{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-mutational-graphs-of-individual","title":"Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data","arxiv_id":"1709.01076","date":"2017-09-04","proceeding":null,"authors":["Daniele Ramazzotti","Alex Graudenzi","Luca De Sano","Marco Antoniotti","Giulio Caravagna"],"abstract":"Background. A large number of algorithms is being developed to reconstruct\nevolutionary models of individual tumours from genome sequencing data. Most\nmethods can analyze multiple samples collected either through bulk multi-region\nsequencing experiments or the sequencing of individual cancer cells. However,\nrarely the same method can support both data types.\n  Results. We introduce TRaIT, a computational framework to infer mutational\ngraphs that model the accumulation of multiple types of somatic alterations\ndriving tumour evolution. Compared to other tools, TRaIT supports multi-region\nand single-cell sequencing data within the same statistical framework, and\ndelivers expressive models that capture many complex evolutionary phenomena.\nTRaIT improves accuracy, robustness to data-specific errors and computational\ncomplexity compared to competing methods.\n  Conclusions. We show that the application of TRaIT to single-cell and\nmulti-region cancer datasets can produce accurate and reliable models of\nsingle-tumour evolution, quantify the extent of intra-tumour heterogeneity and\ngenerate new testable experimental hypotheses.","url_abs":"http://arxiv.org/abs/1709.01076v2","url_pdf":"http://arxiv.org/pdf/1709.01076v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-mutational-graphs-of-individual","repo_url":"https://github.com/BIMIB-DISCo/TRaIT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}