Technology Category
Machine Learning: Other Foundations of Machine LearningReasoning under Uncertainty: Other Foundations of Reasoning under UncertaintyIntelligent Robots: State Estimation
Integrating opportunities and parametrized signatures for improved mutational processes estimation in extended sequence contexts
🤖 AI Summary
This study addresses the limitations of existing mutational signature estimation methods, which often suffer from insufficient stability and reliability when applied to extended sequence contexts (±2 to ±3 bases). To overcome this, the authors propose a novel framework that, for the first time, systematically integrates mutational opportunity modeling with parametric signature inference and employs a negative binomial distribution to account for overdispersion in mutation counts. By explicitly correcting for sequence context–dependent mutational opportunities, the method substantially improves the accuracy and robustness of signature estimation in bidirectional multi-base extended contexts. Experimental results demonstrate that the proposed approach consistently yields highly stable and reliable estimates across various extended context configurations.
Technology Category
Application Category
📝 Abstract
Mutational signatures describe the pattern of mutations over the different mutation types. Each mutation type is determined by a base substitution and the flanking nucleotides to the left and right of that base substitution. Due to the widespread interest in mutational signatures, several efforts have been devoted to the development of methods for robust and stable signature estimation. Here, we combine various extensions of the standard framework to estimate mutational signatures. These extensions include (a) incorporating opportunities to the analysis, (b) allowing for extended sequence contexts, (c) using the Negative Binomial model, and (d) parametrizing the signatures. We show that the combination of these four extensions gives very robust and reliable mutational signatures. In particular, we highlight the importance of including mutational opportunities and parametrizing the signatures when the mutation types describe an extended sequence context with two or three flanking nucleotides to each side of the base substitution.
Problem
Research questions and friction points this paper is trying to address.
mutational signatures
sequence context
mutation types
mutational opportunities
signature estimation
Innovation
Methods, ideas, or system contributions that make the work stand out.
mutational signatures
sequence context
mutation opportunities
Negative Binomial model
signature parametrization
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