Personalized Genomics and Proteomics. Main diet: Ensembl, side dishes: SNPs

An ensemble approach to accurately detect somatic mutations using SomaticSeq

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.

Snakemake-based workflow for detecting structural variants in genomic data

Python package to annotate and visualize gene fusions.

A quickstart tool for AmpliconArchitect. Performs all preliminary steps (alignment, CNV calling, seed interval detection) required prior to running AmpliconArchitect. Previously called PrepareAA.

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.

Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data

Improving Personalized Prediction of Cancer Prognoses with Clonal Evolution Models

🐍 DRAGEN Tumor/Normal workflow post-processing

Microsatellite Instability Classification using Multiple Instance Learning

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.

Tools for producing pseudo-cgh of next-generation sequencing data

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.

A Graph Neural Network Model for prediction of the effectiveness of a drug on a given cancer cell lines

Reconstructs complex variation using Bionano optical mapping data and breakpoint graph data

A unified downloader+preprocessor for cancer genomics datasets

Predictive models and analysis of cancer prognosis and drug response using primary tumor microbial abundances derived from WGS and RNA-seq sequencing data for 32 TCGA cancers (Poore et al. Nature 2020), including equivalent models using TCGA RNA-seq gene expression and combined microbial abundance and gene expression for comparison.

Map single-cell transcriptomes to copy number evolutionary trees.