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.
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Acknowledgements
We thank R. Hardwick for assistance with primer design, V. Plagnol and H. Li for helpful discussion, and members of the 1000 Genomes community for generating software, data and resources that we used as part of this project. This research was supported in part by Wellcome Trust grant WT098051.
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Contributions
A.R. implemented methods, analyzed data and wrote the paper; M.J.N. performed validation experiments, analyzed data and wrote the paper; R.S.S. performed simulations; A.W. provided code and performed early analysis demonstrating the utility of beta-binomials; M.E.H. and R.A.C. gave conceptual advice, supervised the project and wrote the paper; D.F.C. designed and supervised the project, implemented methods, analyzed data and wrote the paper.
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The authors declare no competing financial interests.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–11, Supplementary Tables 1–8 and Supplementary Note (PDF 4860 kb)
Supplementary Table 9
Location and annotation of 56 de novo indels for which validation was attempted. (XLS 50 kb)
Supplementary Software
DeNovoGear software. (ZIP 14779 kb)
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Ramu, A., Noordam, M., Schwartz, R. et al. DeNovoGear: de novo indel and point mutation discovery and phasing. Nat Methods 10, 985–987 (2013). https://doi.org/10.1038/nmeth.2611
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DOI: https://doi.org/10.1038/nmeth.2611
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