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ABSOLUTE
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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.
PyClone
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.
E-scape / evolutionary landscapes
Renders complex relationships between cancer evolution data in an intuitive, interactive framework for biomedical investigators. E-scape is composed of three visualization tools: TimeScape, MapScape, CellScape. It permits to study the dynamics of disease progression by combining all components needed. The can be useful for researchers engaged effectively with cancer evolution data sets. It will participate to the understanding of clonal evolution in cancer towards translation into the clinical domain.
PurityEst
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Estimates the fraction of tumor DNA molecules that is different from the normal matched tissue. PurityEst is a method that gives a purity estimate from somatic mutations in each chromosome and takes an average of the chromosome-wide estimates to be the purity estimate of the tumor tissue. The software can be used for determining tumor purity based on mutant allele fractions in a mixture of a tumor clone and a normal clone.
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.
deepSNV
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.
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.
Canvas
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.
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.
Subcloneseeker
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.
BitPhylogeny
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.
BubbleTree
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.
Sequenza
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%.
SPRUCE / Somatic Phylogeny Reconstruction Using Combinatorial Enumeration
Infers a multi-state perfect phylogeny describing the evolutionary history of the somatic mutations (Single-Nucleotide Variations (SNV)s and Copy-Number Aberrations (CNAs)) of a tumor given multi-sample bulk sequencing data. SPRUCE addresses complexities in simultaneous analysis of SNVs and CNAs. Importantly, this tool relies on the infinite alleles, or no-homoplasy, assumption. Finally, SPRUCE gives additional insights into intra-tumor heterogeneity.
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.
QuantumClone
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.
MARATHON / copy nuMber vARiAtion and Tumor pHylOgeNy
Enables copy number profiling and downstream analyses in disease genetic studies. MARATHON is a pipeline that gathers statistical software: CODEX and CODEX2 perform read depth normalization for total copy number profiling, iCNV receives read depth normalized by CODEX/CODEX2, FALCON and FALCON-X perform allele-specific copy number (ASCN) analysis and Canopy receives input from FALCON/FALCON-X to perform tumor phylogeny reconstruction. The pipeline adapts to different study designs and research goals.
SciClone
Infers the subclonal architecture of tumors. SciClone is a method for estimating the number and content of subclones across one or many samples. It focuses primarily on variants in copy-number neutral (CNN), loss of heterozygosity (LOH)-free portions of the genome, which allows for the highest-confidence quantification of variant allele frequencies (VAF) and inference of clonality. Application of SciClone to primary and relapse acute myeloid leukemia (AML) tumors identified subclonal populations with dramatically divergent response to conventional therapy.
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.
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.
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