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Nucleotide Transformer is a series of genomics foundation models of different parameter sizes and training datasets that can be applied to various downstream tasks by fine-tuning.
This manuscript reports a high-throughput platform for nanoscale spatially resolved extraction of membrane proteins into native nanodiscs by using a library of membrane-active polymers while maintaining their local membrane context.
SurfDock is a method for predicting protein–ligand complex structures by leveraging multimodal protein information and generative diffusion frameworks. Its results can be generalized to unseen proteins and real-world settings.
This article reports a method based on a bifunctional chemical probe called TME and a workflow named RUBICON to capture, enrich and profile endogenous disordered proteins in cells. The method enables a proteome-wide analysis of protein disorder via high-throughput fluorescence and mass spectrometry-based proteomics.
A variant of structured illumination microscopy called MLS-SIM allows super-resolution imaging of neuronal structures such as spinules, spines and boutons in awake mice.
CelloType is an end-to-end method for spatial omics data analysis that uses a transformer-based deep neural network for concurrent object detection, segmentation and classification and performs with high accuracy on diverse datasets.
This work establishes a prime editing platform for high-throughput interrogation of small genetic variants (up to tens of thousands) with negative selection phenotypes.
spIsoNet is an end-to-end self-supervised deep learning-based software to address the reconstruction and misalignment challenge in single-particle cryo-EM caused by the preferred-orientation problem. spIsoNet can also improve map isotropy and particle alignment of preferentially oriented molecules during subtomogram averaging in cryogenic electron tomography.
BiomedParse is a foundation model for image analysis that uses a joint learning approach to jointly conduct segmentation, detection and recognition and offer state-of-the-art performance across a wide range of datasets and nine modalities.
pSABER combines the power of signal amplification by exchange reaction (SABER) with the deposition of fluorescent or colorimetric substrates by horseradish peroxidase to enable enhanced signals for in situ hybridization in cells and tissues.
This resource integrates different human embryo datasets to create a transcriptional reference map of human embryonic development from zygote to gastrula.
NeuroMechFly v2 extends the capabilities of the original neuromechanical modeling platform for Drosophila, NeuroMechFly, by including sensory input, motor feedback and the ability to simulate complex terrains.
Long-term imaging in the spinal cord is achieved by placing a fluoropolymer membrane on the spinal cord, which reduces fibrosis. This approach, combined with deep-learning-based motion correction, enables months-long imaging of the same neurons.
BehaviorFlow is a behavioral analysis package that overcomes challenges with multiple testing when dealing with large numbers of behavioral variables and limited availability of data. BehaviorFlow also allows combining datasets from different experiments.
EvoScan combines EvolvR mutagenesis and phage selection to explore the protein sequence space in different dimensions, identifying anchor points with critical mutations. Next, EvoAI, a deep learning-based method, uses these anchor points to accurately reconstruct the protein sequence space and design new proteins.
