SUMMARY: Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files and SV annotation. We demonstrate that Viola can extract biologically meaningful SV signatures from publicly available SV data for cancer and we evaluate the computational time necessary for annotation of the data. AVAILABILITY AND IMPLEMENTATION: Viola is available on pip (https://pypi.org/project/Viola-SV/) and the source code is on GitHub (https://github.com/dermasugita/Viola-SV). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In a robotic cell, an assembly robot has to grasp various parts robustly even under some uncertainties in their initial poses. For this purpose, it is necessary to design robust grasping strategies for robotic hands. In this paper, we propose a method to derive an optimal robust grasping strategy from a given initial pose error region of a target object. Based on the pushing operation analysis, it is possible to simulate multi-fingered hand grasping and derive a permissible initial pose error region of a target object from which planned grasping is successful. Adopting an active search algorithm proposed by the authors, we can find the optimal grasping strategy efficiently. As an example, we derive the optimal grasping strategy for grasping a circular object by a three-fingered hand.