Biology subjects

Oak, N.

Publications and source records attributed to Oak, N..

2 recordsLinked to original sources

bioRxiv2017

Landscape of human miRNA variation and conservation using Annotative Database of miRNA Elements, ADmiRE

MicroRNAs (miRNAs) are the most abundant class of non-coding RNAs that regulate expression of >60% genes and are frequently deregulated in many human diseases. Sequence variants in miRNAs are expected to have a high impact on miRNA function. However, the lack of miRNA variant annotation and prioritization guidelines has hampered this analysis from whole genome/exome sequencing (WGS/WES) studies. Through the development of an Annotative Database of miRNA Elements, ADmiRE workflow, we re-annotated the publicly available population dataset of gnomAD 15,596 WGS and 123,136 WES and describe 26,094 precursor-miRNA variants. AdmiRE annotates twice the miRNA variants predicted by existing tools which prioritize variation relative to protein coding regions. We provide the allele frequency distribution of miRNA variation which is comparable to variation in exonic regions. This distribution is similar for miRNAs located in the intragenic and intergenic genomic context. Moreover, high confidence miRNAs (designated by miRBase) harbor less variation (the majority contributed by rare variants) compared with the remaining miRNAs. We identify 279 miRNAs highly constrained with little or no variation in gnomAD. We further describe the evolutionary conservation of miRNAs across 100 vertebrates and identify 434 highly conserved miRNAs. We demonstrate that these constraint and conservation metrics (now incorporated into the ADmiRE workflow) characterize miRNAs previously implicated in human diseases. In conclusion, through the development of ADmiRE, we comprehensively analyze the landscape of miRNA sequence variation in large human population datasets and provide miRNA vertebrate conservation scores to aid future studies of miRNA variation in human diseases.

genomics↗

bioRxiv2017

Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

BackgroundThe American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.\n\nResultsUsing 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation where in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables like disease mechanism, gene constraint and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011-2017).\n\nConclusionsOur analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.

bioinformatics↗