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Vol 13, Issue 1, 108-117, January 2003

METHODS

Comparative Gene Prediction in Human and Mouse

Genís Parra1, Pankaj Agarwal2, Josep F. Abril1, Thomas Wiehe3, James W. Fickett4 and Roderic Guigó1,5

1Grup de Recerca en Informàtica Biomèdica. Institut Municipal d'Investigació Medica / Universitat Pompeu Fabra / Centre de Regulació Genòmica 08003 Barcelona, Catalonia, Spain; 2GlaxoSmithKline, King of Prussia, Pennsylvania 19406, USA; 3Freie Universität Berlin and Berlin Center for Genome Based Bioinformatics (BCB), 14195 Berlin, Germany; 4AstraZeneca R&D Boston, Waltham, Massachusetts 02451, USA

The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.

5 Corresponding author.E-MAIL rguigo{at}imim.es; FAX 34 93 224-0875.

Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.871403.


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