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Modern high-throughput metagenomics is producing hundreds of thousands of metagenome-assembled genomes (MAGs), which is overwhelming traditional sequence-similarity search methods. We present a computational method, skani, that efficiently compares MAGs on a terabyte scale while being robust to the inherent noise in MAGs, enabling larger and more accurate analyses.
Here we developed synthetic transactivation domains (TADs) built from human mechanosensitive transcription factors (MTFs). By linking MTF TAD segments together, we engineered compact and potent multipartite transcriptional activation modules. We then harnessed these modules to create a CRISPR activation system, which we termed the dCas9 recruited enhanced activation module (CRISPR-DREAM).
CryoREAD automatically builds DNA–RNA atomic structure from cryo-EM maps. Backbone accuracy is typically >85% and the method is applicable for maps with RNA-only, DNA-only and DNA–RNA–protein complex structures. CryoREAD uses deep learning to identify structure information and subsequently construct the 3D structure of nucleic acids.
We developed CellOT, a tool that integrates optimal transport with input convex neural networks to predict molecular responses of individual cells to various perturbations. By learning a map between the unpaired distributions of unperturbed and perturbed cells, CellOT outperforms current methods and generalizes the inference of treatment outcomes in unobserved cell types and patients.
We developed fatigue-resistant hydrogel optical fibers through the controlled growth of polymeric nanocrystalline domains to enable light delivery to peripheral nerves during locomotion. The hydrogel fibers withstand locomotion strain across more than 30,000 fiber stretch cycles and enable the optogenetic inhibition of pain hypersensitivity in naturally behaving mice.
We conducted a comprehensive long-read RNA sequencing (RNA-seq) benchmarking experiment by combining spike-ins and in silico mixtures to establish a ground-truth dataset. We used long- and short-read RNA-seq technology to deeply sequence samples and compared the performance of a range of analysis tools on these data.
This Perspective introduces advances in quantitative phase imaging and artificial intelligence-based image analysis and further describes how the two technologies intersect and synergize to enable biomedical research.
skani achieves fast calculation of average nucleotide identity (ANI) between metagenome-assembled genomes (MAGs), with improved robustness against incomplete and fragmented MAGs.
Tension-activated cell tagging (TaCT) is a new method that uses flow cytometry to sort mechanically active cells based on the forces generated by their surface adhesion receptors.
This work introduces microbial-enrichment methodology (MEM) that enables removal of host DNA in human intestinal biopsies and characterization of low-abundance microbial taxa down to 1%.
By learning representations for both cells and various condition covariates, scPoli facilitates atlas-level integration and analysis of single-cell genomics datasets with improved interpretability.
Guided sparse factor analysis (GSFA) is a powerful statistical framework to detect changes in gene expression as a result of perturbations in single-cell CRISPR screening.
A comprehensive redevelopment of the ribosome profiling workflow involves improved nuclease treatment and sequencing library preparation, enabling richer and more accurate translatome profiling with lower input and fewer technical hurdles.
OPUS-DSD is a neural network-based algorithm that reconstructs distinct conformations or continuous dynamics of the macromolecular structural landscape, starting from single-particle cryo-EM data.
Few methods for three-dimensional structure modeling of nucleic acids from cryo-EM data exist. CryoREAD, a fully automated DNA/RNA atomic structure modeling method based on deep learning, was developed to fill this gap.
A deep learning approach bypasses iterative trials associated with sensorless adaptive optics to compensate for wavefront deformations when imaging biological specimens, enabling improved deep tissue localization microscopy.
CellOT combines the benefits of optimal transport and input convex neural architectures to directly learn and uncover maps between control and perturbed cell states at the single-cell level.
This analysis leverages experimentally sequenced data and in silico mixtures to simulate transcript expression differences, which enables a performance assessment of long-read tools developed for isoform detection, differential transcript expression analysis and differential transcript usage analysis.
An updated version of the Waxholm Space atlas of the rat brain includes more detailed annotations of several brain regions, including the cortex, striatopallidal region, midbrain and thalamus, expanding the previous version with 112 new and 57 revised structures.