AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

Abstract

Motivation A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy. Results Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88–95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites. Availability and implementation AssessORF is available as an R package via the Bioconductor package repository. Supplementary information Supplementary data are available at Bioinformatics online.

KEMET – A python tool for KEGG Module evaluation and microbial genome annotation expansion

“…Functional annotations of predicted genes were performed using eggNOG-mapper v2 [7] with default parameters. While in principle alternative gene predictions can impact the subsequent functional annotation, previous empirical investigations found negligible performance variation among different tools [23] , [24] . For this reason, our tests focused on benchmarking functional annotation prediction by using a single state-of-the-art gene prediction tool.…”

A Large-Scale Genome-Based Survey of Acidophilic Bacteria Suggests That Genome Streamlining Is an Adaption for Life at Low pH

“…We minimized these potential errors by analyzing only genomes that had passed a high-quality CheckM filter ( Parks et al, 2015 ), yielding the 597 genomes used in our genomic analyses. However, even high-quality genomes are prone to errors of ORF annotation, especially in the identification of correct translation start sites ( Korandla et al, 2020 ), which will impact the predictions of gene and intergenic spacer sizes. Currently, there is no computational program for ORF prediction that is flawless, including GenBank ( Korandla et al, 2020 ), and we expect that future work will improve the annotations of ORFs used in our study.…”

A large-scale genome-based survey of acidophilic Bacteria suggests that genome streamlining is an adaption for life at low pH

“…al., 2020) which will impact predictions of gene and intergenic spacer sizes. Currently, there are no computational program for ORF prediction that is flawless, including GenBank (Korandla et. al., 2020), and we expect that future work will improve the annotations of ORFs used in our study.…”

“…= −0.11, p-value 0.06). A potential problem in the interpretation of this result stems from uncertainties in the identification of ORFs, most notably by errors in the identification of the correct site of initiation of protein coding regions (Korandla et. al., 2020).…”

“…al., 2015). However, even high-quality genomes are prone to errors of ORF annotation especially in the identification of correct translation start sites (Korandla et. al., 2020) which will impact predictions of gene and intergenic spacer sizes.…”