Tumor purity and clonality estimation software tools | Whole-genome sequencing data analysis
Solid tumor samples typically contain multiple distinct clonal populations of cancer cells, and also stromal and immune cell contamination. A majority of the cancer genomics and transcriptomics studies do not explicitly consider genetic heterogeneity and impurity, and draw inferences based on mixed populations of cells. Deconvolution of genomic data from heterogeneous samples provides a powerful tool to address this limitation.
Predicts malignant tumors. BP neural network consists of an artificial neural network trained by error back propagation algorithm. It can estimate the related degree of risk factors and obtain the rank of each degree of correlation. This tool is useful for the precocious detection and diagnosis of cancer. It can equally serve to improve the survival rate of cancer in humans.
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.
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.
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.
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%.
A method that can be applied to WGS data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. Unlike all previous methods, PhyloWGS appropriately corrects SSM population frequencies in regions overlapping CNVs and is fast enough to perform reconstruction of at least five cancerous subpopulations based on thousands of mutations.
A framework for inferring models of tumor progression from single-cell gene copy number data, including variable rates for different gain and loss events. Application of FISHtrees to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature. Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival.