Jennifer Marie Shelton

Lancet2 improves somatic variant calling with faster runtime and better performance, especially for InDels, using enhanced methods and machine learning.

This paper introduces an explainable boosting machine model to identify and filter variant artifacts caused by formalin-fixed paraffin embedding in cancer genomics.

The paper describes a comprehensive genomic dataset from a tumor-normal pair with broad consent, generated using multiple sequencing technologies for benchmarking somatic variant detection.

This study examines how mutagenesis and extrachromosomal DNA contribute to urothelial cancer evolution, revealing clonal APOBEC3 mutations and chemotherapy-induced mutational bursts within ecDNA.

Fasta-O-Matic checks and reformats FASTA files to ensure compatibility with downstream bioinformatics tools.

The paper presents tools to improve genome assembly using consensus maps and super scaffolding, significantly increasing contiguity in a Tribolium castaneum genome.