title: Evidence-based gene predictions in plant genomes.
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 expressed sequence tags (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 discuss potential application of the pipeline to other genomes.
authors: Cold Spring Harbor Laboratory, New York 11724, USA. liang@cshl.edu
authors: Chengzhi
authors: C
authors: Liang
authors: Long
authors: L
authors: Mao
authors: Doreen
authors: D
authors: Ware
authors: Lincoln
authors: L
authors: Stein
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D001937:Brassica / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods; D005544:Forecasting; D017343:Genes, Plant; D018745:Genome, Plant; D012275:Oryza / Q000235:genetics; D010940:Plant Proteins / Q000032:analysis / Q000235:genetics; D011786:Quality Control; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D013045:Species Specificity; D003313:Zea mays / Q000235:genetics
chemical_list: D010940:Plant Proteins