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Tumor Phylogeny Topology Inference via Deep Learning [article]

Erfan Sadeqi Azer, Mohammad Haghir Ebrahimabadi, Salem Malikić, Roni Khardon, S. Cenk Sahinalp
2020 bioRxiv   pre-print
In fact, even when the goal is to infer basic topological features of the tumor phylogeny rather than reconstructing it entirely, available techniques may be prohibitively slow.  ...  Because of the limitations of SCS technologies, such as frequent allele dropout and variable sequence coverage, a noise reduction/elimination process may become necessary to infer a tumor phylogeny.  ...  E.S.A. and S.C.S. were supported in part by NSF grant AF-1619081, R.K. was supported in part by NSF grant IIS-1906694, and M.H.E. was supported in part by Indiana U.  ... 
doi:10.1101/2020.02.07.938852 fatcat:ngd6ndp4wrcxthleaf2dkceayi

Tumor Phylogeny Topology Inference via Deep Learning

Erfan Sadeqi Azer, Mohammad Haghir Ebrahimabadi, Salem Malikić, Roni Khardon, S. Cenk Sahinalp
2020 iScience  
Principled computational approaches for tumor phylogeny reconstruction via single-cell sequencing typically aim to build the most likely perfect phylogeny tree from the noisy genotype matrix - which represents  ...  In this paper, we introduce fast deep learning solutions to the problems of inferring whether the most likely tree has a linear (chain) or branching topology and whether a perfect phylogeny is feasible  ...  on phylogeny inference from bulk or single-cell sequencing data El-Kebir, 2018) .  ... 
doi:10.1016/j.isci.2020.101655 pmid:33117968 pmcid:PMC7582044 fatcat:r3vj6gwdvzdw7jtynf4zqb3ct4

Parameter, noise, and tree topology effects in tumor phylogeny inference

Kiran Tomlinson, Layla Oesper
2019 BMC Medical Genomics  
While a number of methods have been proposed to reconstruct the evolutionary history of a tumor from DNA sequencing data, it is not clear how aspects of the sequencing data and tumor itself affect these  ...  Accurate inference of the evolutionary history of a tumor has important implications for understanding and potentially treating the disease.  ...  A number of recent approaches have focused on using single-cell sequencing data to reconstruct tumor phylogenies [14] [15] [16] .  ... 
doi:10.1186/s12920-019-0626-0 pmid:31865909 pmcid:PMC6927103 fatcat:p5nomzes5jdwhkvr3b5tiawq34

Reference-free inference of tumor phylogenies from single-cell sequencing data

Ayshwarya Subramanian, Russell Schwartz
2015 BMC Genomics  
The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially  ...  Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression.  ...  Acknowledgements This work is supported in part by the US National Institutes of Health award #1R01CA140214. We acknowledge Dr. Stanley Shackney for his useful inputs and feedback.  ... 
doi:10.1186/1471-2164-16-s11-s7 pmid:26576947 pmcid:PMC4652515 fatcat:nhv6zujaibeedcypcby2a3koua

Reference-free inference of tumor phylogenies from single-cell sequencing data

Ayshwarya Subramanian, Russell Schwartz
2014 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)  
The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially  ...  Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression.  ...  Acknowledgements This work is supported in part by the US National Institutes of Health award #1R01CA140214. We acknowledge Dr. Stanley Shackney for his useful inputs and feedback.  ... 
doi:10.1109/iccabs.2014.6863944 dblp:conf/iccabs/SubramanianS14 fatcat:c3fmrp4osnavthcoibaokyjkk4

Applying unmixing to gene expression data for tumor phylogeny inference

Russell Schwartz, Stanley E Shackney
2010 BMC Bioinformatics  
Articles in BMC journals are listed in PubMed and archived at PubMed Central.  ...  Like all articles in BMC journals, this peer-reviewed article was published immediately upon acceptance.  ...  The organizations providing funding for this work had no role in the study design; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit  ... 
doi:10.1186/1471-2105-11-42 pmid:20089185 pmcid:PMC2823708 fatcat:qm62u22qcjfw7eotnezs2rcika

BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies

Ke Yuan, Thomas Sakoparnig, Florian Markowetz, Niko Beerenwinkel
2015 Genome Biology  
In two case studies, we demonstrate how BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm.  ...  Here, we present BitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways.  ...  The authors would like to acknowledge Wei Liu for preparing the Sankey plot script, Edith Ross for converting SNV profiles to binary data, Andrea Sottoriva for helpful discussions and providing the processed  ... 
doi:10.1186/s13059-015-0592-6 pmid:25786108 pmcid:PMC4359483 fatcat:kspn5r667fhf3geq66r7g2jigy

MOCA for Integrated Analysis of Gene Expression and Genetic Variation in Single Cells

