PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants.
Niroula, Abhishek LU ; Urolagin, Siddhaling LU and Vihinen, Mauno LU
(2015)
In PLoS ONE
10(2).
- Abstract
- 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... (More)
- 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/. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5145550
- author
- Niroula, Abhishek
LU
; Urolagin, Siddhaling
LU
and Vihinen, Mauno
LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS ONE
- volume
- 10
- issue
- 2
- article number
- e0117380
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- pmid:25647319
- wos:000348822600067
- scopus:84922351308
- pmid:25647319
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0117380
- language
- English
- LU publication?
- yes
- id
- 052fea31-a84d-4087-96b2-ba1299aac0f8 (old id 5145550)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/25647319?dopt=Abstract
- date added to LUP
- 2016-04-01 14:50:11
- date last changed
- 2022-04-14 19:56:44
@article{052fea31-a84d-4087-96b2-ba1299aac0f8,
abstract = {{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/.}},
author = {{Niroula, Abhishek and Urolagin, Siddhaling and Vihinen, Mauno}},
issn = {{1932-6203}},
language = {{eng}},
number = {{2}},
publisher = {{Public Library of Science (PLoS)}},
series = {{PLoS ONE}},
title = {{PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants.}},
url = {{https://lup.lub.lu.se/search/files/4191419/7762133}},
doi = {{10.1371/journal.pone.0117380}},
volume = {{10}},
year = {{2015}},
}