BACKGROUND: The widespread use of next-generation sequencing has identified an important role for somatic mosaicism in many diseases. However, detecting low-level mosaic variants from next-generation sequencing data remains challenging. RESULTS: Here, we present a method for Position-Based Variant Identification (PBVI) that uses empirically-derived distributions of alternate nucleotides from a control dataset. We modeled this approach on 11 segmental overgrowth genes. We show that this method improves detection of single nucleotide mosaic variants of 0.01-0.05 variant allele fraction compared to other low-level variant callers. At depths of 600 × and 1200 ×, we observed > 85% and > 95% sensitivity, respectively. In a cohort of 26 individuals with somatic overgrowth disorders PBVI showed improved signal to noise, identifying pathogenic variants in 17 individuals. CONCLUSION: PBVI can facilitate identification of low-level mosaic variants thus increasing the utility of next-generation sequencing data for research and diagnostic purposes.

BACKGROUND: Somatic single nucleotide variants have gained increased attention because of their role in cancer development and the widespread use of high-throughput sequencing techniques. The necessity to accurately identify these variants in sequencing data has led to a proliferation of somatic variant calling tools. Additionally, the use of simulated data to assess the performance of these tools has become common practice, as there is no gold standard dataset for benchmarking performance. However, many existing somatic variant simulation tools are limited because they rely on generating entirely synthetic reads derived from a reference genome or because they do not allow for the precise customizability that would enable a more focused understanding of single nucleotide variant calling performance. RESULTS: SomatoSim is a tool that lets users simulate somatic single nucleotide variants in sequence alignment map (SAM/BAM) files with full control of the specific variant positions, number of variants, variant allele fractions, depth of coverage, read quality, and base quality, among other parameters. SomatoSim accomplishes this through a three-stage process: variant selection, where candidate positions are selected for simulation, variant simulation, where reads are selected and mutated, and variant evaluation, where SomatoSim summarizes the simulation results. CONCLUSIONS: SomatoSim is a user-friendly tool that offers a high level of customizability for simulating somatic single nucleotide variants. SomatoSim is available at https://github.com/BieseckerLab/SomatoSim .

Pharmacogenetics (PG) examines gene variations for drug disposition, response, or toxicity. At the National Institutes of Health Clinical Center (NIH CC), a multidepartment Pharmacogenetics Testing Implementation Committee (PGTIC) was established to develop clinical decision support (CDS) algorithms for abacavir, carbamazepine, and allopurinol, medications for which human leukocyte antigen (HLA) variants predict severe hypersensitivity reactions. Providing PG CDS in the electronic health record (EHR) during order entry could prevent adverse drug events. Medical Logic Module (MLM) programming was used to implement PG CDS in our EHR. The MLM checks to see if an HLA sequence-based gene test is ordered. A message regarding test status (result present, absent, pending, or test not ordered) is displayed on the order form, and the MLM determines if the prescriber can place the order, place it but require an over-ride reason, or be blocked from placing the order. Since implementation, more than 725 medication orders have been placed for over 230 patients by 154 different prescribers for the three drugs included in our PG program. Prescribers commonly used an over-ride reason when placing the order mainly because patients had been receiving the drug without reaction before implementation of the CDS program. Successful incorporation of PG CDS into the NIH CC EHR required a coordinated, interdisciplinary effort to ensure smooth activation and a positive effect on patient care. Prescribers have adapted to using the CDS and have ordered PG testing as a direct result of the implementation.

UNLABELLED: VarSifter is a graphical software tool for desktop computers that allows investigators of varying computational skills to easily and quickly sort, filter, and sift through sequence variation data. A variety of filters and a custom query framework allow filtering based on any combination of sample and annotation information. By simplifying visualization and analyses of exome-scale sequence variation data, this program will help bring the power and promise of massively-parallel DNA sequencing to a broader group of researchers. AVAILABILITY AND IMPLEMENTATION: VarSifter is written in Java, and is freely available in source and binary versions, along with a User Guide, at http://research.nhgri.nih.gov/software/VarSifter/.