[{"author":[{"first_name":"Sergey","last_name":"Aganezov","full_name":"Aganezov, Sergey"},{"last_name":"Zban","first_name":"Ilya","full_name":"Zban, Ilya"},{"full_name":"Aksenov, Vitalii","id":"2980135A-F248-11E8-B48F-1D18A9856A87","last_name":"Aksenov","first_name":"Vitalii"},{"full_name":"Alexeev, Nikita","first_name":"Nikita","last_name":"Alexeev"},{"full_name":"Schatz, Michael C.","last_name":"Schatz","first_name":"Michael C."}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","volume":20,"title":"Recovering rearranged cancer chromosomes from karyotype graphs","article_processing_charge":"No","external_id":{"isi":["000511618800007"]},"publication_status":"published","abstract":[{"lang":"eng","text":"Background: Many cancer genomes are extensively rearranged with highly aberrant chromosomal karyotypes. Structural and copy number variations in cancer genomes can be determined via abnormal mapping of sequenced reads to the reference genome. Recently it became possible to reconcile both of these types of large-scale variations into a karyotype graph representation of the rearranged cancer genomes. Such a representation, however, does not directly describe the linear and/or circular structure of the underlying rearranged cancer chromosomes, thus limiting possible analysis of cancer genomes somatic evolutionary process as well as functional genomic changes brought by the large-scale genome rearrangements.\r\n\r\nResults: Here we address the aforementioned limitation by introducing a novel methodological framework for recovering rearranged cancer chromosomes from karyotype graphs. For a cancer karyotype graph we formulate an Eulerian Decomposition Problem (EDP) of finding a collection of linear and/or circular rearranged cancer chromosomes that are determined by the graph. We derive and prove computational complexities for several variations of the EDP. We then demonstrate that Eulerian decomposition of the cancer karyotype graphs is not always unique and present the Consistent Contig Covering Problem (CCCP) of recovering unambiguous cancer contigs from the cancer karyotype graph, and describe a novel algorithm CCR capable of solving CCCP in polynomial time. We apply CCR on a prostate cancer dataset and demonstrate that it is capable of consistently recovering large cancer contigs even when underlying cancer genomes are highly rearranged.\r\n\r\nConclusions: CCR can recover rearranged cancer contigs from karyotype graphs thereby addressing existing limitation in inferring chromosomal structures of rearranged cancer genomes and advancing our understanding of both patient/cancer-specific as well as the overall genetic instability in cancer."}],"doi":"10.1186/s12859-019-3208-4","publication_identifier":{"eissn":["1471-2105"]},"publication":"BMC Bioinformatics","date_created":"2019-12-29T23:00:46Z","year":"2019","article_number":"641","has_accepted_license":"1","ddc":["570"],"quality_controlled":"1","file":[{"file_name":"2019_BMCBioinfo_Aganezov.pdf","content_type":"application/pdf","date_created":"2020-01-02T16:10:58Z","creator":"dernst","file_id":"7221","file_size":1917374,"date_updated":"2020-07-14T12:47:54Z","access_level":"open_access","checksum":"7a30357efdcf8f66587ed495c0927724","relation":"main_file"}],"scopus_import":"1","_id":"7214","intvolume":"        20","oa_version":"Published Version","article_type":"original","type":"journal_article","date_published":"2019-12-17T00:00:00Z","language":[{"iso":"eng"}],"month":"12","date_updated":"2026-04-16T08:35:00Z","department":[{"_id":"DaAl"}],"publisher":"BMC","isi":1,"file_date_updated":"2020-07-14T12:47:54Z","oa":1,"day":"17","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"citation":{"mla":"Aganezov, Sergey, et al. “Recovering Rearranged Cancer Chromosomes from Karyotype Graphs.” <i>BMC Bioinformatics</i>, vol. 20, 641, BMC, 2019, doi:<a href=\"https://doi.org/10.1186/s12859-019-3208-4\">10.1186/s12859-019-3208-4</a>.","apa":"Aganezov, S., Zban, I., Aksenov, V., Alexeev, N., &#38; Schatz, M. C. (2019). Recovering rearranged cancer chromosomes from karyotype graphs. <i>BMC Bioinformatics</i>. BMC. <a href=\"https://doi.org/10.1186/s12859-019-3208-4\">https://doi.org/10.1186/s12859-019-3208-4</a>","chicago":"Aganezov, Sergey, Ilya Zban, Vitalii Aksenov, Nikita Alexeev, and Michael C. Schatz. “Recovering Rearranged Cancer Chromosomes from Karyotype Graphs.” <i>BMC Bioinformatics</i>. BMC, 2019. <a href=\"https://doi.org/10.1186/s12859-019-3208-4\">https://doi.org/10.1186/s12859-019-3208-4</a>.","ista":"Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. 