GINGER: an integrated method for high-accuracy prediction of gene structure in higher eukaryotes at the gene and exon level

Abstract

Abstract The prediction of gene structure within the genome sequence is the starting point of genome analysis, and its accuracy has a significant impact on the quality of subsequent analyses. Gene structure prediction is roughly divided into RNA-Seq-based methods, ab initio-based methods, homology-based methods, and the integration of individual prediction methods. Integrated methods are mainstream in recent genome projects because they improve prediction accuracy by combining or taking the best individual prediction findings; however, adequate prediction accuracy for eukaryotic species has not yet been achieved. Therefore, we developed an integrated tool, GINGER, that solves various issues related to gene structure prediction in higher eukaryotes. By handling artefacts in alignments of RNA and protein sequences, reconstructing gene structures via dynamic programming with appropriately weighted and scored exon/intron/intergenic regions, and applying different prediction processes and filtering criteria to multi-exon and single-exon genes, we achieved a significant improvement in accuracy compared to the existing integration methods. The feature of GINGER is its high prediction accuracy at the gene and exon levels, which is pronounced for species with more complex gene architectures. GINGER is implemented using Nextflow, which allows for the efficient and effective use of computing resources.