Provides a targeting model that defines the mutation locations and a nucleotide substitution model that determines the resulting mutation. Somatic HypeMutation proceeds by specifying relative rates at which DNA motifs in the Ig sequence are mutated or the probability of each base mutating to each of the other three possibilities as a function of the surrounding bases.
Provides flexible, interoperable, and easy-to-use tools that identify such signatures in cancer sequencing data. SomaticSignatures facilitates large-scale, cross-dataset estimation of mutational signatures, implements existing methods for pattern decomposition, supports extension through user-defined approaches and integrates with existing Bioconductor workflows.
A package to explore and visualize a collection of mutational patterns that are relevant for deciphering which mutational processes have been active in a sample. MutationalPatterns facilitates both (1) de novo mutational signature extraction and (2) quantification of the contribution of user-specified mutational signatures. While the first approach can be used to identify new mutational signatures, this is only meaningful for datasets with a large number of samples with diverse mutation spectra, as it relies on the dimensionality reduction method non-negative matrix factorization (NMF). The second approach can be used to study the activity of mutational processes in a single sample, and to further characterize previously-identified mutational signatures by assessing their contribution in different systems or under different conditions.
Permits users to analyze somatic signatures. YAPSA gathers a set of functions and routines to proceed. It allows signature analysis thanks to known signatures (LCD = linear combination decomposition) and to stratified mutational catalogue (SMC = stratify mutational catalogue). This tool provides a function to iteratively add information to an annotation data structure. It can group single nucleotide variants (SNVs) into 6 different categories.
Consists in tumor mutation management and machine learning analysis framework. Orchid allows users to manage, annotate, and analyze tumor mutations by integrating mutation data with popular databases. It accepts a wide assortment of feature types. This tool can annotate mutations from any region of the genome, allowing for the analysis of non-coding mutations. It offers convenience functions to assist the visualization and analysis models.
A software tool for inferring the mutational signatures present in a number of cancer mutation sets. Several independent mutational processes jointly produce the observed spectrum of mutations in a number of comparable tumours. EMu tries to find the number of processes present in a data set and their individual mutational signatures. These signatures contain information about the biological, physical or chemical processes active in cancer. EMu can also be used to localise regions in a cancer genome where a given set of mutational processes is active. EMu exploits the fact that the outcome of a mutational process not only depends on its signature, but also on the sequence on which it acts.
Offers variant annotation and statistical analyses of mutation patterns from genome-wide data obtained from deep sequencing experiments. MutSpec is based on the Galaxy open-source platform and can be utilized to analyze data from whole-exome, whole-genome or targeted sequencing experiments performed on human or mouse genomes. It suits also to discover complex mutational processes resulting from exogenous and endogenous carcinogenic insults.