VarSight: prioritizing clinically reported variants with binary classification algorithms.

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

uab-cgds-worthey/quac: v1.7

<ul> <li>Makes minor documentation updates - updating citation info, adding JOSS badge and updating zenodo badge to use generic DOI</li> <li>Merges <code>joss_manuscript</code> to the <code>master</code> branch to bring it up to date.</li> </ul> <h2>What's Changed</h2> <ul> <li>QuaC is published!! by @ManavalanG in https://github.com/uab-cgds-worthey/quac/pull/93</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/uab-cgds-worthey/quac/compare/1.6...1.7</p&gt

uab-cgds-worthey/rosalution: Rosalution 0.6.0 - Supporting data accessibility, integration, curation, interoperability, and reuse for precision animal modeling

Maintainers @SeriousHorncat @JmScherer @fatimarabab New Contributors @jbarkley256 made their first contribution in https://github.com/uab-cgds-worthey/rosalution/pull/61 What's Changed Importing cases for analysis, with support of automated configured annotations of genomic units with support at curating evidence for analysis review. Attachment of supporting evidence as files or URLs Support for researchers entering case relevant information to be disseminated to research team Multi image attachment for curated figures for analyses Attachment of visual annotations associated with genomic units Viewing annotations for the genomic units in a case for analysis CAS user login, enabling organizations to connect to their Center Authentication Service for user credentials Filtering available analyses by data presented on analysis cards in the analysis feed Workflows to change analyses from being in preparation to ready, active, approved, declined, on-hold 3rd party attachments to link Monday.com and Phenotips URL to the specific analysis Full Changelog: https://github.com/uab-cgds-worthey/rosalution/compare/0.6.0-er...0.6.

Ten simple rules for using public biological data for your research.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility

VarSight: prioritizing clinically reported variants with binary classification algorithms.

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