A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants
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ABSTRACT:
SUBMITTER: Qorri E
PROVIDER: S-EPMC9322961 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundEukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation.ResultsWe collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast.ConclusionsIn this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new algorithms developed in the future, thus expedite progress in this research field.
| S-EPMC4290732 | biostudies-literature
Project description:The diffusion of next-generation sequencing technologies has revolutionized research and diagnosis in the field of rare Mendelian disorders, notably via whole-exome sequencing (WES). However, one of the main issues hampering achievement of a diagnosis via WES analyses is the extended list of variants of unknown significance (VUS), mostly composed of missense variants. Hence, improved solutions are needed to address the challenges of identifying potentially deleterious variants and ranking them in a prioritized short list. We present MISTIC (MISsense deleTeriousness predICtor), a new prediction tool based on an original combination of two complementary machine learning algorithms using a soft voting system that integrates 113 missense features, ranging from multi-ethnic minor allele frequencies and evolutionary conservation, to physiochemical and biochemical properties of amino acids. Our approach also uses training sets with a wide spectrum of variant profiles, including both high-confidence positive (deleterious) and negative (benign) variants. Compared to recent state-of-the-art prediction tools in various benchmark tests and independent evaluation scenarios, MISTIC exhibits the best and most consistent performance, notably with the highest AUC value (> 0.95). Importantly, MISTIC maintains its high performance in the specific case of discriminating deleterious variants from benign variants that are rare or population-specific. In a clinical context, MISTIC drastically reduces the list of VUS (<30%) and significantly improves the ranking of "causative" deleterious variants. Pre-computed MISTIC scores for all possible human missense variants are available at http://lbgi.fr/mistic.
| S-EPMC7394404 | biostudies-literature
Project description:Objective: We aimed to improve the classification of SCN1A missense variants in patients with Dravet syndrome (DS) by combining and modifying the current variants classification criteria to minimize inconclusive test results. Methods: We established a score classification workflow based on evidence of pathogenicity to adapt the classification of DS-related SCN1A missense variants. In addition, we compiled the variants reported in the literature and our cohort and assessed the proposed pathogenic classification criteria. We combined information regarding previously established pathogenic amino acid changes, mode of inheritance, population-specific allele frequencies, localization within protein domains, and deleterious effect prediction analysis. Results: Our meta-analysis showed that 46% (506/1,101) of DS-associated SCN1A variants are missense. We applied the score classification workflow and 56.5% (286/506) of the variants had their classification changed from VUS: 17.8% (90/506) into "pathogenic" and 38.7% (196/506) as "likely pathogenic." Conclusion: Our results indicate that using multimodal analysis seems to be the best approach to interpret the pathogenic impact of SCN1A missense changes for the molecular diagnosis of patients with DS. By applying the proposed workflow, most DS related SCN1A variants had their classification improved.
| S-EPMC6455056 | biostudies-literature
Project description:With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were not always applicable for different conditions. Furthermore, the specificities were lower than the sensitivities for most methods (especially, for the experimentally evaluated benchmark datasets), suggesting that more rigorous cutoff values are necessary to distinguish pathogenic variants. Furthermore, REVEL, VEST3 and the combination of both methods (i.e. ReVe) showed the best overall performances with all the benchmark data. Finally, we evaluated the performances of these methods with de novo mutations, finding that ReVe consistently showed the best performance. We have summarized the performances of different methods under various conditions, providing tentative guidance for optimal tool selection.
| S-EPMC6125674 | biostudies-literature
Project description:Recombinant adeno-associated viral (rAAV) vectors have shown early promise in clinical trials. The therapeutic transgene cassette can be packaged in different AAV capsid pseudotypes, each having a unique transduction profile. At present, rAAV capsid serotype selection for a specific clinical trial is based on effectiveness in animal models. However, preclinical animal studies are not always predictive of human outcome. Here, in an attempt to further our understanding of these discrepancies, we used a chimaeric human-murine liver model to compare directly the relative efficiency of rAAV transduction in human versus mouse hepatocytes in vivo. As predicted from preclinical and clinical studies, rAAV2 vectors functionally transduced mouse and human hepatocytes at equivalent but relatively low levels. However, rAAV8 vectors, which are very effective in many animal models, transduced human hepatocytes rather poorly-approximately 20 times less efficiently than mouse hepatocytes. In light of the limitations of the rAAV vectors currently used in clinical studies, we used the same murine chimaeric liver model to perform serial selection using a human-specific replication-competent viral library composed of DNA-shuffled AAV capsids. One chimaeric capsid composed of five different parental AAV capsids was found to transduce human primary hepatocytes at high efficiency in vitro and in vivo, and provided species-selected transduction in primary liver, cultured cells and a hepatocellular carcinoma xenograft model. This vector is an ideal clinical candidate and a reagent for gene modification of human xenotransplants in mouse models of human diseases. More importantly, our results suggest that humanized murine models may represent a more precise approach for both selecting and evaluating clinically relevant rAAV serotypes for gene therapeutic applications.
