CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

Kozlov, Alexey M; Alves, João M.; Stamatakis, Alexandros; Posada, David

doi:10.1186/s13059-021-02583-w

Cited by 45 publications

(44 citation statements)

References 101 publications

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“…Our finding that copy number profiles broadly support the TE-based phylogeny of S2 sublines suggests that the major Clades A and B we identify are not artifacts of our approach, and complements recent results showing that different types of genetic variation (SNP, TE, and local duplications) generate similar clustering of independently derived Drosophila cell line genomes ( Lewerentz et al 2022 ). Nevertheless, future work using other sources of genetic variation is worthwhile to cross-validate and resolve remaining uncertainties in the TE-based phylogeny of S2 sublines presented here, perhaps using extensions to methods developed for the analysis of single cell phylogenies ( Kozlov et al 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines

et al. 2022

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Cultured cells are widely used in molecular biology despite poor understanding of how cell line genomes change in vitro over time. Previous work has shown that Drosophila cultured cells have a higher transposable element (TE) content than whole flies, but whether this increase in TE content resulted from an initial burst of transposition during cell line establishment or ongoing transposition in cell culture remains unclear. Here we sequence the genomes of 25 sub-lines of Drosophila S2 cells and show that TE insertions provide abundant markers for the phylogenetic reconstruction of diverse sub-lines in a model animal cell culture system. DNA copy number evolution across S2 sub-lines revealed dramatically different patterns of genome organization that support the overall evolutionary history reconstructed using TE insertions. Analysis of TE insertion site occupancy and ancestral states support a model of ongoing transposition dominated by episodic activity of a small number of retrotransposon families. Our work demonstrates that substantial genome evolution occurs during long-term Drosophila cell culture, which may impact the reproducibility of experiments that do not control for sub-line identity.

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Section: Discussionmentioning

confidence: 99%

Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines

et al. 2022

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“…Our relaxation therefore only considers violations which should have an additional signal in the data beyond the infinite sites base model and typical sequencing noise. The relaxation is correspondingly more conservative than transition-based classical phylogenetic models adapted for SCS ( Kozlov et al , 2020 ; Zafar et al , 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…Binarized single-cell data have allowed us to test such assumptions, and find that it may be often violated in real tumour samples ( Kuipers et al , 2017b ). More complex phylogenetic models mitigating, or entirely avoiding the infinite sites assumption, have also been developed ( El-Kebir, 2018 ; Kozlov et al , 2020 ; Satas et al , 2020 ; Zafar et al , 2017 , 2019 ), though there is an apparent trade-off in model complexity between too simple models that cannot capture all relevant aspects of the evolutionary process, and too complex models that are prone to over-fitting or computationally too expensive to be learned efficiently from data. The existing models rely on processed data, where the mutations have already been called.…”

Section: Introductionmentioning

confidence: 99%

Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence

2022

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Motivation Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient's tumour composition and evolutionary history is key for personalised therapies. Single-cell sequencing now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, but brings with it challenges related to the particular noise profiles of the sequencing protocols as well as the complexity of the underlying evolutionary process. Results By modelling the noise processes and allowing mutations to be lost or to reoccur during tumour evolution, we present a method to jointly call mutations in each cell, reconstruct the phylogenetic relationship between cells, and determine the locations of mutational losses and recurrences. Our Bayesian approach allows us to accurately call mutations as well as to quantify our certainty in such predictions. We show the advantages of allowing mutational loss or recurrence with simulated data and present its application to tumour single-cell sequencing data. Availability SCIϕN is available at https://github.com/cbg-ethz/SCIPhIN Supplementary information Supplementary data are available at Bioinformatics online.

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“…Several recent studies have proposed various mathematical methods to infer mutation order (Fig 1C -1E) from data arising from somatic mutations (i.e., [16,18,[22][23][24][25]). Among these, we focus specifically on the methods of Jahn et al [22], Zafar et al [16], and El-Kebir [18], called SCITE, SiFit and SPhyR, respectively, as these methods use single-cell data for inference of the order in which mutations arise along a phylogeny as part of their estimation procedure.…”

Section: Plos Computational Biologymentioning

confidence: 99%

A phylogenetic approach to inferring the order in which mutations arise during cancer progression

et al. 2022

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Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.

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CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

Cited by 45 publications

References 101 publications

Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines

Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines

Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence

A phylogenetic approach to inferring the order in which mutations arise during cancer progression

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