OrthoFiller: utilising data from multiple species to improve the completeness of genome annotations

Michael P Dunne,Steven Kelly

doi:10.1186/s12864-017-3771-x

Abstract

BackgroundComplete and accurate annotation of sequenced genomes is of paramount importance to their utility and analysis. Differences in gene prediction pipelines mean that genome annotations for a species can differ considerably in the quality and quantity of their predicted genes. Furthermore, genes that are present in genome sequences sometimes fail to be detected by computational gene prediction methods. Erroneously unannotated genes can lead to oversights and inaccurate assertions in biological investigations, especially for smaller-scale genome projects, which rely heavily on computational prediction.ResultsHere we present OrthoFiller, a tool designed to address the problem of finding and adding such missing genes to genome annotations. OrthoFiller leverages information from multiple related species to identify those genes whose existence can be verified through comparison with known gene families, but which have not been predicted. By simulating missing gene annotations in real sequence datasets from both plants and fungi we demonstrate the accuracy and utility of OrthoFiller for finding missing genes and improving genome annotations. Furthermore, we show that applying OrthoFiller to existing “complete” genome annotations can identify and correct substantial numbers of erroneously missing genes in these two sets of species.ConclusionsWe show that significant improvements in the completeness of genome annotations can be made by leveraging information from multiple species.

Highlights

Genome sequences have become fundamental to many aspects of biological research
We show that applying OrthoFiller to existing “complete” genome annotations can identify and correct substantial numbers of erroneously missing genes in these two sets of species
We show that significant improvements in the completeness of genome annotations can be made by leveraging information from multiple species

Summary

Introduction

Genome sequences have become fundamental to many aspects of biological research. They provide the basis for our understanding of the biological properties of organisms, and enable extrapolation and comparison of information between species. There has been substantial methodology development in the area of automated gene prediction, with the production of several effective algorithms for identifying genes in de novo sequenced genomes [3] These methods predict genes by learning species-specific characteristics from training sets of manually curated genes. These characteristics include the distribution of intron and exon lengths, intron GC content, exon GC content, codon bias, and motifs associated with the starts and ends of exons (splice donor and acceptor sites, poly-pyrimidine tracts and other features). These characteristics are used to identify novel genes in raw nucleotide sequences.

Results

Discussion

Conclusion