ArticleBMC bioinformatics2019
BAMSE: Bayesian model selection for tumor phylogeny inference among multiple samples.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.Bioinformatics advances · 2025Review
- A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies.Frontiers in genetics · 2024Review
- Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses.Bioinformatics (Oxford, England) · 2021Article
- Methodological Advancements for Investigating Intra-tumoral Heterogeneity in Breast Cancer at the Bench and Bedside.Journal of mammary gland biology and neoplasia · 2020Article
- Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm.BMC bioinformatics · 2020Article
- Phylogenetic Tree Inference: A Top-Down Approach to Track Tumor Evolution.Frontiers in genetics · 2019Article
- Using LICHeE and BAMSE for Reconstructing Cancer Phylogenetic Trees.Current protocols in bioinformatics · 2018Article
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3 authors.
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Abstract
backgroundIntra-tumor heterogeneity is known to contribute to cancer complexity and drug resistance. Understanding the number of distinct subclones and the evolutionary relationships between them is scientifically and clinically very important and still a challenging problem.
resultsIn this paper, we present BAMSE (BAyesian Model Selection for tumor Evolution), a new probabilistic method for inferring subclonal history and lineage tree reconstruction of heterogeneous tumor samples. BAMSE uses somatic mutation read counts as input and can leverage multiple tumor samples accurately and efficiently. In the first step, possible clusterings of mutations into subclones are scored and a user defined number are selected for further analysis. In the next step, for each of these candidates, a list of trees describing the evolutionary relationships between the subclones is generated. These trees are sorted by their posterior probability. The posterior probability is calculated using a Bayesian model that integrates prior belief about the number of subclones, the composition of the tumor and the process of subclonal evolution. BAMSE also takes the sequencing error into account. We benchmarked BAMSE against state of the art software using simulated datasets.
conclusionsIn this work we developed a flexible and fast software to reconstruct the history of a tumor's subclonal evolution using somatic mutation read counts across multiple samples. BAMSE software is implemented in Python and is available open source under GNU GLPv3 at https://github.com/HoseinT/BAMSE .
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