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BMC Genomics. 2015 Mar 11;16:170. doi: 10.1186/s12864-015-1344-4.

CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

Author information

1
Centre for Crop and Disease Management, Department of Environment and Agriculture, School of Science, Curtin University, Bentley, WA, 6102, Australia. 13392554@student.curtin.edu.au.
2
Postal address: Department of Environment and Agriculture Centre for Crop and Disease Management, GPO Box U1987, Perth, 6845, Western Australia. 13392554@student.curtin.edu.au.
3
Centre for Crop and Disease Management, Department of Environment and Agriculture, School of Science, Curtin University, Bentley, WA, 6102, Australia. james.hane@curtin.edu.au.
4
Centre for Crop and Disease Management, Department of Environment and Agriculture, School of Science, Curtin University, Bentley, WA, 6102, Australia. simon.ellwood@curtin.edu.au.
5
Centre for Crop and Disease Management, Department of Environment and Agriculture, School of Science, Curtin University, Bentley, WA, 6102, Australia. richard.oliver@curtin.edu.au.

Abstract

BACKGROUND:

The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study.

RESULTS:

CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes.

CONCLUSIONS:

We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available ( https://sourceforge.net/projects/codingquarry/ ), and suitable for incorporation into genome annotation pipelines.

PMID:
25887563
PMCID:
PMC4363200
DOI:
10.1186/s12864-015-1344-4
[Indexed for MEDLINE]
Free PMC Article

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