plasmut

plasmut quantifies the likelihood that a given plasma mutation arises from clonal hematopoiesis or the underlying tumor using a Bayesian framework applied to sequencing data from plasma and white blood cells.

Key Features:

• Bayesian Likelihood Quantification: Computes the probability that a mutation originates from tumor versus non-tumor sources using a Bayesian statistical framework.
• Dual-Tissue Sequencing Input: Requires sequencing data from both plasma and white blood cells, including the number of distinct and mutant reads in each tissue.
• Monte Carlo Importance Sampling: Implements Monte Carlo importance sampling to assess the relative likelihood of tumor versus non-tumor mutation origin.

Scientific Applications:

• Mutation Stratification: Stratifies mutations observed in cell-free DNA and white blood cells as germline, hematopoietic, or somatic.
• Clonal Hematopoiesis Detection: Distinguishes plasma mutations arising from clonal hematopoiesis from those originating from the underlying tumor.

Methodology:

plasmut applies a Bayesian method with Monte Carlo importance sampling to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin, using the number of distinct and mutant reads from sequencing data of plasma and white blood cells.