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Article: A zero-agnostic model for copy number evolution in cancer.

Schmidt, Henri / Sashittal, Palash / Raphael, Benjamin J

bioRxiv : the preprint server for biology

2023

Abstract: Motivation: New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling ... ...

Abstract	Motivation: New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such Results: We introduce the
Language	English
Publishing date	2023-04-12
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.10.536302
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Article ; Online: A zero-agnostic model for copy number evolution in cancer.

Schmidt, Henri / Sashittal, Palash / Raphael, Benjamin J

PLoS computational biology

2023 Volume 19, Issue 11, Page(s) e1011590

Abstract	Motivation: New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. Copy number aberrations alter multiple adjacent genomic loci, violating the standard phylogenetic assumption that loci evolve independently. Thus, specialized models to infer copy number phylogenies have been introduced. A widely used model is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results: We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.
MeSH term(s)	Humans ; Phylogeny ; DNA Copy Number Variations/genetics ; Neoplasms/genetics ; Genomics/methods ; Genome ; Algorithms
Language	English
Publishing date	2023-11-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011590
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Article ; Online: A zero-agnostic model for copy number evolution in cancer.

Henri Schmidt / Palash Sashittal / Benjamin J Raphael

PLoS Computational Biology, Vol 19, Iss 11, p e

2023 Volume 1011590

Abstract: Motivation New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling ... ...

Abstract	Motivation New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. Copy number aberrations alter multiple adjacent genomic loci, violating the standard phylogenetic assumption that loci evolve independently. Thus, specialized models to infer copy number phylogenies have been introduced. A widely used model is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number ...
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
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Article: ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model.

Sashittal, Palash / Zhang, Haochen / Iacobuzio-Donahue, Christine A / Raphael, Benjamin J

bioRxiv : the preprint server for biology

2023

Abstract: Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor ... ...

Abstract	Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent Code availability: Software is available at https://github.com/raphael-group/constrained-Dollo.
Language	English
Publishing date	2023-01-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.05.522408
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Article ; Online: Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing.

Sashittal, Palash / Schmidt, Henri / Chan, Michelle / Raphael, Benjamin J

Cell systems

2023 Volume 14, Issue 12, Page(s) 1113–1121.e9

Abstract: CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct ... ...

Abstract	CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.
MeSH term(s)	Animals ; Mice ; CRISPR-Cas Systems/genetics ; Phylogeny ; Gene Editing/methods ; Cell Lineage/genetics ; Mutation
Language	English
Publishing date	2023-12-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2854138-8
ISSN	2405-4720 ; 2405-4712
ISSN (online)	2405-4720
ISSN	2405-4712
DOI	10.1016/j.cels.2023.11.005
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Article ; Online: ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.

Sashittal, Palash / Zhang, Haochen / Iacobuzio-Donahue, Christine A / Raphael, Benjamin J

Genome biology

2023 Volume 24, Issue 1, Page(s) 272

Abstract: A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA ... ...

Abstract	A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.
MeSH term(s)	Humans ; Animals ; Phylogeny ; Neoplasms/genetics ; Mutation ; Algorithms ; Sequence Analysis, DNA ; Birds/genetics ; DNA Copy Number Variations
Language	English
Publishing date	2023-11-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-023-03106-5
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Article: Parsimonious Clone Tree Integration in cancer.

Sashittal, Palash / Zaccaria, Simone / El-Kebir, Mohammed

Algorithms for molecular biology : AMB

2022 Volume 17, Issue 1, Page(s) 3

Abstract: Background: Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). ...

Abstract	Background: Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor's clonal composition. Results: To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. Conclusion: PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.
Language	English
Publishing date	2022-03-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2224970-9
ISSN	1748-7188
ISSN	1748-7188
DOI	10.1186/s13015-022-00209-9
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Article ; Online: Sampling and summarizing transmission trees with multi-strain infections.

Sashittal, Palash / El-Kebir, Mohammed

Bioinformatics (Oxford, England)

2020 Volume 36, Issue Suppl_1, Page(s) i362–i370

Abstract: Motivation: The combination of genomic and epidemiological data holds the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as ... ...

Abstract	Motivation: The combination of genomic and epidemiological data holds the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. Results: We formulate the direct transmission inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce Transmission Tree Uniform Sampler (TiTUS), a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritize parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS's ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. Availability and implementation: https://github.com/elkebir-group/TiTUS. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Disease Outbreaks ; Genomics ; Phylogeny
Keywords	covid19
Language	English
Publishing date	2020-07-13
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa438
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Article ; Online: Parsimonious Clone Tree Integration in cancer

Palash Sashittal / Simone Zaccaria / Mohammed El-Kebir

Algorithms for Molecular Biology, Vol 17, Iss 1, Pp 1-

2022 Volume 14

Abstract: Abstract Background Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations ( ...

Abstract	Abstract Background Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor’s clonal composition. Results To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. Conclusion PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods.
Keywords	Intra-tumor heterogeneity ; Perfect phylogeny ; Constraint programming ; Single-cell DNA sequencing ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
Subject code	004
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
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Article ; Online: doubletD: detecting doublets in single-cell DNA sequencing data.

Weber, Leah L / Sashittal, Palash / El-Kebir, Mohammed

Bioinformatics (Oxford, England)

2021 Volume 37, Issue Suppl_1, Page(s) i214–i221

Abstract: Motivation: While single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a ... ...

Abstract	Motivation: While single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and/or performance. Results: We present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results. Availability and implementation: https://github.com/elkebir-group/doubletD. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Gene Expression Profiling ; Likelihood Functions ; Sequence Analysis, DNA ; Sequence Analysis, RNA ; Single-Cell Analysis ; Software
Language	English
Publishing date	2021-07-09
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab266
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