scholarly journals Unmet needs in human genomic variant interpretation

2017 ◽  
Vol 20 (3) ◽  
pp. 376-377 ◽  
Author(s):  
Peter Bauer ◽  
Ellen Karges ◽  
Gabriela Oprea ◽  
Arndt Rolfs
Keyword(s):  
Unmet Needs ◽  
Variant Interpretation ◽  
Human Genomic ◽  
Genomic Variant

scholarly journals GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation

2020 ◽  
Author(s):  
Roozbeh Manshaei ◽  
Sean DeLong ◽  
Veronica Andric ◽  
Esha Joshi ◽  
John B. A. Okello ◽  
...  
Keyword(s):  
Integrated System ◽  
Variant Interpretation ◽  
Expression Data ◽  
Variant Prioritization ◽  
Computational Tools ◽  
Phenotype Data ◽  
Causal Variants ◽  
Interpretation Process ◽  
Genomic Variant ◽  
Speed And Accuracy

ABSTRACTVariant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually scouring through a multitude of independent databases, often with the aid of several and mostly independent computational tools.To streamline the variant interpretation process, we developed GeneTerpret platform that collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in a given genome. The platform assigns quantitative validity scores to genes by query and assembly of the current genotype-phenotype data, sequence homology, molecular interactions, expression data, and animal models. The platform uses the American College of Medical Genetics (ACMG) criteria to categorize variants into five tiers (from benign to pathogenic). The platform then outputs a prioritized list of potentially causal variants/genes in a given genome for a specific case.GeneTerpret is a flexible and free platform designed to streamline the variant interpretation process through a unique interface, with improved ease, speed and accuracy. This unique integrated system provides effective validity and pathogenicity modules to assess genetic variant data and allows the user to decide which output and impact level should be considered in this process. The platform can be accessed and used online at https://geneterpret.com.

scholarly journals VarSome: the human genomic variant search engine

2018 ◽  
Vol 35 (11) ◽  
pp. 1978-1980 ◽  
Author(s):  
Christos Kopanos ◽  
Vasilis Tsiolkas ◽  
Alexandros Kouris ◽  
Charles E Chapple ◽  
Monica Albarca Aguilera ◽  
...  
Keyword(s):  
Search Engine ◽  
Human Genomic ◽  
Genomic Variant

scholarly journals Assessing performance of pathogenicity predictors using clinically-relevant variant datasets

2020 ◽  
Author(s):  
Adam C Gunning ◽  
Verity Fryer ◽  
James Fasham ◽  
Andrew H Crosby ◽  
Sian Ellard ◽  
...  
Keyword(s):  
In Silico ◽  
Variant Classification ◽  
Variant Interpretation ◽  
Independent Validation ◽  
Tool Performance ◽  
Diagnostic Panel ◽  
Genomic Variant ◽  
Insight Into ◽  
Panel Sequencing

ABSTRACTPurposePathogenicity predictors are an integral part of genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically-relevant dataset has not been undertaken.MethodsWe derive two validation datasets: an “open” dataset containing variants extracted from publicly-available databases, similar to those commonly applied in previous benchmarking exercises, and a “clinically-representative” dataset containing variants identified through research/diagnostic exome and diagnostic panel sequencing. Using these datasets, we evaluate the performance of three recently developed meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2.ResultsAlthough the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically-representative dataset. Using our clinically-relevant dataset, REVEL performed best with an area under the ROC of 0.81. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification.ConclusionOur results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as recommended by current variant classification guidelines.

scholarly journals Keeping up with the genomes: scaling genomic variant interpretation

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Heidi L. Rehm ◽  
Douglas M. Fowler
Keyword(s):  
Variant Interpretation ◽  
Genomic Variant

scholarly journals Assessing performance of pathogenicity predictors using clinically relevant variant datasets

2020 ◽  
pp. jmedgenet-2020-107003
Author(s):  
Adam C Gunning ◽  
Verity Fryer ◽  
James Fasham ◽  
Andrew H Crosby ◽  
Sian Ellard ◽  
...  
Keyword(s):  
In Silico ◽  
Operating Characteristic ◽  
Current Practice ◽  
Characteristic Curve ◽  
Variant Interpretation ◽  
Independent Validation ◽  
Tool Performance ◽  
Genomic Variant ◽  
Insight Into ◽  
In Silico Tools

BackgroundPathogenicity predictors are integral to genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically relevant dataset has not been undertaken.MethodsWe derive two validation datasets: an ‘open’ dataset containing variants extracted from publicly available databases, similar to those commonly applied in previous benchmarking exercises, and a ‘clinically representative’ dataset containing variants identified through research/diagnostic exome and panel sequencing. Using these datasets, we evaluate the performance of three recent meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2.ResultsAlthough the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically representative dataset. Using our clinically relevant dataset, REVEL performed best with an area under the receiver operating characteristic curve of 0.82. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification.ConclusionOur results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as in current practice.

scholarly journals VarSome: The Human Genomic Variant Search Engine

2018 ◽  
Author(s):  
Christos Kopanos ◽  
Vasilis Tsiolkas ◽  
Alexandros Kouris ◽  
Charles E. Chapple ◽  
Monica Albarca Aguilera ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Search Engine ◽  
Impact Analysis ◽  
Analysis Tool ◽  
Human Genetic Variation ◽  
Link Type ◽  
Human Genomic ◽  
Genomic Variant ◽  
Global Expertise

AbstractSummaryVarSome.com is a search engine, aggregator and impact analysis tool for human genetic variation and a community-driven project aiming at sharing global expertise on human variants.AvailabilityVarSome is freely available at http://varsome.com.

Hypomorphic alleles pose challenges in rare disease genomic variant interpretation

2021 ◽  
Author(s):  
Daniel K. Nolan ◽  
Bimal Chaudhari ◽  
Samuel J. Franklin ◽  
Saranga Wijeratne ◽  
Ruthann Pfau ◽  
...  
Keyword(s):  
Rare Disease ◽  
Variant Interpretation ◽  
Hypomorphic Alleles ◽  
Genomic Variant
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