Nature Methods Nature Methods offers a unique interdisciplinary forum for the publication of novel methods. Nature Methods focuses on the life sciences, combining practical, technique-driven subject matter with rigorous peer-review standards to ensure that readers are consistently presented with only the most valuable and highest quality methodological research. The journal offers its readers primary research papers as well as an array of opinions, reviews and short journalistic pieces to provide busy researchers with a broad, yet easily absorbed perspective of important methodological developments in the life sciences. http://feeds.nature.com/nmeth/rss/current Nature Publishing Group en © 2025 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Methods © 2025 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. [email protected]
  • Nature Methods https://www.nature.com/uploads/product/nmeth/rss.gif http://feeds.nature.com/nmeth/rss/current <![CDATA[The Hodge Laplacian advances inference of single-cell trajectories]]> https://www.nature.com/articles/s41592-025-02858-1 <![CDATA[
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    Nature Methods, Published online: 23 October 2025; doi:10.1038/s41592-025-02858-1

    A recently proposed Hodge Laplacian model has advanced single-cell multimodal data analysis by providing highly reliable results for complex multi-branching trajectories.]]> <![CDATA[The Hodge Laplacian advances inference of single-cell trajectories]]> Kelin Xia doi:10.1038/s41592-025-02858-1 Nature Methods, Published online: 2025-10-23; | doi:10.1038/s41592-025-02858-1 2025-10-23 Nature Methods 10.1038/s41592-025-02858-1 https://www.nature.com/articles/s41592-025-02858-1 <![CDATA[PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories]]> https://www.nature.com/articles/s41592-025-02870-5 <![CDATA[

    Nature Methods, Published online: 23 October 2025; doi:10.1038/s41592-025-02870-5

    PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.]]>     <![CDATA[PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories]]> Mingbo ChengJitske JansenKatharina C. ReimerVincent P. GrandeJames S. NagaiZhijian LiPaul KießlingMartin GrasshoffChristoph KuppeMichael T. SchaubRafael KramannIvan G. Costa doi:10.1038/s41592-025-02870-5 Nature Methods, Published online: 2025-10-23; | doi:10.1038/s41592-025-02870-5 2025-10-23 Nature Methods 10.1038/s41592-025-02870-5 https://www.nature.com/articles/s41592-025-02870-5     <![CDATA[scooby: modeling multimodal genomic profiles from DNA sequence at single-cell resolution]]> https://www.nature.com/articles/s41592-025-02854-5 <![CDATA[

    Nature Methods, Published online: 22 October 2025; doi:10.1038/s41592-025-02854-5

    scooby achieves DNA sequence-based single-cell level modeling of RNA-sequencing coverage and ATAC-sequencing insertion profiles by adapting a deep learning model that predicts bulk RNA-sequencing coverage.]]>     <![CDATA[scooby: modeling multimodal genomic profiles from DNA sequence at single-cell resolution]]> Johannes C. HingerlLaura D. MartensAlexander KarollusTrevor ManzJason D. BuenrostroFabian J. TheisJulien Gagneur doi:10.1038/s41592-025-02854-5 Nature Methods, Published online: 2025-10-22; | doi:10.1038/s41592-025-02854-5 2025-10-22 Nature Methods 10.1038/s41592-025-02854-5 https://www.nature.com/articles/s41592-025-02854-5     <![CDATA[CELLECT: contrastive embedding learning for large-scale efficient cell tracking]]> https://www.nature.com/articles/s41592-025-02886-x <![CDATA[

    Nature Methods, Published online: 20 October 2025; doi:10.1038/s41592-025-02886-x

