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      Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

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          Abstract

          Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                July 2023
                : 33
                : 7
                : 1089-1100
                Affiliations
                [1 ]Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
                [2 ]Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA;
                [3 ]Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA;
                [4 ]Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland, College Park, Maryland 20742, USA;
                [5 ]Department of Computer Science, Indiana University, Bloomington, Indiana 47408, USA;
                [6 ]Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
                [7 ]Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
                [8 ]Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
                Author notes
                [9]

                These authors contributed equally to this work.

                Corresponding author: cenk.sahinalp@ 123456nih.gov
                Author information
                http://orcid.org/0000-0002-1051-7236
                http://orcid.org/0000-0002-4215-5655
                http://orcid.org/0000-0001-5553-3312
                http://orcid.org/0000-0002-5582-3130
                http://orcid.org/0000-0002-7862-3940
                http://orcid.org/0000-0001-5119-7550
                http://orcid.org/0000-0002-5050-0682
                Article
                9509184
                10.1101/gr.277608.122
                10538489
                37316351
                9d9d29f7-1173-4bdb-94f2-5f5aae4e3ec3
                © 2023 Liu et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 12 January 2023
                : 6 June 2023
                Page count
                Pages: 12
                Funding
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                Funded by: National Cancer Institute , doi 10.13039/100000054;
                Funded by: Center for Cancer Research
                Funded by: NCI-UMD Partnership Program
                Funded by: state of Maryland
                Categories
                Methods

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