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Peer review is at the heart of publishing scientific papers. In this first installment of a two-part Editorial, we explain how we manage the process at Nature Methods.
Brown seaweeds are multicellular eukaryotes that have been evolving independently of animals and plants for more than a billion years. The filamentous brown alga Ectocarpus has been used as a model to understand the biology of these enigmatic organisms and to shed light on a range of major questions, from the molecular basis of complex developmental patterns to the evolution of sex.
Collaborations between researchers and companies can progress swimmingly and teams quickly validate findings and mature methods. All too often, things can’t advance and the ‘Valley of Death’ looms. New ways to collaborate, underpinned by computational muscle, can help.
We pinpoint PCR artifacts as the primary source of inaccurate quantification in both short- and long-read RNA sequencing, a problem that intensifies with an increase in PCR cycles in both bulk and single-cell sequencing contexts. To overcome this challenge, we engineered a novel unique molecular identifier (UMI) barcode composed of homotrimer nucleotide blocks. This design facilitates accurate quantification of RNA molecules, substantially improving molecular counting.
Interactions between RNA and RNA-binding proteins (RBPs) define the fate and function of every RNA molecule. We present TREX, or targeted RNase H-mediated extraction of crosslinked RBPs, an efficient and accurate method to unbiasedly reveal the protein interactors of specific regions of RNAs isolated from living cells.
We developed a prime editing (PE) strategy by incorporating a 5′–3′ exonuclease activity, which enhanced the efficacy and precision of ≥30-nucleotide DNA insertions without a secondary nick. Our optimization of the PE complex revealed that recruiting the exonuclease via an RNA aptamer outperformed direct protein fusions.
Intrinsically disordered regions of proteins are prevalent across the kingdoms of life; however, biophysical characterization is expensive, requiring specialized expertise and equipment and time-consuming sample preparation. By combining simulations and deep learning, we have developed a method to predict their average ensemble properties directly from sequence.
We developed Tapioca, an integrative ensemble machine learning-based framework, to accurately predict global protein–protein interaction network dynamics. Tapioca enabled the characterization of host regulation during reactivation from latency of an oncogenic virus. Introducing an interactome homology analysis method, we identified a proviral host factor with broad relevance for herpesviruses.
We developed a high-content profiling method named vibrational painting (VIBRANT) for single-cell drug response measurements, combining vibrational imaging, multiplexed vibrational probes and machine learning. VIBRANT showed high performance in predicting drug mechanisms of action, discovering novel compounds and assessing drug combinations, demonstrating great promise for phenotypic drug discovery.
This study introduces a method utilizing homotrimeric nucleotide blocks to achieve accurate counts of RNA molecules in both bulk and single-cell sequencing data.
Improvements to the fully genetically encoded Neonothopanusnambi bioluminescence pathway enhance autobioluminescence by up to two orders of magnitude in plants and other species, enabling novel applications of bioluminescence imaging in biology.
Single-cell structure probing of RNA transcripts enables simultaneous determination of transcript secondary structure and abundance in single cells, allowing new insights into RNA structural heterogeneity within and among cells.
RNA family sequence generator (RfamGen) is a deep generative model for designing novel, functional RNA sequences. RfamGen is applicable to diverse RNA families and can yield ribozymes with higher enzymatic activity.
Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) offers a general framework for estimating uncertainty for spatial gene expression predictions, enabling improved downstream analysis of spatially resolved transcriptomics data.
ALBATROSS is a deep-learning-based model for predicting ensemble properties of intrinsically disordered proteins and protein regions, such as radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences.
CombFold is a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2.
Tapioca is an ensemble machine learning framework for studying protein–protein interactions (PPIs) that facilitates integration of curve-based dynamic PPI data from thermal proximity coaggregation, ion-based proteome-integrated solubility alteration or cofractionation mass spectrometry data with static interaction data to predict PPIs in dynamic contexts.
Vibrational painting (VIBRANT) is a high-content single-cell phenotypic profiling method using mid-infrared imaging with vibrational probes for metabolic activity, which offers high accuracy with minimal batch effects to capture cellular responses to perturbation.
Micro-kiss (μkiss) is a micropipette-based approach for delivering very small amounts of nanoparticles and small molecules to the cell surface with exquisite spatiotemporal control, enabling a wide range of biological investigations.
MEISTER is an integrative experimental and computational framework for mass spectrometry that integrates three-dimensional, organ-wide biomolecular mapping with single-cell analysis for multiscale profiling of spatial–biochemical organization.