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There was insufficient data to support the claim of unexpected off-target effects due to CRISPR in a paper published in Nature Methods. More work is needed to determine whether such events occur in vivo.
In this CRISPR-based feedback control system, sgRNA expression is triggered by the burden of protein overexpression, and the sgRNA directs repression of the exogenous gene promoter to reduce burdensome expression and restore growth of the cell.
Optopharmacological manipulation with ‘caged’ glutamate and GABA has enabled the study of these ligands’ cognate receptors, but other ligands such as tertiary amine drugs have not been amenable to caging. A new strategy yields a photoactivatable nicotine, PA-Nic, which allows manipulation of nicotinic acetylcholine receptors.
The integration of quantum chemistry and molecular dynamics programs coupled with a graphical user interface provides a streamlined tool for powerful simulations of biomolecular reaction mechanisms.
Metagenomic mining generates a rich resource of regulatory sequences with species-selective and universal activity, making it possible to engineer synthetic circuits with tunable gene expression across diverse bacterial hosts.
A collection of 1,406 high-quality, immunoprecipitation- and immunoblotting-grade monoclonal antibodies to 737 human transcription factors is made available as a community resource, along with all validation data.
trendsceek identifies genes with significant spatial trends in single-cell spatial expression data, as well as in low-dimensional projections of dissociated single-cell RNA-seq data.
SpatialDE identifies genes with significant spatial expression patterns from multiplexed imaging or spatial RNA-sequencing data, and can cluster genes with similar spatial patterns as a form of expression-based tissue histology.
PRICE uses Ribo-seq data to predict ORFs and start codons with high accuracy by computationally eliminating experimental noise and dissecting overlapping translation events.
cellAlign enables quantitative comparisons of expression dynamics within and between single-cell trajectories based on single-cell RNA-seq or mass cytometry data.
Embedding a deep-learning model in the known structure of cellular systems yields DCell, a ‘visible’ neural network that can be used to mechanistically interpret genotype–phenotype relationships.
The combination of photoactivatable fluorescent markers with single-cell RNA-seq allows transcriptome analysis of cells from specific tissue locations.