Pub Date : 2026-01-05DOI: 10.1038/s41576-025-00922-2
Valborg Gudmundsdottir
In this Journal Club, Valborg Gudmundsdottir recalls a study by Menche et al., who used a network-based approach to systematically identify clusters of connections between disease-related proteins and elucidate the molecular underpinnings of disease–disease relationships.
{"title":"Mapping the disease interactome","authors":"Valborg Gudmundsdottir","doi":"10.1038/s41576-025-00922-2","DOIUrl":"10.1038/s41576-025-00922-2","url":null,"abstract":"In this Journal Club, Valborg Gudmundsdottir recalls a study by Menche et al., who used a network-based approach to systematically identify clusters of connections between disease-related proteins and elucidate the molecular underpinnings of disease–disease relationships.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 3","pages":"191-191"},"PeriodicalIF":52.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145900780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1038/s41576-025-00925-z
David B. Brückner
In this Journal Club article, David Brückner discusses how seminal molecular genetic studies by Driever and Nüsslein-Volhard and Sick et al. demonstrated that both instructed (Wolpert model) and self-organized (Turing model) patterning occurs during animal development.
{"title":"From models to molecules: self-organized and instructed modes of developmental patterning","authors":"David B. Brückner","doi":"10.1038/s41576-025-00925-z","DOIUrl":"10.1038/s41576-025-00925-z","url":null,"abstract":"In this Journal Club article, David Brückner discusses how seminal molecular genetic studies by Driever and Nüsslein-Volhard and Sick et al. demonstrated that both instructed (Wolpert model) and self-organized (Turing model) patterning occurs during animal development.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 3","pages":"192-192"},"PeriodicalIF":52.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145897394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1038/s41576-025-00920-4
Daniel Dimitrov, Stefan Schrod, Martin Rohbeck, Oliver Stegle
Single-cell analyses have transitioned from descriptive atlasing towards inferring causal effects and mechanistic relationships that capture cellular logic. Technological advances and the growing scale of observational and interventional datasets have fuelled the development of machine learning methods aimed at identifying such dependencies and extrapolating perturbation effects. Here, we review and connect these approaches according to their modelling concepts (including representation learning, causal inference, mechanistic discovery, disentanglement and population tracing), underlying assumptions and downstream tasks. We propose a unifying ontology to guide practitioners in selecting the most suitable methods for a given biological question, with detailed technical descriptions provided in an online resource . Finally, we identify promising computational directions and underexplored data properties that could pave the way for future developments.
{"title":"Interpretation, extrapolation and perturbation of single cells.","authors":"Daniel Dimitrov, Stefan Schrod, Martin Rohbeck, Oliver Stegle","doi":"10.1038/s41576-025-00920-4","DOIUrl":"https://doi.org/10.1038/s41576-025-00920-4","url":null,"abstract":"<p><p>Single-cell analyses have transitioned from descriptive atlasing towards inferring causal effects and mechanistic relationships that capture cellular logic. Technological advances and the growing scale of observational and interventional datasets have fuelled the development of machine learning methods aimed at identifying such dependencies and extrapolating perturbation effects. Here, we review and connect these approaches according to their modelling concepts (including representation learning, causal inference, mechanistic discovery, disentanglement and population tracing), underlying assumptions and downstream tasks. We propose a unifying ontology to guide practitioners in selecting the most suitable methods for a given biological question, with detailed technical descriptions provided in an online resource . Finally, we identify promising computational directions and underexplored data properties that could pave the way for future developments.</p>","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":" ","pages":""},"PeriodicalIF":52.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145889741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1038/s41576-025-00927-x
Moritz Schaefer
In this Tools of the Trade article, Moritz Schaefer introduces CellWhisperer, a multimodal machine learning model and software tool for the chat-based interrogation of single-cell RNA sequencing datasets.
