RNA-Seq greatly improves the accuracy of prediction of protein-coding genes

As more and more genomes are sequenced, genome annotation becomes increasingly important in bridging the gap between sequence and biology. Gene prediction, which is at the center of genome annotation, usually integrates various resources to compute consensus gene structures. However, many newly sequenced genomes have limited resources for gene predictions.

cucumberA group led by researchers at  Beijing Normal University set out to create high-quality gene models of the cucumber genome (Cucumis sativus var. sativus), based on the EVidenceModeler gene prediction pipeline. Using RNA-Seq reads of 10 cucumber tissues, they were able to reassemble the cucumber genome. They applied the new pipeline to the reassembled cucumber genome and included a comparison between their predicted protein-coding gene sets and a published set.

The reassembled cucumber genome, annotated with RNA-Seq reads from 10 tissues, has 23,248 identified protein-coding genes. Compared with the published prediction in 2009, approximately 8,700 genes reveal structural modifications and 5,285 genes only appear in the reassembled cucumber genome.

The comparison between the two gene sets also suggests that it is feasible to use RNA-Seq reads to annotate newly sequenced or less-studied genomes.

All the related results, including genome sequence and annotations, are available at http://cmb.bnu.edu.cn/Cucumis_sativus_v20/

  • Li Z, Zhang Z, Yan P, Huang S, Fei Z, Lin K. (2011) RNA-Seq improves annotation of protein-coding genes in the cucumber genome. BMC Genomics [Epub ahead of print]. [abstract]
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