| 1. |
- Niroula, Abhishek, et al.
(författare)
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How good are pathogenicity predictors in detecting benign variants?
- 2019
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-7358. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- Computational tools are widely used for interpreting variants detected in sequencing projects. The choice of these tools is critical for reliable variant impact interpretation for precision medicine and should be based on systematic performance assessment. The performance of the methods varies widely in different performance assessments, for example due to the contents and sizes of test datasets. To address this issue, we obtained 63,160 common amino acid substitutions (allele frequency ≥1% and <25%) from the Exome Aggregation Consortium (ExAC) database, which contains variants from 60,706 genomes or exomes. We evaluated the specificity, the capability to detect benign variants, for 10 variant interpretation tools. In addition to overall specificity of the tools, we tested their performance for variants in six geographical populations. PON-P2 had the best performance (95.5%) followed by FATHMM (86.4%) and VEST (83.5%). While these tools had excellent performance, the poorest method predicted more than one third of the benign variants to be disease-causing. The results allow choosing reliable methods for benign variant interpretation, for both research and clinical purposes, as well as provide a benchmark for method developers.
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| 2. |
- Niroula, Abhishek, et al.
(författare)
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MPRAscore : robust and non-parametric analysis of massively parallel reporter assays
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 35:24, s. 5351-5353
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Massively parallel reporter assays (MPRA) enable systematic screening of DNA sequence variants for effects on transcriptional activity. However, convenient analysis tools are still needed. RESULTS: We introduce MPRAscore, a novel tool to infer allele-specific effects on transcription from MPRA data. MPRAscore uses a weighted, variance-regularized method to calculate variant effect sizes robustly, and a permutation approach to test for significance without assuming normality or independence. AVAILABILITY AND IMPLEMENTATION: Source code (C++), precompiled binaries and data used in the paper at https://github.com/abhisheknrl/MPRAscore and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA554195. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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| 3. |
- Yang, Yang, et al.
(författare)
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ProTstab - Predictor for cellular protein stability
- 2019
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Stability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples. Results: We took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities. Conclusions: The Pearson's correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.
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