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Biological techniques are methods or procedures that are used to study living things. They include experimental and computational methods, approaches, protocols and tools for biological research.
SANGO efficiently removed batch effects between the query and reference single-cell ATAC signals through the underlying genome sequences, to enable cell type assignment according to the reference data. The method achieved superior performance on diverse datasets and could detect unknown tumor cells, providing valuable functional biological signals.
We created DELiVR, a deep-learning pipeline for 3D brain-cell mapping that is trained with virtual reality-generated reference annotations. It can be deployed via the user-friendly interface of the open-source software Fiji, which makes the analysis of large-scale 3D brain images widely accessible to scientists without computational expertise.
Super-resolution imaging of reference and target structures enables precise determination of the labeling efficiency of high-affinity binding proteins in cells for improved quantitative assessment of protein organization at the single-molecule level.
Bock and colleagues perform integrative analysis of JAK-STAT mutant mice and find JAK-STAT signaling regulates CD8+ T cell and macrophage homeostasis by contributing to a poised epigenetic and transcription-regulatory state, preparing cells to rapidly respond to stimuli.
BEAN is a Bayesian approach for analyzing base editing screens with improved effect size quantification and variant classification. Applied to low-density lipoprotein (LDL)-associated common variants and saturation base editing of LDLR, BEAN identifies new LDL uptake genes and offers insights into variant structure–pathogenicity mechanisms.
Nature’s annual photography competition attracted stunning images from around the world, including two very different shots featuring the Polarstern research vessel.
SANGO efficiently removed batch effects between the query and reference single-cell ATAC signals through the underlying genome sequences, to enable cell type assignment according to the reference data. The method achieved superior performance on diverse datasets and could detect unknown tumor cells, providing valuable functional biological signals.
We created DELiVR, a deep-learning pipeline for 3D brain-cell mapping that is trained with virtual reality-generated reference annotations. It can be deployed via the user-friendly interface of the open-source software Fiji, which makes the analysis of large-scale 3D brain images widely accessible to scientists without computational expertise.