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Cascadia is a mass spectrometry-based de novo sequencing model that uses a transformer architecture to handle data-independent acquisition data and achieves substantially improved performance across a range of instruments and experimental protocols.
ColdBrew is a machine learning-based method that predicts the probability of cryogenic crystallographic waters to be present at room temperature, which links to their relative energies and displaceability by ligands.
CryoDRGN-AI is a deep learning system for ab initio reconstruction of dynamic biomolecular complexes from cryo-electron microscopy and cryo-electron tomography imaging datasets.
A theoretical foundation for entrapment methods is presented, along with a method that enables more accurate evaluation of false discovery rate (FDR) control in proteomics mass spectrometry analysis pipelines. Evaluation of popular data-dependent acquisition tools indicates that these generally seem to control the FDR, but data-independent acquisition tools exhibit inconsistent control of the FDR at both the peptide and protein levels.
CondenSeq is an imaging-based, high-throughput platform for characterizing condensate formation within the nuclear environment, uncovering the protein sequence features that promote this process.
Spotiflow uses deep learning for subpixel-accurate spot detection in diverse 2D and 3D images. The improved accuracy offered by Spotiflow enables improved biological insights in both iST and live imaging experiments.
Cell Neural Networks on Spatial Transcriptomics (CellNEST) deciphers patterns of communication between cells in spatially resolved transcriptomics data and can detect both signals between individual cells and relay networks of communication.
SpotSweeper is a spatially aware method for quality control of spatially resolved transcriptomics data that corrects for spatial confounding missed by existing methods, including both local and regional artifacts, across diverse technologies.
In this Analysis, Liu et al. benchmark more than 200 pairwise statistics for functional brain connectivity in tasks such as hub mapping, distance relationships, structure–function coupling and behavior prediction, revealing varying effectiveness for specific neurophysiological applications.
The Krakencoder can translate between structural and functional connectivity as well as between different atlases and processing choices via latent representations, as illustrated with datasets from the Human Connectome Project.
This work presents ReLiC, an RNA-linked CRISPR platform that enables dissection of RNA metabolic processes by targeting over 2,000 RNA-associated genes, revealing key regulators on RNA splicing, translation and decay.
Bioluminescence imaging with enhanced sensitivity, resolution and dynamic range is enabled by a camera based on quanta image sensor technology in combination with a dedicated unconventional microscope design. The capabilities of the QIScope are demonstrated in live imaging of extracellular vesicles or low-abundance proteins.
OmiCLIP is a visual–omics foundation model that integrates histology and spatial transcriptomics. The associated Loki platform offers accurate and robust tools for alignment, annotation, cell-type decomposition and spatial gene expression prediction.
InterpolAI leverages optimal flow-based artificial intelligence to produce synthetic images between pairs of images for diverse three-dimensional image types. InterpolAI is more robust and accurate than existing methods, improving data quality for downstream analysis.
SAVANA is a tool to detect somatic structural variants and copy number aberrations using long-read sequencing data, offering high sensitivity, specificity and compatibility with or without germline controls.
This work presents SUM-seq, an ultra-high-throughput method for co-profiling chromatin accessibility and gene expression in single nuclei across multiplexed samples, advancing the study of gene regulation in diverse biological systems.
Proseg is a segmentation approach for single-cell spatially resolved transcriptomics data that uses unsupervised probabilistic modeling of the spatial distribution of transcripts to accurately segment cells without the need for multimodal staining.
A suite of bridged rhodamine dyes (BriDyes) offers excellent brightness, solubility, photostability, and tunable cell permeability along with resistance to photoblueing, making them exceptional all-purpose dyes for fluorescence biomaging.
ALI is an approach for analyzing voltage imaging data that is inspired by algorithms used in super-resolution microscopy. It allows resolving the activity of single neurons in densely labeled populations in scattering conditions.
