ArticlePLoS computational biology2011
Genome majority vote improves gene predictions.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
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- A Novel Quality Measure and Correction Procedure for the Annotation of Microbial Translation Initiation Sites.PloS one · 2015Article
- Functional phylogenomics analysis of bacteria and archaea using consistent genome annotation with UniFam.BMC evolutionary biology · 2014Article
- eCAMBer: efficient support for large-scale comparative analysis of multiple bacterial strains.BMC bioinformatics · 2014Article
- ORFcor: identifying and accommodating ORF prediction inconsistencies for phylogenetic analysis.PloS one · 2013Article
- SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery.PloS one · 2013Article
- Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.PloS one · 2013Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Recent studies have noted extensive inconsistencies in gene start sites among orthologous genes in related microbial genomes. Here we provide the first documented evidence that imposing gene start consistency improves the accuracy of gene start-site prediction. We applied an algorithm using a genome majority vote (GMV) scheme to increase the consistency of gene starts among orthologs. We used a set of validated Escherichia coli genes as a standard to quantify accuracy. Results showed that the GMV algorithm can correct hundreds of gene prediction errors in sets of five or ten genomes while introducing few errors. Using a conservative calculation, we project that GMV would resolve many inconsistencies and errors in publicly available microbial gene maps. Our simple and logical solution provides a notable advance toward accurate gene maps.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.