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Permits to analyze any given set of mutations, and determines the contributions of predefined annotated mutational signatures. MutaGene is an online computational framework that constructs DNA context-dependent mutational profiles. This method can identify the cancer type, primary tumor site and cohorts of patients with similar mutagenic processes. It also derives signatures from major cancer whole exome and genome sequencing studies available through the International Cancer Genome Consortium (ICGC) and the Cancer Genome Atlas (TCGA) repositories.


Serves to decompose a patient mutational profile into a linear combination of pre-defined mutational signatures. SignatureEstimation can give a profile tumor with the frequencies or the proportions of 96 mutational context types. This tool decomposes the tumor catalogue into the known signatures with sure intensities of signature exposures. It chooses decompose method quadratic programming or simulated annealing, and presents the bootstrap distribution or simulated annealing distribution of the exposures.

RGIFE / Ranked Guided Iterative Feature Elimination

Removes blocks of attributes rather than in a static approach. RGIFE is a heuristic method for the identification of small sets of highly predictive biomarkers. It provides higher performance in terms of: (i) prediction accuracy, (ii) size of the selected signatures, and (iii) computational time. It also can work with any (-omics) dataset as long as the samples are associated with discrete classes. Finally, RGIFE can be used with any classifier that is able to provide an attribute ranking after the training process.

pmsignature / Probabilistic Mutation signature

Methods for modelling, identifying and visualizing such mutation signatures. Pmsignature simplifies mutation signature models compared with existing approaches and reducing the number of parameters by orders of magnitude even while increasing the contextual factors that are accounted for. This improves both sensitivity and robustness of inferred signatures. It also provides an intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites.

Emu / Expectation-Maximisation inference of mutational signatures

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.

YAPSA / Yet Another Package for Signature Analysis

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.