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A machine-learning approach for accurate detection of copy-number variants from exome sequencing
[article]
2018
bioRxiv
pre-print
Copy-number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome sequencing data are limited by high false positive rates and low concordance due to the inherent biases of individual algorithms. To overcome these issues, calls generated by two or more algorithms are often intersected using Venn-diagram approaches to identify "high-confidence" CNVs. However, this
doi:10.1101/460931
fatcat:tzghlthtjnc7pkcoiqjujp47ti