The regulatory potential of transposable elements in maize.

Since their initial discovery in maize, transposable elements (TEs) have emerged as being integral to the evolution of maize, accounting for 80% of its genome. However, the repetitive nature of TEs has hindered our understanding of their regulatory potential. Here, we demonstrate that long-read chromatin fiber sequencing (Fiber-seq) permits the comprehensive annotation of the regulatory potential of maize TEs.

Coordination of bacterial cell wall and outer membrane biosynthesis.

Gram-negative bacteria surround their cytoplasmic membrane with a peptidoglycan (PG) cell wall and an outer membrane (OM) with an outer leaflet composed of lipopolysaccharide (LPS). This complex envelope presents a formidable barrier to drug entry and is a major determinant of the intrinsic antibiotic resistance of these organisms. The biogenesis pathways that build the surface are also targets of many of our most effective antibacterial therapies.

An in silico method to assess antibody fragment polyreactivity.

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.

Disease variant prediction with deep generative models of evolutionary data.

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods have relied on training machine learning models on known disease labels.