Dataset Information

GeneValidator: identify problems with protein-coding gene predictions.

ABSTRACT:

Unlabelled

: Genomes of emerging model organisms are now being sequenced at very low cost. However, obtaining accurate gene predictions remains challenging: even the best gene prediction algorithms make substantial errors and can jeopardize subsequent analyses. Therefore, many predicted genes must be time-consumingly visually inspected and manually curated. We developed GeneValidator (GV) to automatically identify problematic gene predictions and to aid manual curation. For each gene, GV performs multiple analyses based on comparisons to gene sequences from large databases. The resulting report identifies problematic gene predictions and includes extensive statistics and graphs for each prediction to guide manual curation efforts. GV thus accelerates and enhances the work of biocurators and researchers who need accurate gene predictions from newly sequenced genomes.

Availability and implementation

GV can be used through a web interface or in the command-line. GV is open-source (AGPL), available at https://wurmlab.github.io/tools/genevalidator

Contact

: y.wurm@qmul.ac.uk

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Dragan MA

PROVIDER: S-EPMC4866521 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

GeneValidator: identify problems with protein-coding gene predictions.

Drăgan Monica-Andreea MA Moghul Ismail I Priyam Anurag A Bustos Claudio C Wurm Yannick Y

Bioinformatics (Oxford, England) 20160118 10

<h4>Unlabelled</h4>: Genomes of emerging model organisms are now being sequenced at very low cost. However, obtaining accurate gene predictions remains challenging: even the best gene prediction algorithms make substantial errors and can jeopardize subsequent analyses. Therefore, many predicted genes must be time-consumingly visually inspected and manually curated. We developed GeneValidator (GV) to automatically identify problematic gene predictions and to aid manual curation. For each gene, GV ...[more]

PMID: 26787666

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Project description:BackgroundMany crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated.ResultsGene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets) in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets.ConclusionGene Ontology annotations along with the deduced knowledge rules could be implemented to partially remove false predicted PPI pairs. Removal of false positives from predicted datasets increases the true positive fractions of the datasets and improves the robustness of predicted pairs as compared to random protein pairing, and eventually results in better overlap with experimental results.

Dataset Information

GeneValidator: identify problems with protein-coding gene predictions.

Unlabelled

Availability and implementation

Contact

Supplementary information

Publications

GeneValidator: identify problems with protein-coding gene predictions.

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