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ZygosityPredictor

Authors :
Marco Rheinnecker
Martina Fröhlich
Marc Rübsam
Nagarajan Paramasivam
Christoph E. Heilig
Stefan Fröhling
Richard F. Schlenk
Barbara Hutter
Daniel Hübschmann
Publication Year :
2023
Publisher :
Cold Spring Harbor Laboratory, 2023.

Abstract

SummaryZygosityPredictor provides functionality to evaluate how many copies of a gene are affected by mutations in next generation sequencing data. In cancer samples, both somatic and germline mutations are accurately processed. In particular, ZygosityPredictor computes the number of affected copies for single nucleotide variants and small insertions and deletions (Indels). In addition, the tool integrates information at gene level by performing haplotype phasing and subsequent logic to derive how strongly a gene is affected by mutations. This information is of particular interest in precision oncology, e.g. when assessing whether unmutated copies of tumor-suppressor genes remain.Availability and implementationZygosityPredictor was implemented as an R-package and is available via Bioconductor athttps://bioconductor.org/packages/ZygosityPredictor. Detailed documentation is provided in the vignette including application to an example genome.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........cedeef3dd3d6d1ba5562d9b4914cc59c
Full Text :
https://doi.org/10.1101/2023.03.09.531877