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PINNACLE is a context-specific geometric deep learning model for generating protein representations. Leveraging single-cell transcriptomics combined with networks of protein–protein interactions, cell type-to-cell type interactions and a tissue hierarchy, PINNACLE generates high-resolution protein representations tailored to each cell type.
Bayesian nonparametric Track (BNP-Track) simultaneously determines emitter numbers and their tracks alongside uncertainty, extending the superresolution paradigm from static samples to single-particle tracking even in dense environments.
cryoDRGN-ET is a generative neural network method for heterogeneous reconstruction of cryo-ET subtomograms. Using subtomogram tilt-series images, it can capture states diverse in both composition and conformation.
Onavg is a surface template of the human cortex. In contrast to existing templates, the cortical surface is uniformly sampled, which has advantages in numerous applications.
SwitchSeeker combines computational and experimental techniques to identify functional RNA structural switches. Applied to the human transcriptome, it identified a novel RNA switch in the 3ʹUTR of RORC, linked to nonsense-mediated decay.
Keypoint-MoSeq is an unsupervised behavior segmentation algorithm that extracts behavioral modules from keypoint tracking data acquired with diverse algorithms, as demonstrated in mice, rats and fruit flies. The extracted modules faithfully reflect human-annotated behaviors even though they are obtained in an unsupervised fashion.
This work presents ORFtag that enables proteome-wide functional screens by tagging and overexpression of endogenously encoded proteins via randomly integrated cassettes.
The MHz repetition rates available at second-generation X-ray free-electron lasers enable the collection of microsecond time-resolved X-ray scattering data with exceptionally low noise, providing insights into protein structural dynamics.
Scalable tools are needed for the analysis of increasingly large mass spectrometry-based proteomics datasets. quantms offers an open-source, cloud-based pipeline for massively parallel proteomics data analysis.
This analysis presents a systematic comparison of 11 sequencing-based spatial transcriptomics methods using well-characterized references, which offers insights into performance variations in spatial transcriptomic techniques.
This work introduces CRISPRdelight, a dCas12a-based DNA-imaging tool that facilitates the imaging of non-repetitive loci and the tracking of DNA dynamics.
Gapr is an efficient platform for reconstructing neurons in large-scale light microscopy datasets. It enables various proofreading modes as well as collaboration among many annotators.
CandyCrunch is a deep learning-based tool for predicting glycan structures from tandem mass spectrometry data. The paper also introduces CandyCrumbs that automatically annotates fragment ions in higher-order tandem mass spectrometry spectra.
Lightning Pose is an efficient pose estimation approach that requires few labeled training data owing to its semi-supervised learning strategy and ensembling.
The EMDataResource Ligand Model Challenge aimed at assessing the reliability and reproducibility of modeling ligands bound to protein and protein–nucleic acid complexes in cryo-EM maps determined at near-atomic resolution. This analysis presents the results and recommends best practices for assessing cryo-EM structures of liganded macromolecules.
SpatialGlue is a graph neural network-based approach for integrating multimodal spatial omics data. Combining complementary data modalities improves the discovery of spatial domains as well as the identification of cell subpopulations across tissues.
Bessel-droplet two-photon fluorescence microscopy offers high-contrast and high-resolution volumetric imaging in vivo and can be used for high-throughput mapping of functional synaptic organization in the mouse brain.