Marek Kimmel

This study assesses microhomology-associated deletions in cancer genomes, identifies their prognostic significance, and optimizes detection parameters for improved patient stratification.

This study evaluates methods to correct cytosine deamination artifacts in FFPE sequencing, finding enzymatic repair is most effective and FFPErase is the best computational method.

DECODE improves tumor clonality and expansion mode detection by accounting for sequencing biases and neutral tail in SFS, showing better performance on synthetic data and clinical relevance in glioma.

BESTish infers mutation fitness, mutation rate, and environmental strength in clonal hematopoiesis using a diffusion-approximation framework applied to VAF datasets.

The paper analyzes FFPE breast cancer specimens to assess recoverable molecular evolution features using computational methods despite sequencing artifacts.

The paper develops CliPP to estimate subclonal mutation load (sML) and shows it predicts survival and immunotherapy response in cancers with low to moderate TMB.

The paper compares Tug-of-War models under Moran and branching process dynamics, showing similar statistics and inferring selection coefficients for breast cancer samples, supporting deleterious passenger mutations.

This study uses pan-genomic data to analyze D4Z4 repeats and CpG methylation in FSHD, finding low methylation correlates with SMCHD1 variants but not clinical phenotypes.

This study identifies molecular changes in bladder mucosa that initiate cancer, revealing inflammation's role and mutation patterns linked to early carcinogenesis.

The paper identifies replication origins in human tumors using mutation patterns and compares them across methods to define a consensus set.

CliPP reconstructs subclonal architecture in cancer DNA sequencing data efficiently and accurately, outperforming existing methods in speed while maintaining high concordance with consensus calls.