| 1. |
- Carraro, Marco, et al.
(author)
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Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI
- 2017
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In: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 38:9, s. 1042-1050
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Journal article (peer-reviewed)abstract
- Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.
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| 2. |
- Daneshjou, Roxana, et al.
(author)
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Working toward precision medicine : Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
- 2017
-
In: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 38:9, s. 1182-1192
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Journal article (peer-reviewed)abstract
- Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.
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| 3. |
- Niroula, Abhishek, et al.
(author)
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Classification of Amino Acid Substitutions in Mismatch Repair Proteins Using PON-MMR2.
- 2015
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In: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 36:12, s. 1128-1134
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Journal article (peer-reviewed)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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| 4. |
- Niroula, Abhishek, et al.
(author)
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PON-P and PON-P2 predictor performance in CAGI challenges : Lessons learned
- 2017
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In: Human Mutation. - : Hindawi Limited. - 1059-7794 .- 1098-1004. ; 38:9, s. 1085-1091
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Journal article (peer-reviewed)abstract
- Computational tools are widely used for ranking and prioritizing variants for characterizing their disease relevance. Since numerous tools have been developed, they have to be properly assessed before being applied. Critical Assessment of Genome Interpretation (CAGI) experiments have significantly contributed toward the assessment of prediction methods for various tasks. Within and outside the CAGI, we have addressed several questions that facilitate development and assessment of variation interpretation tools. These areas include collection and distribution of benchmark datasets, their use for systematic large-scale method assessment, and the development of guidelines for reporting methods and their performance. For us, CAGI has provided a chance to experiment with new ideas, test the application areas of our methods, and network with other prediction method developers. In this article, we discuss our experiences and lessons learned from the various CAGI challenges. We describe our approaches, their performance, and impact of CAGI on our research. Finally, we discuss some of the possibilities that CAGI experiments have opened up and make some suggestions for future experiments.
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| 5. |
- Niroula, Abhishek, et al.
(author)
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Variation Interpretation Predictors : Principles, Types, Performance, and Choice
- 2016
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In: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 37:6, s. 579-597
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Journal article (peer-reviewed)abstract
- Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.
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