A toolkit for modeling cis-regulatory logic of enhancers at large scale - Nature Methods Deciphering the genomic regulatory code driving cell type-specific gene regulation has been a research quest for decades. We present CREsted, a software package that provides data-driven insights into...

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CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species - Nature Methods CREsted is an efficient and user-friendly toolbox for analysis, modeling and design of cell-type-specific enhancers across diverse species.

CREsted: an efficient and user-friendly toolbox for analysis, modeling and design of cell-type-specific enhancers.

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Assessing uncertainty of sequence representations generated by protein language models - Nature Methods Language model-inferred embeddings are replacing structure-derived descriptions of proteins, genes and genomes. We propose a model-agnostic measure to quantify reliability of these new representations...

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Quantifying uncertainty in protein representations across models and tasks - Nature Methods A model-agnostic empirical framework is proposed to measure the uncertainty associated with protein embeddings and to assess the biological relevance of these embeddings in order to improve model reli...

A model-agnostic empirical framework measures the uncertainty associated with protein embeddings and assesses their biological relevance to improve model reliability. @yanabromberg.bsky.social

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AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network - Nature Methods ROCKET improves experimental structure elucidation by integrating implicit structural knowledge from OpenFold, a trainable reimplementation of AlphaFold2, with X-ray crystallographic and cryo-electron...

ROCKET improves experimental structure elucidation by integrating implicit structural knowledge from OpenFold with crystallography and cryo-EM data. @moalquraishi.bsky.social

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longcallR: Long-read RNA sequencing achieves accurate SNP calling, haplotype phasing and allele-specific expression analysis.

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Next-gen micropillars for investigating oligodendrocyte myelination - Nature Methods Tunable hydrogel-based micropillar arrays are fabricated to model the biomechanical cues of size, shape and rigidity characteristic of axons. These next-gen micropillars allow the exquisite examinatio...

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Tunable hydrogel-based micropillar arrays for myelination studies - Nature Methods To study myelination in vitro, a hydrogel-based micropillar array is used to mimic the stiffness and diameter of axons, with variations in these properties allowing evaluation of their influence. The ...

A hydrogel-based micropillar array allows studying myelination in vitro.

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Bridging the gap between hybrid and sequence-only protein language models - Nature Methods By allowing protein language models (PLMs) to learn from each other’s most confident predictions, we compressed the collective knowledge of existing PLMs into VESM — a single sequence-only model that ...

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Compressing the collective knowledge of ESM into a single protein language model - Nature Methods A co-distillation framework is used to iteratively adapt sequence-only protein language models for high-accuracy variant effect prediction, without the need for additional structural or genetic data. ...

A co-distillation framework is used to iteratively adapt sequence-only protein language models for high-accuracy variant effect prediction.

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Differentiation of sphingomyelin and cholesterol by hyperspectral mid-infrared detection of single-bond vibrational modes in the fingerprint region - Nature Methods Hyperspectral fingerprint optoacoustic microscopy enables the differentiation of different lipid classes in living cells, which is of interest in the study of lipid metabolism in health and disease.

Hyperspectral fingerprint optoacoustic microscopy differentiates lipid classes in live cells. @munichimaging.bsky.social

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3d-OT enables efficient alignment of heterogeneous multiomics tissue slices and spatial domain identification.

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Clustering the protein universe of life using DIAMOND DeepClust - Nature Methods DIAMOND DeepClust provides an ultra-fast clustering method for organizing the protein universe of life at low sequence identity, enabling large-scale dimensionality reduction and improving downstream ...

DIAMOND DeepClust: an ultrafast clustering method for organizing the protein universe of life.

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Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage - Nature Methods An integrated mass spectrometry platform enabling automated collision-, electron- and photon-based fragmentation techniques is presented. A deep learning-based model trained to predict spectra across ...

A mass spectrometry platform integrates alternative fragmentation techniques into standard LC-MS workflows for broader community adoption.

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Generating 3D models of complex carbohydrates with GLYCAM-Web - Nature Methods GLYCAM-Web offers a suite of validated 3D oligosaccharide structure modeling tools via a convenient web server.

