1,3*

• * Corresponding author: Peter J Myler peter.myler@sbri.org

Author Affiliations

¹ Seattle Biomedical Research Institute 4 Nickerson Street, Seattle, WA 98109, USA

² Immunex Corporation, 51 University Street, Seattle, WA 98101, USA

³ Departments of Pathobiology and Medical Education and Biomedical Informatics, University of Washington, Seattle, WA 98195, USA

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BMC Bioinformatics 2003, 4:23 doi:10.1186/1471-2105-4-23

Published: 7 June 2003

Abstract

Background

Seattle Biomedical Research Institute (SBRI) as part of the Leishmania Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces.

Results

Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN) into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODONUSAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence.

Conclusion

An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.