1 - 50 of 68 results


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Infers tumor purity and malignant cell ploidy directly from analysis of somatic DNA alterations. ABSOLUTE can detect subclonal heterogeneity, somatic homozygosity, and calculate statistical sensitivity to detect specific aberrations. It provides a foundation for integrative genomic analysis of cancer genome alterations on an absolute (cellular) basis. It may be possible to extend ABSOLUTE to other types of genomic alterations, such as structural rearrangements and small insertions/deletions.


A tool for inferring the cellular frequency of point mutations from deeply sequenced data. The model supports simultaneous analysis of multiple related samples and infers clusters of mutations whose cellular prevalences shift together. Such clusters of mutations can be inferred as mutational genotypes of distinct clonal populations. The input data for PyClone consists of a set read counts from a deep sequencing experiment, the copy number of the genomic region containing the mutation and an estimate of tumour content.


Provides quantitative variant callers for detecting subclonal mutations in ultra-deep sequencing experiments. DeepSNV is a comparative targeted deep-sequencing approach combined with a customised statistical algorithm, which can detect and quantify subclonal single-nucleotide variants (SNVs) in mixed populations. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and the shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters.

ExPANdS / Expanding Ploidy and Allele Frequency on Nested Subpopulations

Characterizes coexisting subpopulations in a single tumor sample using copy number and allele frequencies derived from exome- or whole genome sequencing input data. The model detects coexisting genotypes by leveraging run-specific tradeoffs between depth of coverage and breadth of coverage. ExPANdS predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation and tumor purity. The main function runExPANdS provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers. The robustness of the subpopulation predictions by ExPANdS increases with the number of mutations provided. It is recommended that at least 200 mutations are used as input to obtain stable results.


An intuitive representation of purity, allele-specific copy number, and clonality for human tumor specimens. BubbleTree displays the clonal composition within a tumor at the genomic segment level with allele-specific copy number – a granular quality that is not provided by other tools used in NGS data analysis. Further, these estimates can be obtained simply by manual inspection of the BubbleTree graph. For larger patient studies, we developed a heuristic model to automate the predictions and provide a more accurate estimate (than that provided by visual inspection). The robust performance of the BubbleTree framework is primarily attributed to the use of both R scores and BAFs of the heterozygous germline loci and the three-step implementation.

MEDICC / Minimum Event Distance for Intra-tumour Copy-number Comparisons

A method for phylogenetic reconstruction and heterogeneity quantification based on a minimum event distance for intra-tumour copy-number comparisons. Given multiple such evolutionarily-related copy-number profiles, for example from distinct primary and metastatic sites of the same patient, phylogenetic inference in MEDICC then involves three steps: (i) allele-specific assignment of major and minor copy-numbers, (ii) estimation of evolutionary distances between samples followed by tree inference and (iii) reconstruction of ancestral genomes. The MEDICC algorithms are independent of the experimental techniques used and are applicable to both next-generation sequencing and array CGH data.

GRAFT / Genomic Rearrangement Assembly For Tumours

A technique to help reconstruct the history of rearrangements responsible for cancer genome karyotypes. This uses allelic copy number segmentation, rearrangements, and somatic single-nucleotide mutation distributions, and so is based entirely on the final observed portfolio of mutations. The simplest application of this method is to construct digital karyotypes with path-walking techniques that have classically required chromosomal painting.


A probabilistic framework to reconstruct intra-tumor evolutionary pathways. The statistical model is based on simultaneously assigning markers of evolution to clones, which are represented as both inner nodes and leaves of a phylogenetic tree, and on learning the topology and the parameters of the tree. We use a tree-structured stick-breaking process (TSSB) to construct a prior probability of trees and a Markov chain Monte Carlo (MCMC) inference scheme for sampling from the joint posterior. The relationships between parent and child nodes are derived from a classical phylogeny model.

ALOHA / Allele-frequency/Loss-Of-Heterozygosity/Allele-imbalance

Extracts hidden genetic information and broaden the potential application of allele frequency to genomic research. ALOHA can detect loss of heterozygosity (LOH) and recognize allelic imbalance (AI). It can be useful for distinguishing genetic differences among ethnic populations. This tool can analyze data from DNA samples reflecting clonal heterogeneity and containing DNA from contaminating “normal” cells, which is often the case in cancer studies.

CLONET / CLONality Estimate in Tumors

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Quantifies the percentage of reads supporting a considered aberration from clinical tumors. CLONET uses the abundant germline heterozygous SNP genotype data provided by whole genome sequence coverage by exploiting individuals’ genetic background. It allows to compare tumors types of the same aberration class and different aberrations within the same tumor type. The tool is based on a local optimization where estimates of purity and ploidy are derived from few clonal events.


A software package that uses paired tumor-normal DNA sequencing data to estimate tumor cellularity and ploidy, and to calculate allele-specific copy number profiles and mutation profiles. Comparison between Sequenza/exome and SNP/ASCAT revealed strong correlation in cellularity (Pearson's r = 0.90) and ploidy estimates (r = 0.42, or r = 0.94 after manual inspecting alternative solutions). This performance was noticeably superior to previously published algorithms. In addition, in artificial data simulating normal-tumor admixtures, Sequenza detected the correct ploidy in samples with tumor content as low as 30%.


Clusters variants into clones. QuantumClone applies an expectation-maximization (EM) algorithm and allows for accurate inference of clonal structure using Variant Allele Frequencies (VAFs) from one or several tumor samples sequenced using whole genome sequencing (WGS). It can analyze variants coming from highly rearranged and hyper-diploid cancer genomes. It was also completed with a robust framework for the functional assessment of mutations based on signaling pathway analysis combined with the clonal assignment.


A tool for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and simple to execute workflows that can scale to thousands of samples and can be easily incorporated into existing variant calling pipelines.


Examines somatic variation events (such as copy number changes, loss of heterozygosity, or point mutations) in order to identify the underlying subclone structure, i.e. the subclones including the normal (non-cancerous) cells and their cellular frequencies within the tumor tissue. In contrast to other methods that require SNV allele frequencies, Subcloneseeker is able to analyze many different types of genomic variant data, as long as allele frequency measurements can be converted into cell prevalence values.


Incorporates information from multiple sections from a single tumor at a single time point to infer the frequencies and genotypes for a specified number of clones. An important difference between Clomial and many other methods based on clustering is our explicit probabilistic modeling of the random selection of normal and variant alleles during sequencing, according to a binomial distribution. By taking into account not just the relative frequency of the two alleles but the separate counts of normal and variant alleles, our model automatically assigns less importance to a locus with lower coverage, even if the locus yields the same variant allele frequency as a high-coverage locus. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.

scploid / Single Cell RNA-seq Aneuploidy Caller

Provides an approach for aneuploidies calling in single-cell RNA-sequencing. scploid is an R package performing for each cell, the identification of chromosomes including genes with potentially detected deviant expression, by applying a statistical method. It aims to supplies a straightforward and easy to interpret method for stem cell and embryonic research as well as assists users in determining genes possibly associated with copy number aberrations.


Estimates tumor purity, copy number, loss of heterozygosity (LOH), and status of single nucleotide variants (SNVs). PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for tumor samples without matching normal samples. It integrates standard GATK-based pipelines, utilizes standard Bioconductor infrastructure for data import and export, supports both matched and unmatched samples, and was tested on targeted panels.