Understanding clinical genetic test results in the era of next-generation sequencing has become increasingly complex, necessitating clear and thorough guidelines for sequence variant interpretation. To meet this need the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for a systematic approach for sequence variant interpretation in 2015. This framework is intended to be adaptable to any Mendelian condition, promoting transparency and consistency in variant interpretation, yet its comprehensive nature yields important challenges and caveats that end users must understand. In this review, we address some of these nuances and discuss the evolving efforts to refine and adapt this framework. We also consider the added complexity of distinguishing between variant-level interpretations and case-level conclusions, particularly in the context of the large gene panel approach to clinical diagnostics.
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http://dx.doi.org/10.1038/s41436-018-0100-y | DOI Listing |
Mol Diagn Ther
January 2025
Istituto Europeo di Oncologia, IRCCS, Via Adamello 16, 20139, Milan, Italy.
Background: Predicting response to targeted cancer therapies increasingly relies on both simple and complex genetic biomarkers. Comprehensive genomic profiling using high-throughput assays must be evaluated for reproducibility and accuracy compared with existing methods.
Methods: This study is a multicenter evaluation of the Oncomine™ Comprehensive Assay Plus (OCA Plus) Pan-Cancer Research Panel for comprehensive genomic profiling of solid tumors.
Nucleic Acids Res
January 2025
Department of Convergent Bioscience and Informatics, College of Bioscience and Biotechnology, Chungnam National University, 99, Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea.
Large genetic variants can be generated via homologous recombination (HR), such as polymerase theta-mediated end joining (TMEJ) or single-strand annealing (SSA). Given that these HR-based mechanisms leave specific genomic signatures, we developed GDBr, a genomic signature interpretation tool for DNA double-strand break repair mechanisms using high-quality genome assemblies. We applied GDBr to a draft human pangenome reference.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Phillip M. Drayer Electrical Engineering Department, Lamar University, Beaumont, TX 77705, USA.
Automated ultrasonic testing (AUT) is a critical tool for infrastructure evaluation in industries such as oil and gas, and, while skilled operators manually analyze complex AUT data, artificial intelligence (AI)-based methods show promise for automating interpretation. However, improving the reliability and effectiveness of these methods remains a significant challenge. This study employs the Segment Anything Model (SAM), a vision foundation model, to design an AI-assisted tool for weld defect detection in real-world ultrasonic B-scan images.
View Article and Find Full Text PDFFoods
December 2024
Institute of Bioengineering, Research Center of Biotechnology of the Russian Academy of Sciences, 119071 Moscow, Russia.
In winemaking, malolactic fermentation (MLF), which converts L-malic acid to L-lactic acid, is often applied after the alcoholic fermentation stage to improve the sensory properties of the wine and its microbiological stability. MLF is usually performed by lactic acid bacteria, which, however, are sensitive to the conditions of alcoholic fermentation. Therefore, the development of wine yeast strains capable of both alcoholic fermentation and MLF is an important task.
View Article and Find Full Text PDFNat Genet
January 2025
Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7-40.
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