PhyloWGS

[…] estimation of the prevalence of a given clone can be inaccurate, and the corresponding heterogeneity results may be misleading. existing methods that capture snvs and cnas in the same model (i.e., phylowgs, spruce and canopy) require running a cna-calling algorithm before heterogeneity analysis, but accurate cna characterization also depends on heterogeneity analysis.table 1 , most existing […]

[…] of cells harboring each event. some tumor progression analysis tools combine vafs of snvs and population frequencies of structure variations to reconstruct subclonal composition and tumor evolution. phylowgs [] uses copy number alterations to correct the vafs of affected snvs and greatly improves subclonal reconstruction compared to existing methods. as tumor is a heterogeneity system, jiang et […]

[…] found to be non-aberrant by absolute (scnas were absent and thus could not be used to determine optimal solutions during the manual curation step of absolute) were excluded from analysis., we used phylowgs to reconstruct complete genotypes and phylogenetic relationships of tumor subpopulations from ccf values of ssnvs, indels, and scnas. phylowgs is capable of performing on both wes as well […]

[…] a more realistic model than synthetic data to assess the performance of different computational methods. in this work we present a comparison of the performance of pyclone, clomial, sciclone, and phylowgs (algorithms in bold in table ) on our ground truth datasets. we selected these four algorithms because they can be applied directly and simultaneously to all of our deep-targeted data […]

[…] and uses a dirichlet process prior over phylogenetic trees to group snvs into clones. it employs bayesian inference, based on mcmc sampling, to infer a distribution over possible evolutionary trees. phylowgs (deshwar et al., ) builds on phylosub and allows the reconstruction of tumor evolution from snvs and cnas obtained from whole genome sequencing data. citup (malikic et al., ) proposes […]