Objectives Recently, several studies documented that de novo mutations (DNMs) play important roles in the aetiology of sporadic diseases. Next-generation sequencing (NGS) enables variant calling at single-base resolution on a genome-wide scale. However, accurate identification of DNMs from NGS data still remains a major challenge. We developed mirTrios, a web server, to accurately detect DNMs and rare inherited mutations from NGS data in sporadic diseases.

Methods The expectation-maximisation (EM) model was adopted to accurately identify DNMs from variant call files of a trio generated by GATK (Genome Analysis Toolkit). The GATK results, which contain certain basic properties (such as PL, PRT and PART), are iteratively integrated into the EM model to strike a threshold for DNMs detection. Training sets of true and false positive DNMs in the EM model were built from whole genome sequencing data of 64 trios.

Results With our in-house whole exome sequencing datasets from 20 trios, mirTrios totally identified 27 DNMs in the coding region, 25 of which (92.6%) are validated as true positives. In addition, to facilitate the interpretation of diverse mutations, mirTrios can also be employed in the identification of rare inherited mutations. Embedded with abundant annotation of DNMs and rare inherited mutations, mirTrios also supports known diagnostic variants and causative gene identification, as well as the prioritisation of novel and promising candidate genes.

Conclusions mirTrios provides an intuitive interface for the general geneticist and clinician, and can be widely used for detection of DNMs and rare inherited mutations, and annotation in sporadic diseases. mirTrios is freely available at http://centre.bioinformatics.zj.cn/mirTrios/.