Augmented Annotation of the Schizosaccharomyces pombe Genome Reveals Additional Genes Required for Growth and Viability

Author:

Bitton Danny A1,Wood Valerie2,Scutt Paul J1,Grallert Agnes3,Yates Tim1,Smith Duncan L4,Hagan Iain M3,Miller Crispin J1

Affiliation:

1. Cancer Research UK Applied Computational Biology and Bioinformatics Group

2. Cancer Research UK, London Research Institute, London WC2A 3PX, United Kingdom

3. Cancer Research UK Cell Division Group

4. Biological Mass Spectrometry Facility, Cancer Research UK, Paterson Institute for Cancer Research, The University of Manchester, Christie Hospital Site, Manchester M20 4BX, United Kingdom and

Abstract

Abstract 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.

Publisher

Oxford University Press (OUP)

Subject

Genetics

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