{"title":"Let the data speak — single-cell analysis with CellWhisperer","authors":"Moritz Schaefer","doi":"10.1038/s41576-025-00927-x","DOIUrl":"10.1038/s41576-025-00927-x","url":null,"abstract":"In this Tools of the Trade article, Moritz Schaefer introduces CellWhisperer, a multimodal machine learning model and software tool for the chat-based interrogation of single-cell RNA sequencing datasets.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 3","pages":"189-189"},"PeriodicalIF":52.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145889822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1038/s41576-025-00917-z
Genomic approaches have transformed how we study microorganisms, which shape nearly every aspect of life on Earth. This Focus issue explores the methods and insights gained from the application of microbial genomics within ecological and evolutionary contexts. Microbial genomics has yielded transformative insights into the ecology and evolution of microorganisms. Nature Reviews Genetics presents a Focus issue that explores how genomic approaches reveal microbial dynamics across ecological and evolutionary contexts.
{"title":"Microbial ecology and evolution in the genomics era","authors":"","doi":"10.1038/s41576-025-00917-z","DOIUrl":"10.1038/s41576-025-00917-z","url":null,"abstract":"Genomic approaches have transformed how we study microorganisms, which shape nearly every aspect of life on Earth. This Focus issue explores the methods and insights gained from the application of microbial genomics within ecological and evolutionary contexts. Microbial genomics has yielded transformative insights into the ecology and evolution of microorganisms. Nature Reviews Genetics presents a Focus issue that explores how genomic approaches reveal microbial dynamics across ecological and evolutionary contexts.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 1","pages":"1-2"},"PeriodicalIF":52.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41576-025-00917-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1038/s41576-025-00912-4
Kelly E. Blevins, María C. Ávila-Arcos, Verena J. Schuenemann, Anne C. Stone
Pathogen emergence and adaptation are constant, but the mechanisms underlying pathogen success as well as host susceptibility and resistance are often only observable in time series data. Ancient DNA research of pathogens and their hosts provides unique insights into past occurrences, including the changes that led to pathogen jumps between animals and humans, pandemic outbreaks, the timing of such events and the genetic, cultural and ecological factors that affect pathogen success and human survival and recovery. Recent technological improvements and the increasing number of ancient samples analysed have enabled the unprecedented investigation of pathogen evolution. Such studies are poised to benefit from the increased diversity of sequenced ancient pathogens, adoption of a broader framework that considers the entire ecosystem of host–pathogen interactions and growing collaboration with related fields. Ancient DNA techniques are being applied to study increasingly diverse pathogens of the past. The authors review the latest insights into pathogen–host coevolution, zoonotic events and the spread of pathogens, all while highlighting the importance of a One Health approach to this research.
{"title":"Ancient DNA insights into diverse pathogens and their hosts","authors":"Kelly E. Blevins, María C. Ávila-Arcos, Verena J. Schuenemann, Anne C. Stone","doi":"10.1038/s41576-025-00912-4","DOIUrl":"10.1038/s41576-025-00912-4","url":null,"abstract":"Pathogen emergence and adaptation are constant, but the mechanisms underlying pathogen success as well as host susceptibility and resistance are often only observable in time series data. Ancient DNA research of pathogens and their hosts provides unique insights into past occurrences, including the changes that led to pathogen jumps between animals and humans, pandemic outbreaks, the timing of such events and the genetic, cultural and ecological factors that affect pathogen success and human survival and recovery. Recent technological improvements and the increasing number of ancient samples analysed have enabled the unprecedented investigation of pathogen evolution. Such studies are poised to benefit from the increased diversity of sequenced ancient pathogens, adoption of a broader framework that considers the entire ecosystem of host–pathogen interactions and growing collaboration with related fields. Ancient DNA techniques are being applied to study increasingly diverse pathogens of the past. The authors review the latest insights into pathogen–host coevolution, zoonotic events and the spread of pathogens, all while highlighting the importance of a One Health approach to this research.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 1","pages":"96-111"},"PeriodicalIF":52.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1038/s41576-025-00924-0
Shubham Khetan
In this Tools of the Trade article, Shubham Khetan presents PADIT-seq (protein affinity to DNA by in vitro transcription and RNA sequencing), which enables the reliable identification of low-affinity DNA binding sites for transcription factors.
