Browse our Tools and Resources

Image3C is a new image-based analysis of cell population composition for research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

A deep neural network trained on semi-synthetic data learns to quickly track and identify neurons in Caenorhabditis elegans.

A new method is described to apply rapid and uniform mechanical forces to vestibular and cochlear hair cells.

DNA nanodevices can be targeted to specific cell types in Caenorhabditis elegans with subcellular precision by exploiting either endogenous or synthetic receptors.

Live-cell imaging captures the heterogenity of bacterial growth within intracellular bacterial communities and demonstrates that the constituent bacteria are protected from clearance by antibiotics delivered with a physiologically relevant pharmacodynamic profile.

An inference paradigm for extracting neuronal correlations from two-photon imaging data, without requiring intermediate spike deconvolution, provides significant performance gains over existing methods as demonstrated by theoretical analysis, simulation studies, and real-data applications.

One hundred one high-quality drosophilid genomes are released, along with low-cost assembly workflows, as an open community resource for studying genetics, ecology, and evolution in this important model system.

The World Mortality Dataset and paper is the largest dataset of all-cause mortality currently in existence for tracking mortality and excess mortality during the COVID-19 pandemic.

NICEdrug.ch is a resource allowing systematic and large-scale computational analysis of drug biochemistry, enzymatic targets, and toxicity in the context of cellular metabolism.

A novel deep-learning framework shows how to interpret and decode raw neural recordings, avoiding the need for strong prior hypotheses, revealing a novel representation of head direction.