SUMMARY: Advances in high-throughput DNA sequencing technologies and decreasing costs have fueled the identification of small genetic variants (such as single nucleotide variants and indels) across tumors. Despite efforts to standardize variant formats and vocabularies, many sources of variability persist across databases and computational tools that annotate variants, hindering their integration within cancer genomic analyses. In this context, we present OpenVariant, an easily extendable Python package that facilitates seamless reading, parsing and refinement of diverse input file formats in a customizable structure, all within a single process. AVAILABILITY AND IMPLEMENTATION: OpenVariant is an open-source package available at https://github.com/bbglab/openvariant. Documentation may be found at https://openvariant.readthedocs.io.

SUMMARY: The generation of large volumes of omics data to conduct exploratory studies has become feasible and is now extensively used to gain new insights in life sciences. The effective exploration of the generated data by experts is a crucial step for the successful extraction of knowledge from these datasets. This requires availability of intuitive and interactive visualization tools that can display complex data. Matrix heatmaps are graphical representations frequently used for the description of complex omics data. Here, we present jHeatmap, a web-based tool that allows interactive matrix heatmap visualization and exploration. It is an adaptable javascript library designed to be embedded by means of basic coding skills into web portals to visualize data matrices as interactive and customizable heatmaps. AVAILABILITY: jHeatmap is freely available at the GitHub code repository at https://github.com/jheatmap/jheatmap. Working examples and the documentation may be found at http://jheatmap.github.io/jheatmap.