GLYCAM-Web offers a suite of validated 3D oligosaccharide structure modeling tools via a convenient web server.

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LazySlide: accessible and interoperable whole-slide image analysis - Nature Methods LazySlide combines scverse with foundation models to enable efficient whole-slide image analysis.

LazySlide combines scverse with foundation models for efficient whole-slide image analysis.

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Pushing the boundaries of autonomous biological discovery - Nature Methods A new AI framework autonomously explores single-cell RNA sequencing datasets and produces creative, biologically relevant findings.

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CellVoyager: AI CompBio agent generates new insights by autonomously analyzing biological data - Nature Methods CellVoyager is an artificial intelligence agent capable of exploring new biological hypotheses by autonomously analyzing single-cell RNA sequencing datasets and accounting for background information a...

CellVoyager: an AI agent that explores new biological hypotheses by autonomously analyzing scRNA-seq datasets.

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In this month's Editorial, we highlight what's happened in the spatial proteomics field since we chose it as our Method of the Year 2024. www.nature.com/articles/s41...

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stVCR: spatiotemporal dynamics of single cells - Nature Methods stVCR models and reconstructs single-cell dynamics of cell differentiation, proliferation and migration using time-series spatial transcriptome data.

stVCR models and reconstructs single-cell dynamics of cell differentiation, proliferation and migration from time-series spatial transcriptome data.

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Live imaging of neuronal dynamics in transparent mouse brains - Nature Methods Making live mammalian tissues transparent for imaging experiments without compromising their normal cellular functions has been a long-standing challenge. An isotonic and minimally invasive optical cl...

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Isotonic and minimally invasive optical clearing media for live cell imaging ex vivo and in vivo - Nature Methods SeeDB-Live is a tissue-clearing approach for live samples such as tissue slices or the in vivo brain. It improves image quality while having minimal effects on electrophysiological properties of neuro...

SeeDB-Live is a tissue-clearing approach for live tissue slices or brains, improving image quality while having minimal effects on electrophysiological properties of neurons. @imailab.bsky.social

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Learning genes deeply - Nature Methods Annevo uses deep learning to achieve unprecedented accuracy in eukaryotic gene annotation, approaching the performance of evidence-based methods.

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Highly accurate ab initio gene annotation with ANNEVO - Nature Methods ANNEVO advances accurate and scalable ab initio gene annotation of evolutionarily diverse genomes using deep learning approach modeling sequence evolution and long-range dependencies and mixture of ex...

ANNEVO uses a deep learning approach to advance accurate and scalable ab initio gene annotation of evolutionarily diverse genomes.

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Connectome-seq: high-throughput mapping of neuronal connectivity at single-synapse resolution via barcode sequencing - Nature Methods Connectome-seq allows access to connectomic and transcriptomic information in high throughput, using an RNA barcoding strategy and isolation of synaptosomes, followed by single-nucleus and single-syna...

Connectome-seq uses RNA barcoding, synaptosome isolation, single-nucleus and single-synaptosome RNA sequencing to allow access to connectomic and transcriptomic information in high throughput.

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Our March issue is live! 🎉
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On the cover, the Antscan database consists of detailed synchrotron microCT images of diverse ant species.
Paper: www.nature.com/articles/s41...
Image: Thomas van de Kamp, Karlsruhe Institute of Technology

AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2 - Nature Methods AF2BIND is a logistic regression model trained on AlphaFold2 pair features to predict small-molecule binding-site residues in proteins, without multiple sequence alignments, homology models or knowled...

AF2BIND: a logistic regression model that predicts small-molecule binding site residues in proteins.

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Scaffolds with optimized quaternary symmetry for de novo cryoEM structure determination of small RNAs - Nature Methods Engineered RNA domains that can adopt two- and fourfold symmetry are shown to be effective scaffolds for structure determination for RNAs otherwise too small for cryoEM.

Engineered RNA domains are effective scaffolds for structure determination of RNAs that are otherwise too small for cryo-EM.

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