Abstract
BackgroundAutomated gene-calling is still an error-prone process, particularly for the highly plastic genomes of fungal species. Improvement through quality control and manual curation of gene models is a time-consuming process that requires skilled biologists and is only marginally performed. The wealth of available fungal genomes has not yet been exploited by an automated method that applies quality control of gene models in order to obtain more accurate genome annotations.ResultsWe provide a novel method named alignment-based fungal gene prediction (ABFGP) that is particularly suitable for plastic genomes like those of fungi. It can assess gene models on a gene-by-gene basis making use of informant gene loci. Its performance was benchmarked on 6,965 gene models confirmed by full-length unigenes from ten different fungi. 79.4% of all gene models were correctly predicted by ABFGP. It improves the output of ab initio gene prediction software due to a higher sensitivity and precision for all gene model components. Applicability of the method was shown by revisiting the annotations of six different fungi, using gene loci from up to 29 fungal genomes as informants. Between 7,231 and 8,337 genes were assessed by ABFGP and for each genome between 1,724 and 3,505 gene model revisions were proposed. The reliability of the proposed gene models is assessed by an a posteriori introspection procedure of each intron and exon in the multiple gene model alignment. The total number and type of proposed gene model revisions in the six fungal genomes is correlated to the quality of the genome assembly, and to sequencing strategies used in the sequencing centre, highlighting different types of errors in different annotation pipelines. The ABFGP method is particularly successful in discovering sequence errors and/or disruptive mutations causing truncated and erroneous gene models.ConclusionsThe ABFGP method is an accurate and fully automated quality control method for fungal gene catalogues that can be easily implemented into existing annotation pipelines. With the exponential release of new genomes, the ABFGP method will help decreasing the number of gene models that require additional manual curation.
Highlights
Automated gene-calling is still an error-prone process, for the highly plastic genomes of fungal species
GeneMark-ES was chosen as a state of the art ab initio gene predictor, and we have shown that the alignment-based fungal gene prediction (ABFGP) method improves the quality of the gene models
The ABFGP method is a useful tool to integrate into existing gene annotation pipelines because it can assess and improve gene models with great accuracy in a fully automated manner
Summary
Automated gene-calling is still an error-prone process, for the highly plastic genomes of fungal species. Genomes are annotated through an automated genecalling pipeline, which is still an error-prone process, Most gene annotation pipelines integrate different gene prediction algorithms to increase the accuracy of the annotation [3]. These algorithms include ab initio supervised, ab initio unsupervised and (supervised) alignment-based gene predictors, which are implemented in tools such as Augustus [4], GeneMark-ES [5] and TWINSCAN 2.0α [6], respectively. It “often requires significant effort in implementation to cast comparative information into a form compatible with the existing gene models” [13]
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