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Identifying, understanding, and correcting technical biases on the sex chromosomes in next-generation sequencing data
View ORCID ProfileTimothy H. Webster, Madeline Couse, View ORCID ProfileBruno M. Grande, Eric Karlins, View ORCID ProfileTanya N. Phung, View ORCID ProfilePhillip A. Richmond, View ORCID ProfileWhitney Whitford, View ORCID ProfileMelissa A. Wilson Sayres
doi: https://doi.org/10.1101/346940
Timothy H. Webster
1School of Life Sciences, Arizona State University
Madeline Couse
2Child and Family Research Institute, University of British Columbia
Bruno M. Grande
3Department of Molecular Biology and Biochemistry, Simon Fraser University
Eric Karlins
4Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health
Tanya N. Phung
5Interdepartmental Program in Bioinformatics, UCLA
Phillip A. Richmond
6Centre for Molecular Medicine and Therapeutics, University of British Columbia
7BC Children’s Hospital
Whitney Whitford
8School of Biological Sciences, The University of Auckland
Melissa A. Wilson Sayres
1School of Life Sciences, Arizona State University
9Center for Evolution and Medicine, Arizona State University
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Posted July 18, 2018.
Identifying, understanding, and correcting technical biases on the sex chromosomes in next-generation sequencing data
Timothy H. Webster, Madeline Couse, Bruno M. Grande, Eric Karlins, Tanya N. Phung, Phillip A. Richmond, Whitney Whitford, Melissa A. Wilson Sayres
bioRxiv 346940; doi: https://doi.org/10.1101/346940
Identifying, understanding, and correcting technical biases on the sex chromosomes in next-generation sequencing data
Timothy H. Webster, Madeline Couse, Bruno M. Grande, Eric Karlins, Tanya N. Phung, Phillip A. Richmond, Whitney Whitford, Melissa A. Wilson Sayres
bioRxiv 346940; doi: https://doi.org/10.1101/346940
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