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An in silico approach helps researchers identify biosynthetic gene clusters coding for bioactive small molecules from metagenomic data of the human microbiome.
The 2018 Human Protein Atlas Image Classification competition sought to improve automated classification of protein subcellular localizations from fluorescence images. The winning strategies involved innovative deep learning approaches for multi-label classification.
The unique advantages of single-particle cryo-electron microscopy and cryo-electron tomography are combined in a method called TYGRESS, here applied to determine the structure of the intact ciliary axoneme at a resolution of 12 Å.
An approach combining cryo-electron microscopy and mass spectrometry analysis of protein complexes enriched directly from cells enables structure determination of unknown complexes at atomic resolution.
Single-cell isolation following time-lapse imaging (SIFT) enables high-throughput screening of complex and dynamic phenotypes from pooled bacterial libraries. SIFT was used to generate ultraprecise synthetic gene oscillators.
A new method of autophagy measurement is based on the detection of phospho-ATG16L1, a conserved early marker of autophagy. Sensitive detection can be achieved in multiple biological systems and assays with advantages over standard methods.
A deep learning-based software tool, DIA-NN, enables deep proteome analysis from data generated using fast chromatographic approaches and data-independent acquisition mass spectrometry.
A miniaturized NMR-on-a-chip needle can be implanted into rodent brains and can measure blood flow and oxygenation changes in vivo in a small volume at an unprecedentedly high temporal resolution of a few milliseconds.
An alternative to focused ion beam scanning electron microscopy (FIB-SEM), gas cluster ion beam scanning electron microscopy (GCIB-SEM) is compatible with large tissue samples while achieving similar isotropic resolution.