2019. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 20, 641.","ama":"Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. Recovering rearranged cancer chromosomes from karyotype graphs. <i>BMC Bioinformatics</i>. 2019;20. doi:<a href=\"https://doi.org/10.1186/s12859-019-3208-4\">10.1186/s12859-019-3208-4</a>","short":"S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, M.C. Schatz, BMC Bioinformatics 20 (2019).","ieee":"S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, and M. C. Schatz, “Recovering rearranged cancer chromosomes from karyotype graphs,” <i>BMC Bioinformatics</i>, vol. 20. BMC, 2019."}},{"intvolume":"         7","oa_version":"None","article_type":"original","type":"journal_article","publist_id":"1109","_id":"4351","date_published":"2006-01-01T00:00:00Z","language":[{"iso":"eng"}],"month":"01","date_updated":"2026-08-21T11:57:14Z","publisher":"BioMed Central","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"OA_type":"gold","day":"01","status":"public","citation":{"apa":"Bollback, J. P. (2006). SIMMAP: stochastic character mapping of discrete traits on phylogenies. <i>BMC Bioinformatics</i>. BioMed Central. <a href=\"https://doi.org/10.1186/1471-2105-7-88\">https://doi.org/10.1186/1471-2105-7-88</a>","chicago":"Bollback, Jonathan P. “SIMMAP: Stochastic Character Mapping of Discrete Traits on Phylogenies.” <i>BMC Bioinformatics</i>. BioMed Central, 2006. <a href=\"https://doi.org/10.1186/1471-2105-7-88\">https://doi.org/10.1186/1471-2105-7-88</a>.","mla":"Bollback, Jonathan P. “SIMMAP: Stochastic Character Mapping of Discrete Traits on Phylogenies.” <i>BMC Bioinformatics</i>, vol. 7, 88, BioMed Central, 2006, doi:<a href=\"https://doi.org/10.1186/1471-2105-7-88\">10.1186/1471-2105-7-88</a>.","short":"J.P. Bollback, BMC Bioinformatics 7 (2006).","ieee":"J. P. Bollback, “SIMMAP: stochastic character mapping of discrete traits on phylogenies,” <i>BMC Bioinformatics</i>, vol. 7. BioMed Central, 2006.","ista":"Bollback JP. 2006. SIMMAP: stochastic character mapping of discrete traits on phylogenies. BMC Bioinformatics. 7, 88.","ama":"Bollback JP. SIMMAP: stochastic character mapping of discrete traits on phylogenies. <i>BMC Bioinformatics</i>. 2006;7. doi:<a href=\"https://doi.org/10.1186/1471-2105-7-88\">10.1186/1471-2105-7-88</a>"},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","author":[{"id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4624-4612","full_name":"Bollback, Jonathan P","first_name":"Jonathan P","last_name":"Bollback"}],"extern":"1","OA_place":"publisher","volume":7,"pmid":1,"article_processing_charge":"No","external_id":{"pmid":["16504105 "]},"publication_status":"published","abstract":[{"text":"BACKGROUND: Character mapping on phylogenies has played an important, if not critical role, in our understanding of molecular, morphological, and behavioral evolution. Until very recently we have relied on parsimony to infer character changes. Parsimony has a number of serious limitations that are drawbacks to our understanding. Recent statistical methods have been developed that free us from these limitations enabling us to overcome the problems of parsimony by accommodating uncertainty in evolutionary time, ancestral states, and the phylogeny. RESULTS: SIMMAP has been developed to implement stochastic character mapping that is useful to both molecular evolutionists, systematists, and bioinformaticians. Researchers can address questions about positive selection, patterns of amino acid substitution, character association, and patterns of morphological evolution. CONCLUSION: Stochastic character mapping, as implemented in the SIMMAP software, enables users to address questions that require mapping characters onto phylogenies using a probabilistic approach that does not rely on parsimony. Analyses can be performed using a fully Bayesian approach that is not reliant on considering a single topology, set of substitution model parameters, or reconstruction of ancestral states. Uncertainty in these quantities is accommodated by using MCMC samples from their respective posterior distributions.","lang":"eng"}],"title":"SIMMAP: stochastic character mapping of discrete traits on phylogenies","date_created":"2018-12-11T12:08:25Z","year":"2006","article_number":"88","publication_identifier":{"eissn":["1471-2105"]},"doi":"10.1186/1471-2105-7-88","publication":"BMC Bioinformatics"}]