Faculty Collaboration Database

Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project. BMC Bioinformatics 2003 Jun 07;4:23

Date

06/10/2003

Pubmed ID

12793912

Pubmed Central ID

PMC165441

DOI

10.1186/1471-2105-4-23

Scopus ID

2-s2.0-0345181812 (requires institutional sign-in at Scopus site)   16 Citations

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.

Author List

Aggarwal G, Worthey EA, McDonagh PD, Myler PJ

MESH terms used to index this publication - Major topics in bold

Algorithms
Amino Acid Sequence
Animals
Base Sequence
Computational Biology
Genes, Protozoan
Genome, Protozoan
Leishmania major
Models, Statistical
Molecular Sequence Data
Predictive Value of Tests
Programming Languages
Protozoan Proteins