Our latest paper detailing new methods in DeNovoGear was published in Nature Methods today.

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.

Citation: Ramu et al. (2013) DeNovoGear: de novo indel and point mutation discovery and phasing. Nature Methods 10:985–987. doi: 10.1038/nmeth.2611.