Fetal copy number variation detection software tools | Whole-genome sequencing data analysis
The past several years have seen the development of methodologies to identify genomic variation within a fetus through the non-invasive sequencing of maternal blood plasma. These methods are based on the observation that maternal plasma contains a fraction of DNA (typically 5–15%) originating from the fetus, and such methodologies have already been used for the detection of whole-chromosome events (aneuploidies), and to a more limited extent for smaller (typically several megabases long) copy number variants (CNVs).
A probabilistic method for the identification of de novo CNVs from maternal blood plasma sequencing. fCNV combines three types of data: allelic ratios, reflecting the changes in the expected observations of various alleles at SNP positions in the presence of the CNV; phasing information, allowing for the combining of allelic ratios across multiple SNP positions, thus improving the signal-to-noise ratio; and depth of coverage information reflecting the change in expected sequencing depth in the presence of the CNV. We apply the resulting method to simulated sequencing data, demonstrating promising results for CNVs >400 kb in length, and especially for CNVs of paternal origin.
Permits users to identify subchromosomal copy number variants (CNVs) in a fetal genome from cell-free DNA (cfDNA) fragment size information. FSDA is a method for non-invasive prenatal CNV prediction. For a given target region, this model identifies a set of control regions with similar fragment size distribution characteristics and utilizes this set of controls for predicting the fetal copy number in the target region.
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