DeNovoGear: De novo indel and point mutation discovery and phasing

Avinash Ramu, Michiel J. Noordam, Rachel S. Schwartz, Arthur Wuster, Matthew E. Hurles, Reed A. Cartwright, Donald F. Conrad

Research output: Contribution to journalArticle

74 Scopus citations

Abstract

We present DeNovoGear software for analyzing de novo mutations from familial and somatic tissue sequencing data. DeNovoGear uses likelihood-based error modeling to reduce the false positive rate of mutation discovery in exome analysis and fragment information to identify the parental origin of germ-line mutations. We used DeNovoGear on human whole-genome sequencing data to produce a set of predicted de novo insertion and/or deletion (indel) mutations with a 95% validation rate.

Original languageEnglish (US)
Pages (from-to)985-987
Number of pages3
JournalNature Methods
Volume10
Issue number10
DOIs
StatePublished - Oct 1 2013

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ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Cite this

Ramu, A., Noordam, M. J., Schwartz, R. S., Wuster, A., Hurles, M. E., Cartwright, R. A., & Conrad, D. F. (2013). DeNovoGear: De novo indel and point mutation discovery and phasing. Nature Methods, 10(10), 985-987. https://doi.org/10.1038/nmeth.2611