Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
By learning representations for both cells and various condition covariates, scPoli facilitates atlas-level integration and analysis of single-cell genomics datasets with improved interpretability.
OPUS-DSD is a neural network-based algorithm that reconstructs distinct conformations or continuous dynamics of the macromolecular structural landscape, starting from single-particle cryo-EM data.
Tension-activated cell tagging (TaCT) is a new method that uses flow cytometry to sort mechanically active cells based on the forces generated by their surface adhesion receptors.
Few methods for three-dimensional structure modeling of nucleic acids from cryo-EM data exist. CryoREAD, a fully automated DNA/RNA atomic structure modeling method based on deep learning, was developed to fill this gap.
A comprehensive redevelopment of the ribosome profiling workflow involves improved nuclease treatment and sequencing library preparation, enabling richer and more accurate translatome profiling with lower input and fewer technical hurdles.
Single-cell Deep Visual Proteomics integrates imaging, cell segmentation, laser microdissection and multiplexed mass spectrometry for spatial single-cell proteomics measurements in complex tissues.
This analysis leverages experimentally sequenced data and in silico mixtures to simulate transcript expression differences, which enables a performance assessment of long-read tools developed for isoform detection, differential transcript expression analysis and differential transcript usage analysis.
An updated version of the Waxholm Space atlas of the rat brain includes more detailed annotations of several brain regions, including the cortex, striatopallidal region, midbrain and thalamus, expanding the previous version with 112 new and 57 revised structures.
CellOT combines the benefits of optimal transport and input convex neural architectures to directly learn and uncover maps between control and perturbed cell states at the single-cell level.
A deep learning approach bypasses iterative trials associated with sensorless adaptive optics to compensate for wavefront deformations when imaging biological specimens, enabling improved deep tissue localization microscopy.
Guided sparse factor analysis (GSFA) is a powerful statistical framework to detect changes in gene expression as a result of perturbations in single-cell CRISPR screening.
D-LMBmap is a fully automated pipeline for mesoscale connectomics including deep-learning modules for axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap works accurately across cell types and modalities.
AlteredPQR is a software tool, available as an R package, to infer remodeling of protein functional modules from whole-cell or tissue lysate proteomic measurements.
A Ni2+-modified MspA nanopore construct can unambiguously discriminate the 20 proteogenic amino acids as well as several post-translational modifications.
Droplet-based microfluidics enable rapid mixing with millisecond dead times and allow single-molecule measurements of non-equilibrium binding kinetics on even challenging, strongly adsorptive samples, such as intrinsically disordered proteins.
skani achieves fast calculation of average nucleotide identity (ANI) between metagenome-assembled genomes (MAGs), with improved robustness against incomplete and fragmented MAGs.
EzMechanism is a tool for automated prediction of the catalytic mechanisms of enzymes using their three-dimensional structures and chemical reactions as input.
veloVI enhances RNA velocity analysis with uncertainty quantification and extensibility by deep generative modeling of gene-specific transcriptional dynamics.