David A. Wheeler

CanID: a robust and accurate RNAseq Expression-based diagnostic classification scheme for pediatric malignancies

CanID is a machine learning classifier for pediatric cancer subtypes using RNAseq data, achieving 99% accuracy for solid tumors and 92-93% for hematologic malignancies.

Evaluation of Bayesian Point-Based System on the Variant Classification of Hereditary Cancer Predisposition Genes

The paper evaluates a Bayesian point-based system for variant classification in hereditary cancer genes, finding it reduces variants of uncertain significance compared to ACMG/AMP 2015 guidelines.

Detection and characterization of replication origins defined by DNA polymerase epsilon

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

Genomic profiling of childhood tumor patient-derived xenograft models to enable rational clinical trial design

This paper genomically characterizes 261 pediatric cancer patient-derived xenograft models to guide rational clinical trial design and drug development.

Landscape of human miRNA variation and conservation using Annotative Database of miRNA Elements, ADmiRE

The paper develops ADmiRE to annotate miRNA variants in large population datasets, identifies highly constrained miRNAs, and provides conservation scores for future disease studies.

Pathway and network analysis of more than 2,500 whole cancer genomes

The paper analyzes non-coding mutations across 2,583 cancer genomes to identify genes and pathways affected, finding clusters of interacting proteins and insights into cancer mechanisms.

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data

A comprehensive multicenter comparison of whole genome sequencing pipelines using a uniform tumor-normal sample pair

This study compares whole genome sequencing pipelines across five centers using a uniform tumor-normal sample pair, identifying factors affecting output consistency.