GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins. Issue 2 (13th May 2020)

• Record Type: Journal Article
• Authors: Brůna, Tomáš; Lomsadze, Alexandre; Borodovsky, Mark
• Is Part Of: NAR genomics and bioinformatics. Volume 2:Issue 2(2020)
• Publication Date: 2020-05-13
• Subjects: Genomics -- Periodicals; Bioinformatics -- Periodicals; 572.8
• DOI: 10.1093/nargab/lqaa026
• Languages: English
• ISSNs: 2631-9268

Abstract

Abstract: We have made several steps toward creating a fast and accurate algorithm for gene prediction in eukaryotic genomes. First, we introduced an automated method for efficient ab initio gene finding, GeneMark-ES, with parameters trained in iterative unsupervised mode. Next, in GeneMark-ET we proposed a method of integration of unsupervised training with information on intron positions revealed by mapping short RNA reads. Now we describe GeneMark-EP, a tool that utilizes another source of external information, a protein database, readily available prior to the start of a sequencing project. A new specialized pipeline, ProtHint, initiates massive protein mapping to genome and extracts hints to splice sites and translation start and stop sites of potential genes. GeneMark-EP uses the hints to improve estimation of model parameters as well as to adjust coordinates of predicted genes if they disagree with the most reliable hints (the -EP+ mode). Tests of GeneMark-EP and -EP+ demonstrated improvements in gene prediction accuracy in comparison with GeneMark-ES, while the GeneMark-EP+ showed higher accuracy than GeneMark-ET. We have observed that the most pronounced improvements in gene prediction accuracy happened in large eukaryotic genomes.