A pipeline for automated annotation of yeast genome sequences by a conserved-synteny approach

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Abstract

Background: Yeasts are a model system for exploring eukaryotic genome evolution. Next-generation sequencing technologies are poised to vastly increase the number of yeast genome sequences, both from resequencing projects (population studies) and from de novo sequencing projects (new species). However, the annotation of genomes presents a major bottleneck for de novo projects, because it still relies on a process that is largely manual.Results: Here we present the Yeast Genome Annotation Pipeline (YGAP), an automated system designed specifically for new yeast genome sequences lacking transcriptome data. YGAP does automatic de novo annotation, exploiting homology and synteny information from other yeast species stored in the Yeast Gene Order Browser (YGOB) database. The basic premises underlying YGAP's approach are that data from other species already tells us what genes we should expect to find in any particular genomic region and that we should also expect that orthologous genes are likely to have similar intron/exon structures. Additionally, it is able to detect probable frameshift sequencing errors and can propose corrections for them. YGAP searches intelligently for introns, and detects tRNA genes and Ty-like elements.Conclusions: In tests on Saccharomyces cerevisiae and on the genomes of Naumovozyma castellii and Tetrapisispora blattae newly sequenced with Roche-454 technology, YGAP outperformed another popular annotation program (AUGUSTUS). For S. cerevisiae and N. castellii, 91-93% of YGAP's predicted gene structures were identical to those in previous manually curated gene sets. YGAP has been implemented as a webserver with a user-friendly interface at http://wolfe.gen.tcd.ie/annotation. © 2012 Proux-Wéra et al.; licensee BioMed Central Ltd.

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APA

Proux-Wéra, E., Armisén, D., Byrne, K. P., & Wolfe, K. H. (2012). A pipeline for automated annotation of yeast genome sequences by a conserved-synteny approach. BMC Bioinformatics, 13(1). https://doi.org/10.1186/1471-2105-13-237

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