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Published on: August 20, 2019
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation
Emidio Capriotti1, Remo Calabrese, Piero Fariselli
1Division of Informatics, Department of Pathology, University of Alabama at Birmingham, Birmingham AL, USA. emidio@uab.edu
The WS-SNPs&GO web server predicts disease-associated Single Amino acid Polymorphisms (SAPs) using protein sequence and structure. It achieves high accuracy, offering a valuable tool for genetic variation analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- SNPs&GO is a method for predicting deleterious Single Amino acid Polymorphisms (SAPs) using protein functional annotation.
- The web server implementation, WS-SNPs&GO, is presented, utilizing Support Vector Machines (SVM).
- Input includes protein sequence/structure, target variations, and functional Gene Ontology (GO) terms.
Purpose of the Study:
- To present the web server implementation of SNPs&GO (WS-SNPs&GO).
- To provide probabilities for protein variations associated with human diseases.
- To integrate sequence, structure, evolutionary profile, and functional information for variant analysis.
Main Methods:
- Implementation of updated sequence-based SNPs&GO and structure-based SNPs&GO(3d) algorithms.
- Extensive testing on annotated variations from the SwissVar database.
- Utilizing Support Vector Machines (SVM) with protein sequence, structure, and functional annotations.
Main Results:
- Sequence-based approach achieved 81% accuracy, 0.61 correlation, and 0.88 AUC on over 38,000 SAPs.
- Structure-based method scored 84% accuracy, 0.68 correlation, and 0.91 AUC on ~6,600 variations mapped to PDB structures.
- Blind set testing yielded 79% (sequence-based) and 83% (structure-based) overall accuracy.
Conclusions:
- WS-SNPs&GO is a valuable tool integrating diverse protein data for variant analysis.
- The server provides probabilities for disease association of protein variations.
- WS-SNPs&GO is freely available at http://snps.biofold.org/snps-and-go.
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