Evidence-based gene predictions in plant genomes
- 1 Cold Spring Harbor Laboratory;
- 2 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences;
- 3 Cold Spring Harbor Laboratory / USDA
- E-mail: liang{at}cshl.edu
Abstract
Automated evidence-based gene building is a rapid and cost-effective way to provide reliable gene annotations on newly sequenced genomes. One of the limitations of evidence-based gene builders, however, is their requirement for transcriptional evidence - known proteins, full-length cDNAs, or ESTs - in the species of interest. This limitation is of particular concern for plant genomes, where the rate of genome sequencing is greatly outpacing the rate of EST- and cDNA-sequencing projects. To overcome this limitation, we have developed an evidence-based gene build system (the Gramene pipeline) that can use transcriptional evidence across related species. The Gramene pipeline uses the Ensembl computing infrastructure with a novel data processing scheme. Using the previously annotated plant genomes, the dicot Arabidopsis thaliana and the monocot Oryza sativa, we show that the cross-species ESTs from within monocot or dicot class are a valuable source of evidence for gene predictions. We also find that, using only EST and cross-species evidence, the Gramene pipeline can generate a plant gene set that is comparable in quality to the human genes based on known proteins and full-length cDNAs. We compare the Gramene pipeline to several widely used ab initio gene prediction programs in rice; this comparison shows the pipeline performs favorably at both the gene and exon levels with cross-species gene products only. We discuss the results of testing the pipeline on a 22-Mb region of the newly sequenced maize genome and potential application of the pipeline to other genomes.
Footnotes
-
- Received November 6, 2008.
- Accepted June 8, 2009.
- Copyright © 2009, Cold Spring Harbor Laboratory Press
