SomatoSim: precision simulation of somatic single nucleotide variants.

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 .

Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach.

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.

Activation of Superficial and Deep Finger Flexors Through Transcutaneous Nerve Stimulation.

OBJECTIVE: Functional electrical stimulation (FES) is a common technique to elicit muscle contraction and help improve muscle strength. Traditional FES over the muscle belly typically only activates superficial muscle regions. In the case of hand FES, this prevents the activation of the deeper flexor muscles which control the distal finger joints. Here, we evaluated whether an alternative transcutaneous nerve-bundle stimulation approach can activate both superficial and deep extrinsic finger flexors using a high-density stimulation grid. METHODS: Transverse ultrasound of the forearm muscles was used to obtain cross-sectional images of the underlying finger flexors during stimulated finger flexions and kinematically-matched voluntary motions. Finger kinematics were recorded, and an image registration method was used to capture the large deformation of the muscle regions during each flexion. This deformation was used as a surrogate measure of the contraction of muscle tissue, and the regions of expanding tissue can identify activated muscles. RESULTS: The nerve-bundle stimulation elicited contractions in the superficial and deep finger flexors. Both separate and concurrent activation of these two muscles were observed. Joint kinematics of the fingers also matched the expected regions of muscle contractions. CONCLUSIONS: Our results showed that the nerve-bundle stimulation technique can activate the deep extrinsic finger flexors, which are typically not accessible via traditional surface FES. SIGNIFICANCE: Our nerve-bundle stimulation method enables us to produce the full range of motion of different joints necessary for various functional grasps, which could benefit future neuroprosthetic applications.