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- W3089129286 abstract "Abstract Detection of copy number variations (CNVs) is essential for uncovering genetic factors underlying human diseases. However, CNV detection by current methods is prone to error, and precisely identifying CNVs from paired-end whole genome sequencing (WGS) data is still challenging. Here, we present a framework, CNV-JACG, for Judging the Accuracy of CNVs and Genotyping using paired-end WGS data. CNV-JACG is based on a random forest model trained on 21 distinctive features characterizing the CNV region and its breakpoints. Using the data from the 1000 Genomes Project, Genome in a Bottle Consortium, the Human Genome Structural Variation Consortium and in-house technical replicates, we show that CNV-JACG has superior sensitivity over the latest genotyping method, SV2, particularly for the small CNVs (≤1 kb). We also demonstrate that CNV-JACG outperforms SV2 in terms of Mendelian inconsistency in trios and concordance between technical replicates. Our study suggests that CNV-JACG would be a useful tool in assessing the accuracy of CNVs to meet the ever-growing needs for uncovering the missing heritability linked to CNVs." @default.
- W3089129286 created "2020-10-01" @default.
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- W3089129286 date "2020-09-01" @default.
- W3089129286 modified "2023-10-17" @default.
- W3089129286 title "A random forest-based framework for genotyping and accuracy assessment of copy number variations" @default.
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- W3089129286 doi "https://doi.org/10.1093/nargab/lqaa071" @default.
- W3089129286 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7671382" @default.
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- W3089129286 hasPublicationYear "2020" @default.
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