Modeling epigenetic lesions that cause gliomas.

Epigenetic lesions that disrupt regulatory elements represent potential cancer drivers. However, we lack experimental models for validating their tumorigenic impact. Here, we model aberrations arising in isocitrate dehydrogenase-mutant gliomas, which exhibit DNA hypermethylation.

Integrative dissection of gene regulatory elements at base resolution.

Although vast numbers of putative gene regulatory elements have been cataloged, the sequence motifs and individual bases that underlie their functions remain largely unknown. Here, we combine epigenetic perturbations, base editing, and deep learning to dissect regulatory sequences within the exemplar immune locus encoding CD69. We converge on a ∼170 base interval within a differentially accessible and acetylated enhancer critical for CD69 induction in stimulated Jurkat T cells.

Detecting sample swaps in diverse NGS data types using linkage disequilibrium.

As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays.

Altered chromosomal topology drives oncogenic programs in SDH-deficient GISTs.

Epigenetic aberrations are widespread in cancer, yet the underlying mechanisms and causality remain poorly understood. A subset of gastrointestinal stromal tumours (GISTs) lack canonical kinase mutations but instead have succinate dehydrogenase (SDH) deficiency and global DNA hyper-methylation. Here, we associate this hyper-methylation with changes in genome topology that activate oncogenic programs.

A statistical model for improved membrane protein expression using sequence-derived features.

The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino acid sequence for a given IMP can dramatically affect expression levels, yet these observations have not resulted in generalizable approaches to improve expression levels.