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


CONCLUSIONS
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.
Aggarwal
Gautam
G
Seattle Biomedical Research Institute 4 Nickerson Street, Seattle, WA 98109, USA. gaggarwal@sbri.org
Worthey
E A
EA
McDonagh
Paul D
PD
Myler
Peter J
PJ
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data* / Q000639:trends; D017125:Genes, Protozoan; D018503:Genome, Protozoan; D018320:Leishmania major / Q000235:genetics*; D015233:Models, Statistical; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D011381:Programming Languages; D015800:Protozoan Proteins / Q000235:genetics*
D015800:Protozoan Proteins