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Two community challenges assess the correctness of cryo-EM structures; future challenges should help determine the most appropriate structure validation methods.
A recent approach for single-cell RNA-sequencing data uses Bayesian deep learning to correct technical artifacts and enable accurate and multifaceted downstream analyses.
Inbred mice are preferred over outbred mice because it is assumed that they display less trait variability. We compared coefficients of variation and did not find evidence of greater trait stability in inbred mice. We conclude that contrary to conventional wisdom, outbred mice might be better subjects for most biomedical research.
An ex vivo 3D culture model of mycobacterial granulomas recapitulates the in vivo physiology of these structures and enables longitudinal imaging studies. The platform allows genetic and pharmacological manipulation of this key structure.
Imaging of neuronal activity across the whole zebrafish brain in combination with online analysis allows for manipulating neuronal activity according to function. This approach is used to ablate or activate neurons in fictively swimming zebrafish larvae.
The combination of positive and negative selection strategies, paired with the use of shRNAs to avoid random integration, allows efficient and scarless CRISPR-based homologous recombination.
A particle-filter algorithm for single-particle cryo-electron microscopy, implemented in a tool called THUNDER, provides high-dimensional parameter estimation, improving the obtainable resolution for several protein structures.
CellDMC finds cell type–specific differential methylation in mixtures of cells, including epithelial tissues, and scenarios in which methylation changes in opposite directions in different cell types.
scVI is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream analyses.
YETI puts individual -omics experiments in the context of public genomics data by creating an integrated dataset-specific functional network, thus allowing more thorough interpretation of the data.
Content-aware image restoration (CARE) uses deep learning to improve microscopy images. CARE bypasses the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.
The machine learning approach FIT leverages public mouse and human expression data to improve the translation of mouse model results to analogous human disease.