Augmented annotation of the Schizosaccharomyces pombe genome reveals additional genes required for growth and viability.
Authors
Bitton, Danny AWood, V
Scutt, Paul J
Grallert, Agnes
Yates, Tim
Smith, Duncan L
Hagan, Iain M
Miller, Crispin J
Affiliation
Cancer Research UK Applied Computational Biology and Bioinformatics Group, University of Manchester, Christie Hospital Site, Manchester M20 4BX, United Kingdom.Issue Date
2011-04
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Show full item recordAbstract
Genome annotation is a synthesis of computational prediction and experimental evidence. Small genes are notoriously difficult to detect because the patterns used to identify them are often indistinguishable from chance occurrences, leading to an arbitrary cutoff threshold for the length of a protein-coding gene identified solely by in silico analysis. We report a systematic reappraisal of the Schizosaccharomyces pombe genome that ignores thresholds. A complete six-frame translation was compared to a proteome data set, the Pfam domain database, and the genomes of six other fungi. Thirty-nine novel loci were identified. RT-PCR and RNA-Seq confirmed transcription at 38 loci; 33 novel gene structures were delineated by 5' and 3' RACE. Expression levels of 14 transcripts fluctuated during meiosis. Translational evidence for 10 genes, evolutionary conservation data supporting 35 predictions, and distinct phenotypes upon ORF deletion (one essential, four slow-growth, two delayed-division phenotypes) suggest that all 39 predictions encode functional proteins. The popularity of S. pombe as a model organism suggests that this augmented annotation will be of interest in diverse areas of molecular and cellular biology, while the generality of the approach suggests widespread applicability to other genomes.Citation
Augmented annotation of the Schizosaccharomyces pombe genome reveals additional genes required for growth and viability. 2011, 187 (4):1207-17 GeneticsJournal
GeneticsDOI
10.1534/genetics.110.123497PubMed ID
21270388Type
ArticleLanguage
enISSN
1943-2631ae974a485f413a2113503eed53cd6c53
10.1534/genetics.110.123497
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