John B. Easton

The paper identifies non-coding somatic variants in TAL1 regulatory regions that reactivate enhancers normally active in hematopoietic stem cells in T-lineage acute lymphoblastic leukemia.

The paper introduces BOUQUET, an algorithm that identifies 3D-super-enhancers as protein-rich cis-regulatory communities associated with co-activator condensates in mammalian cells.

This study shows that targeting RBM39 with indisulam promotes natural killer cell activity and eradicates neuroblastoma regardless of cellular state plasticity.

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

The paper uses single-cell sequencing to uncover hidden genetic diversity in pediatric ALL that is not detected by bulk sequencing, identifying treatment-resistant clones and their associated mutations.

Spotiphy transforms spatial transcriptomics data into single-cell-resolved whole-transcriptome images, revealing novel cellular compositions and spatial domains in Alzheimer's and breast tissue.

The paper investigates RA's limited effect on primary neuroblastoma tumors but efficacy in clearing bone marrow cells, identifying BMP signaling as a key determinant of cell fate and RA sensitivity.

This study creates two mouse models with conditional c-MYC activation in specific cell types, leading to distinct neuroendocrine tumors resembling human neuroblastoma and somatostatinoma.

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.

This study identifies DNA methylation sites associated with cancer treatments and mediates cardiometabolic risks in childhood cancer survivors.

The paper identifies TCF4 and ID3 as key transcription factors driving mammary epithelial stem cell differentiation through single-cell RNA sequencing analysis.

RNAIndel identifies somatic coding indels in tumor RNA-Seq data with high sensitivity and low false positives, outperforming existing methods.

LCA identifies subtle cell population diversity in large-scale single-cell RNA-seq data using a dual-space model with spectral clustering and technical variation control.

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.

The paper identifies differentially methylated regions in myeloid differentiation, showing genetic and developmental influences on methylome dynamics.