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SigProfilerExtractor allows de novo extraction of mutational signatures from data generated in a matrix format. The tool identifies the number of operative mutational signatures, their activities in each sample, and the probability for each signature to cause a specific mutation type in a cancer sample. The tool makes use of SigProfilerMatrixGen…
SigProfilerMatrixGenerator creates mutational matrices for all types of somatic mutations. It allows downsizing the generated mutations only to parts for the genome (e.g., exome or a custom BED file). The tool seamlessly integrates with other SigProfiler tools.
SigProfilerPlotting provides a standard tool for displaying all types of mutational signatures as well as all types of mutational patterns in cancer genomes. The tool seamlessly integrates with other SigProfiler tools.
SigProfilerTopography allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures.
SigProfilerSimulator allows realistic simulations of mutational patterns and mutational signatures in cancer genomes. The tool can be used to simulate signatures of single point mutations, double point mutations, and insertion/deletions. Further, the tool makes use of SigProfilerMatrixGenerator and SigProfilerPlotting.
An R wrapper for SigProfilerExtractor that allows de novo extraction of mutational signatures from data generated in a matrix format. The tool identifies the number of operative mutational signatures, their activities in each sample, and the probability for each signature to cause a specific mutation type in a cancer sample. The tool makes use o…
Tool for analyzing the inter-mutational distances between SNV-SNV and INDEL-INDEL mutations. Tool separates mutations into clustered and non-clustered groups on a sample-dependent basis.
Comprehensive cancer signatures with reusable modules written in python, integrating SNV, SV and MSI profiles in signatures decomposed using non-negative matrix factorisation, and produce production ready pdf reports.
As part of an overall strategy for improving therapies for childhood cancers, the PPTC seeks to develop models for the types of tumors that will be encountered in early phase clinical testing by establishing patient derived xenografts (PDXs) from high-risk childhood cancers refractory to current standard of care treatments. Genomic profiling of …