An ensemble approach to accurately detect somatic mutations using SomaticSeq
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Updated
Jul 10, 2024 - Python
An ensemble approach to accurately detect somatic mutations using SomaticSeq
NeuSomatic: Deep convolutional neural networks for accurate somatic mutation detection
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…
Snakemake-based workflow for detecting structural variants in genomic data
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.
Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests
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.
accessory scripts for processing varscan somatic/copynumber outputs.
Clinical Whole Genome and Exome Sequencing Pipeline
ClairS - a deep-learning method for long-read somatic small variant calling
A Platypus-based variant calling pipeline for cancer data
Galaxy Tool Shed repositories maintained and developed by the Morin Lab.
Simulates somatic mutations, and calls statistically significant oncogenes and tumor suppressor genes based on a randomization-based test
highly-efficient & lightweight mutation signature matrix aggregation
A Platypus-based workflow for indel calling
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.
Pipeline for Somatic Variant Calling with WES and WGS data
ClairS-TO - a deep-learning method for tumor-only somatic variant calling
Snakemake based workflow for analysis of dnaseq data for calling germline/somatic mutations
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.
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