Jared Huzar, Hannah Kim, Sudhir Kumar, Sayaka Miura
2022 Frontiers in Genetics  
We present Multi-Omics Concordance Analysis (MOCA) software to jointly analyze gene expressions and genetic variations from single-cell RNA sequencing profiles.  ...  In cancer, somatic mutations occur continuously, causing cell populations to evolve.  ...  In conclusion, MOCA provides tools to assess the reliability of genetic ancestry annotation from phylogenies inferred using noisy genetic data (i.e., SNVs detected in scRNA-seq data).  ... 
doi:10.3389/fgene.2022.831040 pmid:35432484 pmcid:PMC9009314 fatcat:bvocyyvnlzajjhtalh274aouay

Accurate and Efficient Cell Lineage Tree Inference from Noisy Single Cell Data: the Maximum Likelihood Perfect Phylogeny Approach [article]

Yufeng Wu
2019 bioRxiv   pre-print
In this paper, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data.  ...  Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes.  ...  It is highly desirable to infer cell lineage tree with single cell data (e.g., single cell DNA sequences) from multiple sampled (healthy or tumor) cells.  ... 
doi:10.1101/742395 fatcat:qdfhknej7fgwrcy3iqx7wu6lnm

Tree inference for single-cell data [article]

Katharina Jahn, Jack Kuipers, Niko Beerenwinkel
2016 biorxiv/medrxiv   pre-print
Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches.  ...  Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumour from noisy and incomplete mutation profiles of single cells.  ...  for tree inference from bulk-sequencing data.  ... 
doi:10.1101/047795 fatcat:i7k55rq67fakjiz4ypqwtliile

Predicting clone genotypes from tumor bulk sequencing of multiple samples [article]

Sayaka Miura, Karen Gomez, Oscar Murillo, Louise A Huuki, Tracy Vu, Tiffany Buturla, Sudhir Kumar
2018 bioRxiv   pre-print
Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data.  ...  Motivation: Analyses of data generated from bulk sequencing of tumors have revealed extensive ge-nomic heterogeneity within patients.  ...  While phylogenies of tumor samples are commonly inferred using these data, these phylogenies are not identical to clone phylogenies because multiple clones are often present within each sample (Alves  ... 
doi:10.1101/341180 fatcat:tipsb5nm55gydkkvako5vxku34

Tree inference for single-cell data

Katharina Jahn, Jack Kuipers, Niko Beerenwinkel
2016 Genome Biology  
Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells.  ...  Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches.  ...  for tree inference from bulk-sequencing data.  ... 
doi:10.1186/s13059-016-0936-x pmid:27149953 pmcid:PMC4858868 fatcat:blwhd3rbdfh7hh5zyjmy3ds4wu

Next Generation Sequencing (Dagstuhl Seminar 16351)

Gene Myers, Mihai Pop, Knut Reinert, Tandy Warnow, Marc Herbstritt
2017 Dagstuhl Reports  
Next Generation Sequencing (NGS) data have begun to appear in many applications that are clinically relevant, such as resequencing of cancer patients, disease-gene discovery and diagnostics for rare diseases  ...  The analysis of sequencing data is demanding because of the enormous data volume and the need for fast turnaround time, accuracy, reproducibility, and data security.  ...  Phylogeny inference under the infinite sites assumption to clean up noisy observations: SCITE and OncoNEM.  ... 
doi:10.4230/dagrep.6.8.91 dblp:journals/dagstuhl-reports/MyersPRW16 fatcat:ffovfpifvfe5nbmlsvwqkhiv3u

SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models [article]

Hamim Zafar, Anthony Tzen, Nicholas Navin, Ken Chen, Luay Nakhleh
2016 bioRxiv   pre-print
We propose a statistical inference method for tumor phylogenies from noisy SCS data under a finite-sites model. We demonstrate the performance of our method on synthetic and biological data sets.  ...  Single-cell sequencing (SCS) enables the inference of tumor phylogenies and methods were recently introduced for this task under the infinite-sites assumption.  ...  Results and discussion Overview of SiFit We start with a brief explanation of how SiFit infers a tumor phylogeny from noisy genotype data obtained from single-cell sequencing.  ... 
doi:10.1101/091595 fatcat:qvnp7lro3bhyhef5g6f7gwagie

SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models

Hamim Zafar, Anthony Tzen, Nicholas Navin, Ken Chen, Luay Nakhleh
2017 Genome Biology  
We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model.  ...  Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories.  ...  Raw sequencing data for the human tumor data sets are available from the Short Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra), under SRA numbers SRP067815 and SRP074289.  ... 
doi:10.1186/s13059-017-1311-2 pmid:28927434 pmcid:PMC5606061 fatcat:d4rhrx4t75g5bl7idot7gbv26q
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