Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm

Abstract

We present a new approach to automatic training of a eukaryotic ab initio gene finding algorithm. With the advent of Next-Generation Sequencing, automatic training has become paramount, allowing genome annotation pipelines to keep pace with the speed of genome sequencing. Earlier we developed GeneMark-ES, currently the only gene finding algorithm for eukaryotic genomes that performs automatic training in unsupervised ab initio mode. The new algorithm, GeneMark-ET augments GeneMark-ES with a novel method that integrates RNA-Seq read alignments into the self-training procedure. Use of 'assembled' RNA-Seq transcripts is far from trivial; significant error rate of assembly was revealed in recent assessments. We demonstrated in computational experiments that the proposed method of incorporation of 'unassembled' RNA-Seq reads improves the accuracy of gene prediction; particularly, for the 1.3 GB genome of Aedes aegypti the mean value of prediction Sensitivity and Specificity at the gene level increased over GeneMark-ES by 24.5%. In the current surge of genomic data when the need for accurate sequence annotation is higher than ever, GeneMark-ET will be a valuable addition to the narrow arsenal of automatic gene prediction tools.

Figure 1.

The dot plot graph depicting average lengths of exons, introns and intergenic regions against the value of percentage of non-coding DNA in a given genome was made for the five insect genomes used in the GeneMark-ET tests as well as for several other eukaryotic species. The average lengths of intron and intergenic regions correlate with the genome length while the average length of protein-coding exons (CDS) does not show dependence on the genome size.

Figure 2.

Diagram of the iterative semi-supervised training of GeneMark-ET.

Figure 3.

Selection of elements of training set in GeneMark-ET for the next iteration. The new training set of protein-coding regions is comprised from exons with at least one ‘anchored splice site’ as well as long exons predicted ab initio (>800 nt).

Figure 4.

Observed dynamics of change in iterations of the mean of Sn and Sp internal exon prediction values for the GeneMark-ET and GeneMark-ES algorithms in cases of Drosophila melanogaster (A) and Anopheles aegypti (B) genomes.