    CELLECT learns contrastive embeddings to enable high-fidelity cell tracking.]]>     <![CDATA[CELLECT: contrastive embedding learning for large-scale efficient cell tracking]]> Hongyu ZhouSeonghoon KimZhifeng ZhaoJiaqi FanWen HuangXinghua SuiLizhi ShaoHaoran AnJing-Ren ZhangJiamin WuQionghai Dai doi:10.1038/s41592-025-02886-x Nature Methods, Published online: 2025-10-20; | doi:10.1038/s41592-025-02886-x 2025-10-20 Nature Methods 10.1038/s41592-025-02886-x https://www.nature.com/articles/s41592-025-02886-x     <![CDATA[gReLU: a comprehensive framework for DNA sequence modeling and design]]> https://www.nature.com/articles/s41592-025-02868-z <![CDATA[

    Nature Methods, Published online: 15 October 2025; doi:10.1038/s41592-025-02868-z

    gReLU advances deep-learning-based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications.]]>     <![CDATA[gReLU: a comprehensive framework for DNA sequence modeling and design]]> Avantika LalLaura GunsalusSurag NairTommaso BiancalaniGokcen Eraslan doi:10.1038/s41592-025-02868-z Nature Methods, Published online: 2025-10-15; | doi:10.1038/s41592-025-02868-z 2025-10-15 Nature Methods 10.1038/s41592-025-02868-z https://www.nature.com/articles/s41592-025-02868-z     <![CDATA[Multitask benchmarking of single-cell multimodal omics integration methods]]> https://www.nature.com/articles/s41592-025-02856-3 <![CDATA[

    Nature Methods, Published online: 13 October 2025; doi:10.1038/s41592-025-02856-3

    This Registered Report compares computational methods for single-cell multimodal omics integration and provides recommendations for different tasks and scenarios.]]>     <![CDATA[Multitask benchmarking of single-cell multimodal omics integration methods]]> Chunlei LiuSichang DingHani Jieun KimSiqu LongDi XiaoShila GhazanfarPengyi Yang doi:10.1038/s41592-025-02856-3 Nature Methods, Published online: 2025-10-13; | doi:10.1038/s41592-025-02856-3 2025-10-13 Nature Methods 10.1038/s41592-025-02856-3 https://www.nature.com/articles/s41592-025-02856-3     <![CDATA[Deep generative modeling of sample-level heterogeneity in single-cell genomics]]> https://www.nature.com/articles/s41592-025-02808-x <![CDATA[

    Nature Methods, Published online: 13 October 2025; doi:10.1038/s41592-025-02808-x

    MrVI, based on deep generative modelling, is a unified framework for integrative, exploratory and comparative analyses of large-scale (multi-sample) single-cell RNA-seq datasets.]]>     <![CDATA[Deep generative modeling of sample-level heterogeneity in single-cell genomics]]> Pierre BoyeauJustin HongAdam GayosoMartin KimJosé L. McFaline-FigueroaMichael I. JordanElham AziziCan ErgenNir Yosef doi:10.1038/s41592-025-02808-x Nature Methods, Published online: 2025-10-13; | doi:10.1038/s41592-025-02808-x 2025-10-13 Nature Methods 10.1038/s41592-025-02808-x https://www.nature.com/articles/s41592-025-02808-x     <![CDATA[Deciphering single-cell epigenomic language with a foundation model]]> https://www.nature.com/articles/s41592-025-02851-8 <![CDATA[

    Nature Methods, Published online: 10 October 2025; doi:10.1038/s41592-025-02851-8

    EpiAgent, a transformer-based foundation model pretrained on approximately 5 million cells and over 35 billion tokens, has advanced single-cell epigenomics by encoding chromatin accessibility as ‘cell sentences’. Benefiting from this framework, EpiAgent achieved state-of-the-art performance in typical downstream tasks and enabled perturbation response prediction and in silico chromatin region knockouts.]]>     <![CDATA[Deciphering single-cell epigenomic language with a foundation model]]> doi:10.1038/s41592-025-02851-8 Nature Methods, Published online: 2025-10-10; | doi:10.1038/s41592-025-02851-8 2025-10-10 Nature Methods 10.1038/s41592-025-02851-8 https://www.nature.com/articles/s41592-025-02851-8