| 1. |
- Niroula, Abhishek, et al.
(författare)
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Classification of Amino Acid Substitutions in Mismatch Repair Proteins Using PON-MMR2.
- 2015
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Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 36:12, s. 1128-1134
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Tidskriftsartikel (refereegranskat)abstract
- Variations in mismatch repair (MMR) system genes are causative of Lynch syndrome and other cancers. Thousands of variants have been identified in MMR genes, but the clinical relevance is known for only a small proportion. Recently, the InSiGHT group classified 2,360 MMR variants into five classes. One-third of variants, majority of which is nonsynonymous variants, remain to be of uncertain clinical relevance. Computational tools can be used to prioritize variants for disease relevance investigations. Previously, we classified 248 MMR variants as likely pathogenic and likely benign using PON-MMR. We have developed a novel tool, PON-MMR2, which is trained on a larger and more reliable dataset. In performance comparison, PON-MMR2 outperforms both generic tolerance prediction methods as well as methods optimized for MMR variants. It achieves accuracy and MCC of 0.89 and 0.78, respectively, in cross-validation and 0.86 and 0.69, respectively, on an independent test dataset. We classified 354 class 3 variants in InSiGHT database as well as all possible amino acid substitutions in four MMR proteins. Likely harmful variants mainly appear in the protein core, whereas likely benign variants are on the surface. PON-MMR2 is a highly reliable tool to prioritize variants for functional analysis. It is freely available at http://structure.bmc.lu.se/PON-MMR2/.
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| 2. |
- Niroula, Abhishek, et al.
(författare)
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Harmful somatic amino acid substitutions affect key pathways in cancers.
- 2015
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Ingår i: BMC Medical Genomics. - : Springer Science and Business Media LLC. - 1755-8794. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Cancer is characterized by the accumulation of large numbers of genetic variations and alterations of multiple biological phenomena. Cancer genomics has largely focused on the identification of such genetic alterations and the genes containing them, known as 'cancer genes'. However, the non-functional somatic variations out-number functional variations and remain as a major challenge. Recurrent somatic variations are thought to be cancer drivers but they are present in only a small fraction of patients.
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| 3. |
- Niroula, Abhishek, et al.
(författare)
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PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants.
- 2015
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:2
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Tidskriftsartikel (refereegranskat)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/.
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