2026-08-06T06:52:41Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20651 2024-08-26T09:36:46Z ddc:004 open_access

Sapling: Inferring and Summarizing Tumor Phylogenies from Bulk Data Using Backbone Trees Qi, Yuanyuan El-Kebir, Mohammed Cancer intra-tumor heterogeneity consensus maximum agreement Cancer phylogenies are key to understanding tumor evolution. There exist many important downstream analyses that take as input a single or a small number of trees. However, due to uncertainty, one typically infers many, equally-plausible phylogenies from bulk DNA sequencing data of tumors. We introduce Sapling, a heuristic method to solve the Backbone Tree Inference from Reads problem, which seeks a small set of backbone trees on a smaller subset of mutations that collectively summarize the entire solution space. Sapling also includes a greedy algorithm to solve the Backbone Tree Expansion from Reads problem, which aims to expand an inferred backbone tree into a full tree. We prove that both problems are NP-hard. On simulated and real data, we demonstrate that Sapling is capable of inferring high-quality backbone trees that adequately summarize the solution space and that can be expanded into full trees. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Yuanyuan Qi and Mohammed El-Kebir 2024 Is Part Of LIPIcs, Volume 312, 24th International Workshop on Algorithms in Bioinformatics (WABI 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.WABI.2024.7 urn:nbn:de:0030-drops-206518 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2024.7 eng https://creativecommons.org/licenses/by/4.0/legalcode