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- Ali, Heidi, et al.
(författare)
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Classification of mismatch repair gene missense variants with PON-MMR
- 2012
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Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 33:4, s. 642-650
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Tidskriftsartikel (refereegranskat)abstract
- Numerous mismatch repair (MMR) gene variants have been identified in Lynch syndrome and other cancer patients, but knowledge about their pathogenicity is frequently missing. The diagnosis and treatment of patients would benefit from knowing which variants are disease related. Bioinformatic approaches are well suited to the problem and can handle large numbers of cases. Functional effects were revealed based on literature for 168 MMR missense variants. Performance of numerous prediction methods was tested with this dataset. Among the tested tools, only the results of tolerance prediction methods correlated to functional information, however, with poor performance. Therefore, a novel consensus-based predictor was developed. The novel prediction method, pathogenic-or-not mismatch repair (PON-MMR), achieved accuracy of 0.87 and Matthews correlation coefficient of 0.77 on the experimentally verified variants. When applied to 616 MMR cases with unknown effects, 81 missense variants were predicted to be pathogenic and 167 neutral. With PON-MMR, the number of MMR missense variants with unknown effect was reduced by classifying a large number of cases as likely pathogenic or benign. The results can be used, for example, to prioritize cases for experimental studies and assist in the classification of cases. Hum Mutat 33:642650, 2012. (c) 2012 Wiley Periodicals, Inc.
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- Ali, Heidi, et al.
(författare)
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Performance of Protein Disorder Prediction Programs on Amino Acid Substitutions
- 2014
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Ingår i: Human Mutation. - : Hindawi Limited. - 1059-7794. ; 35:7, s. 794-804
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Forskningsöversikt (refereegranskat)abstract
- Many proteins contain intrinsically disordered regions, which may be crucial for function, but on the other hand be related to the pathogenicity of variants. Prediction programs have been developed to detect disordered regions from sequences and used to predict the consequences of variants, although their performance for this task has not been assessed. We tested the performance of protein disorder prediction programs in detecting changes to disorder caused by amino acid substitutions. We assessed the performance of 29 protein disorder predictors and versions with 101 amino acid substitutions, whose effects have been experimentally validated. Disorder predictors detected the true positives at most with 6% success rate and true negatives with 34% rate for variants. The corresponding rates for the wild-type forms are 7% and 90%, respectively. The analysis revealed that disorder programs cannot reliably predict the effects of substitutions; consequently, the tested methods, and possibly similar programs, cannot be recommended for variant analysis without other information indicating to the relevance of disorder. These results inspired us to develop a new method, PON-Diso (http://structure.bmc.lu.se/PON-Diso), for disorder-related amino acid substitutions. With 50% success rate for independent test set and 70.5% rate in cross-validation, it outperforms the evaluated methods.
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- Brownstein, Catherine A., et al.
(författare)
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An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge
- 2014
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Ingår i: Genome Biology. - : Springer Science and Business Media LLC. - 1465-6906 .- 1474-760X. ; 15:3, s. R53-
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
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