Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and…

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. Source text: Shahrabi Farahani and Lagergren, 2013.

Simulates a model for tumour evolution that shows how short-range dispersal and…

Simulates a model for tumour evolution that shows how short-range dispersal and cell turnover can account for rapid cell mixing inside the tumour. TumourSimulator model shows that even a small…

Allows the identification of mutational signatures within a single tumor…

Allows the identification of mutational signatures within a single tumor sample. The deconstructSigs approach determines the linear combination of pre-defined signatures that most accurately…

A generative probabilistic model for detecting patterns of various degrees of…

A generative probabilistic model for detecting patterns of various degrees of mutual exclusivity across genetic alterations, which can indicate pathways involved in cancer progression. TiMEx…

An open-source R package that implements the state-of-the-art algorithms for…

An open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract…

Allows tumor growth simulation in C++. tumopp offers many setting options so…

Allows tumor growth simulation in C++. tumopp offers many setting options so that simulations can be carried out under various settings. Setting options include how the cell division rate is…

Deduces from cross-sectional data of genetic alterations in tumor patients the…

Deduces from cross-sectional data of genetic alterations in tumor patients the causal dependencies and the waiting times among these genetic events. From matrices with genetic events and patient…

Relies on a scoring method based on a probabilistic theory developed by Suppes,…

Relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy…

Offers an environment for estimating the mutagenetic trees mixture models from…

Offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. Rtreemix includes functions for fitting the trees mixture…

A comprehensive and expandable simulation tool to visualizing tumor growth.…

A comprehensive and expandable simulation tool to visualizing tumor growth. CPM-Cytoscape offers the advantage of a user-friendly graphical interface with several manipulable input variables to…

An approach for modelling the dependences between genetic changes in human…

An approach for modelling the dependences between genetic changes in human tumours. oncomodel computes probabilistic tree models for oncogenesis based on genetic data using maximum likelihood. In…

A clustering method for cancer sub-clonal evolutionary trees, in which…

A clustering method for cancer sub-clonal evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. phyC can detect true clusters with…

Reconstructs evolutionary paths and ancestral genotypes from sequenced tumor…

Reconstructs evolutionary paths and ancestral genotypes from sequenced tumor samples. BML first estimates the probability P(g) that a particular combination of mutations (denoted by genotype g)…

A specific probabilistic graphical model for the accumulation of mutations and…

A specific probabilistic graphical model for the accumulation of mutations and their interdependencies. The Bayesian network models cancer progression by an explicit unobservable accumulation process…

Estimating oncogenetic trees. Oncotree contains functions to construct and…

Estimating oncogenetic trees. Oncotree contains functions to construct and evaluate directed tree structures that model the process of occurrence of genetic alterations during carcinogenesis.

A comprehensive resource for three major efficacious cancer biomarkers i.e.…

A comprehensive resource for three major efficacious cancer biomarkers i.e. integration and breakpoint events, human papillomaviruses (HPVs) methylation patterns and HPV mediated aberrant expression…

A software package to estimate mixture models of mutagenetic trees from…

A software package to estimate mixture models of mutagenetic trees from observed cross-sectional data. Mutagenetic tree mixtures are probabilistic models that have been designed to describe…

A software tool for learning disease progression networks by reducing to mixed…

A software tool for learning disease progression networks by reducing to mixed integer linear programming (MILP). In contrast to the semi-automatic method, DiProg is automatic and, therefore, needs…

Performs mixture type separation on a tumor sample. Unmix approach uses…

Performs mixture type separation on a tumor sample. Unmix approach uses unmixing of tumor samples to assist in phylogenetic inference of cancer progression pathways. This unmixing method adapts the…

This tool is used to generate hidden-variable oncogenetic trees (HOTs). The…

This tool is used to generate hidden-variable oncogenetic trees (HOTs). The trees are written using standard Newick notation, except that each vertex in the tree has a comment that describes the HOT…