ArticleBioinformatics (Oxford, England)2020
AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
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- A Practical Guide for the Quality Evaluation of Fluobodies/Chromobodies.Biomolecules · 2024Article
- INRI-seq enables global cell-free analysis of translation initiation and off-target effects of antisense inhibitors.Nucleic acids research · 2022Article
- FindNonCoding: rapid and simple detection of non-coding RNAs in genomes.Bioinformatics (Oxford, England) · 2022Article
- KEMET - A python tool for KEGG Module evaluation and microbial genome annotation expansion.Computational and structural biotechnology journal · 2022Article
- A Large-Scale Genome-Based Survey of Acidophilic Bacteria Suggests That Genome Streamlining Is an Adaption for Life at Low pH.Frontiers in microbiology · 2022Article
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Authors and funding
5 authors.
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Abstract
motivationA 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.
resultsHere, 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.
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