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

Abstract

BackgroundThe 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.ResultsCodingQuarry 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.ConclusionsWe 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.

The Complete Genome Sequence of the Phytopathogenic Fungus Sclerotinia sclerotiorum Reveals Insights into the Genome Architecture of Broad Host Range Pathogens

“…The following gene prediction software packages were used to generate predictions for the version two S. sclerotiorum genome: Augustus version 2.5.5 (Stanke and Morgenstern, 2005), Coding Quarry version 1.2 (Testa et al. 2015), and GeneMark-ES version 3.1 (Borodovsky and Lomsadze, 2011).…”

Genomic investigation of the strawberry pathogen Phytophthora fragariae indicates pathogenicity is associated with transcriptional variation in three key races

“…The P. fragariae 329 genome was shown to be highly repeat rich, with 38% of the assembly identified as repetitive or low 330 complexity, a larger value than the 29% shown for P. sojae (Armitage et al, 2018). A total of 37,049 331 genes encoding 37,346 proteins were predicted in the BC-16 assembly, consistening of 20,222 genes 332 predicted by BRAKER1 (Hoff et al, 2016) and 17,131 additional genes added from CodingQuarry 333 (Testa et al, 2015). From these gene models, 486 putative RxLR effectors, 82 putatitve crinkler 334 effectors (CRNs) and 1,274 putative apoplastic effectors were identified (Table 5).…”

Genome Sequence of the Fungal Strain 14919 Producing 3-Hydroxy-3-Methylglutaryl–Coenzyme A Reductase Inhibitor FR901512

“…A total of 14,342 coding sequences (CDSs) were predicted using CodingQuarry version 2.0 from the draft genome sequence, together with assembled transcripts (5). tRNA genes were predicted by tRNAscan-SE version 1.23 (6).…”