PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants.
Författare
Summary, in English
More reliable and faster prediction methods are needed to interpret enormous amounts of data generated by sequencing and genome projects. We have developed a new computational tool, PON-P2, for classification of amino acid substitutions in human proteins. The method is a machine learning-based classifier and groups the variants into pathogenic, neutral and unknown classes, on the basis of random forest probability score. PON-P2 is trained using pathogenic and neutral variants obtained from VariBench, a database for benchmark variation datasets. PON-P2 utilizes information about evolutionary conservation of sequences, physical and biochemical properties of amino acids, GO annotations and if available, functional annotations of variation sites. Extensive feature selection was performed to identify 8 informative features among altogether 622 features. PON-P2 consistently showed superior performance in comparison to existing state-of-the-art tools. In 10-fold cross-validation test, its accuracy and MCC are 0.90 and 0.80, respectively, and in the independent test, they are 0.86 and 0.71, respectively. The coverage of PON-P2 is 61.7% in the 10-fold cross-validation and 62.1% in the test dataset. PON-P2 is a powerful tool for screening harmful variants and for ranking and prioritizing experimental characterization. It is very fast making it capable of analyzing large variant datasets. PON-P2 is freely available at http://structure.bmc.lu.se/PON-P2/.
Avdelning/ar
- Proteinbioinformatik
- BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
Publiceringsår
2015
Språk
Engelska
Publikation/Tidskrift/Serie
PLoS ONE
Volym
10
Issue
2
Fulltext
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Public Library of Science (PLoS)
Ämne
- Medical Genetics
Status
Published
Forskningsgrupp
- Protein Bioinformatics
ISBN/ISSN/Övrigt
- ISSN: 1932-6203