Volume 18 Issue 2, February 2021

Jumping down rabbit holes for a 3.5-Ångstrom peek at molecules.

Assembloids recapitulate organ spatial organization and interaction with high fidelity.

Global protein structure readouts help detect protein functional changes.

Loop-seq is a high-throughput sequencing assay that measures DNA looping and can help explain how DNA bendability contributes to nucleosome organization.

DNA-PAINT with left-handed DNA oligomers enables imaging of nuclear structures with reduced background.

DeepLabCut-Live! performs pose estimation in real time and allows closed-loop feedback on behavior.

Genome assembly projects get a boost from high-accuracy long-read sequencing.

A community-wide challenge yields recommendations for improving cryo-EM structure validation.

This Review describes proximity labeling methods that make use of peroxidases (APEX) or biotin ligases (TurboID, BioID), and their applications to studying protein–protein and protein–nucleic acid interactions in living systems.

The Perspective reviews the computational methods for subclonal reconstruction and recommends best practices for analyses and quality assessments.

A multi-laboratory study in the form of a community challenge assesses the quality of models that can be produced from cryo-EM maps using different software tools, the reproducibility of models generated by different users and the performance of metrics used for model validation.

This work presents a sequencing strategy based on unique molecular identifiers that improves long-read consensus sequence accuracy of targeted amplicons as well as shotgun whole-genome fragments.

Hifiasm is a haplotype-resolved de novo genome assembler for long-read high-fidelity sequencing data based on phased assembly graphs.

CryoDRGN is an unsupervised machine learning algorithm that reconstructs continuous distributions of three-dimensional density maps from heterogeneous single-particle cryo-EM data.

The software M establishes a reference-based multi-particle refinement framework for cryo-EM data. Combined with CTF correction and map denoising, M enables residue-level structure determination inside cells.

This study explores the performance of deep-learning models for super-resolution imaging and introduces models that utilize frequency content information in the Fourier domain to improve SIM reconstruction under low-SNR conditions.

nnU-Net is a deep learning-based image segmentation method that automatically configures itself for diverse biological and medical image segmentation tasks. nnU-Net offers state-of-the-art performance as an out-of-the-box tool.

EXODUS is a high-speed isolation method for the enrichment of exosome from biological fluids with high purity and yield.