| S-EPMC3939040 | biostudies-literature
Project description:BackgroundWhen applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.MethodsWe tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.ResultsWe treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.ConclusionsWe demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.
| S-EPMC6792253 | biostudies-literature
Project description:PurposeTo improve methods for predicting the impact of missense variants of uncertain significance (VUS) in BRCA1 and BRCA2 on protein function.MethodsFunctional data for 248 BRCA1 and 207 BRCA2 variants from assays with established high sensitivity and specificity for damaging variants were used to recalibrate 40 in silico algorithms predicting the impact of variants on protein activity. Additional random forest (RF) and naïve voting method (NVM) metapredictors for both BRCA1 and BRCA2 were developed to increase predictive accuracy.ResultsOptimized thresholds for in silico prediction models significantly improved the accuracy of predicted functional effects for BRCA1 and BRCA2 variants. In addition, new BRCA1-RF and BRCA2-RF metapredictors showed area under the curve (AUC) values of 0.92 (95% confidence interval [CI]: 0.88-0.96) and 0.90 (95% CI: 0.84-0.95), respectively. Similarly, the BRCA1-NVM and BRCA2-NVM models had AUCs of 0.93 and 0.90. The RF and NVM models were used to predict the pathogenicity of all possible missense variants in BRCA1 and BRCA2.ConclusionThe recalibrated algorithms and new metapredictors significantly improved upon current models for predicting the impact of variants in cancer risk-associated domains of BRCA1 and BRCA2. Prediction of the functional impact of all possible variants in BRCA1 and BRCA2 provides important information about the clinical relevance of variants in these genes.
| S-EPMC6287763 | biostudies-literature
Project description:Vital to the treatment of influenza is the use of antivirals such as Oseltamivir (Tamiflu) and Zanamivir (Relenza); however, antiviral resistance is becoming an increasing problem for these therapeutics. The RNA-dependent RNA polymerase acidic N-terminal (PAN) endonuclease, a critical component of influenza viral replication machinery, is an antiviral target that was recently validated with the approval of Baloxavir Marboxil (BXM). Despite its clinical success, BXM has demonstrated susceptibility to resistance mutations, specifically the I38T, E23K, and A36 V mutants of PAN. To better understand the effects of these mutations on BXM resistance and improve the design of more robust therapeutics, this study examines key differences in protein-inhibitor interactions with two inhibitors and the I38T, E23K, and A36 V mutants. Differences in inhibitor binding were evaluated by measuring changes in binding to PAN using two biophysical methods. The binding mode of two distinct inhibitors was determined crystallographically with both wild-type and mutant forms of PAN. Collectively, these studies give some insight into the mechanism of antiviral resistance of these mutants.
| S-EPMC10851415 | biostudies-literature
Project description:Asian populations are under-represented in human genomics research. Here, we characterize clinically significant genetic variation in 9051 genomes representing East Asian, South Asian, and severely under-represented Austronesian-speaking Southeast Asian ancestries. We observe disparate genetic risk burden attributable to ancestry-specific recurrent variants and identify individuals with variants specific to ancestries discordant to their self-reported ethnicity, mostly due to cryptic admixture. About 27% of severe recessive disorder genes with appreciable carrier frequencies in Asians are missed by carrier screening panels, and we estimate 0.5% Asian couples at-risk of having an affected child. Prevalence of medically-actionable variant carriers is 3.4% and a further 1.6% harbour variants with potential for pathogenic classification upon additional clinical/experimental evidence. We profile 23 pharmacogenes with high-confidence gene-drug associations and find 22.4% of Asians at-risk of Centers for Disease Control and Prevention Tier 1 genetic conditions concurrently harbour pharmacogenetic variants with actionable phenotypes, highlighting the benefits of pre-emptive pharmacogenomics. Our findings illuminate the diversity in genetic disease epidemiology and opportunities for precision medicine for a large, diverse Asian population.
| S-EPMC9637116 | biostudies-literature
Project description:Variant interpretation depends on accurate annotations using biologically relevant transcripts. We have developed a systematic strategy for designating primary transcripts and have applied it to 109 hearing loss-associated genes that were divided into three categories. Category 1 genes (n = 38) had a single transcript; category 2 genes (n = 33) had multiple transcripts, but a single transcript was sufficient to represent all exons; and category 3 genes (n = 38) had multiple transcripts with unique exons. Transcripts were curated with respect to gene expression reported in the literature and the Genotype-Tissue Expression Project. In addition, high-frequency loss-of-function variants in the Genome Aggregation Database and disease-causing variants in ClinVar and the Human Gene Mutation Database across the 109 genes were queried. These data were used to classify exons as clinically significant, insignificant, or of uncertain significance. Interestingly, 6% of all exons, containing 124 reportedly disease-causing variants, were of uncertain significance. Finally, we used exon-level next-generation sequencing quality metrics generated at two clinical laboratories and identified a total of 43 technically challenging exons in 20 different genes that had inadequate coverage and/or homology issues that might lead to false-variant calls. We have demonstrated that transcript analysis plays a critical role in accurate clinical variant interpretation.
| S-EPMC6204605 | biostudies-literature