Daniel K. Putnam

This study analyzes structural variants in 1,616 pediatric cancer genomes, revealing unique SV hotspots and patterns distinct from adult cancers.

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.

Seq2Karyotype (S2K) enables in-silico karyotyping from whole-genome sequencing data, revealing intratumoral heterogeneity and evolutionary trajectories in cancer samples.

The paper identifies 'pliancy genes' in Muller glia that enable rapid response to retinal stress, injury, and disease, showing species-specific differences in their activation between zebrafish and mammals.

This paper introduces PTA, a new WGA method that improves variant calling accuracy in single cells, enabling studies of genetic diversity and CRISPR off-target effects.

MethylationToActivity infers promoter activity from DNA methylation data in tumors, showing high accuracy across multiple cancer types.

The paper studies the chromatin structure of rod photoreceptors in mice using Hi-C and machine learning to identify regulatory elements involved in retinal development.

VCF2CNA detects copy-number alterations from VCF data with high accuracy and platform independence, outperforming existing methods in neuroblastoma samples.