{"title":"Detecting transcription factor binding sites with PADIT-seq","authors":"Shubham Khetan","doi":"10.1038/s41576-025-00924-0","DOIUrl":"10.1038/s41576-025-00924-0","url":null,"abstract":"In this Tools of the Trade article, Shubham Khetan presents PADIT-seq (protein affinity to DNA by in vitro transcription and RNA sequencing), which enables the reliable identification of low-affinity DNA binding sites for transcription factors.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 2","pages":"114-114"},"PeriodicalIF":52.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145644915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1038/s41576-025-00918-y
J. Pamela Engelberts, Gene W. Tyson
Technical challenges and high costs remain barriers to the widespread application of microbial single-cell genomics. However, combining meta-omics approaches with single-cell genomics provides new opportunities to better understand microbial diversity, function and community dynamics. Engelberts and Tyson discuss the potential and challenges of microbial single-cell genomics, emphasizing the integration of single-cell omics and meta-omics data as a promising opportunity to reveal the ecological and evolutionary processes that shape microbial communities.
{"title":"Understanding microbial ecology and evolution with single-cell genomics","authors":"J. Pamela Engelberts, Gene W. Tyson","doi":"10.1038/s41576-025-00918-y","DOIUrl":"10.1038/s41576-025-00918-y","url":null,"abstract":"Technical challenges and high costs remain barriers to the widespread application of microbial single-cell genomics. However, combining meta-omics approaches with single-cell genomics provides new opportunities to better understand microbial diversity, function and community dynamics. Engelberts and Tyson discuss the potential and challenges of microbial single-cell genomics, emphasizing the integration of single-cell omics and meta-omics data as a promising opportunity to reveal the ecological and evolutionary processes that shape microbial communities.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 1","pages":"3-4"},"PeriodicalIF":52.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1038/s41576-025-00908-0
Yon Ho Jee, Yixuan He, Wenhan Lu, Yue Shi, Daniel Lazarev, Mark J. Daly, Mary Pat Reeve, Alicia R. Martin
{"title":"Dissecting pleiotropy to gain mechanistic insights into human disease","authors":"Yon Ho Jee, Yixuan He, Wenhan Lu, Yue Shi, Daniel Lazarev, Mark J. Daly, Mary Pat Reeve, Alicia R. Martin","doi":"10.1038/s41576-025-00908-0","DOIUrl":"https://doi.org/10.1038/s41576-025-00908-0","url":null,"abstract":"","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"175 1","pages":""},"PeriodicalIF":42.7,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1038/s41576-025-00907-1
Tyler Thomson, Gen Li, Amy Strilchuk, Haotian Cui, Bo Wang, Bowen Li
CRISPR-based genome editing technologies, including nuclease-based editing, base editing and prime editing, have revolutionized biological research and modern medicine by enabling precise, programmable modification of the genome and offering new therapeutic strategies for a wide range of genetic diseases. Artificial intelligence (AI), including machine learning and deep learning models, is now further advancing the field by accelerating the optimization of gene editors for diverse targets, guiding the engineering of existing tools and supporting the discovery of novel genome-editing enzymes. In this Review, we summarize key AI methodologies underlying these advances and discuss their recent noteworthy applications to genome editing technologies. We also discuss emerging opportunities, such as AI-powered virtual cell models, which can guide genome editing through target selection or prediction of functional outcomes. Finally, we identify key directions where the integration of AI methods is poised to have a substantial impact going forward. CRISPR-based genome editing has revolutionized biotechnology, enabling precise DNA modifications for research and therapy. The authors review how artificial intelligence, including deep learning, is advancing genome editing by improving guide RNA design, editor protein engineering, novel effector discovery and predicting editing outcomes.
{"title":"Harnessing artificial intelligence to advance CRISPR-based genome editing technologies","authors":"Tyler Thomson, Gen Li, Amy Strilchuk, Haotian Cui, Bo Wang, Bowen Li","doi":"10.1038/s41576-025-00907-1","DOIUrl":"10.1038/s41576-025-00907-1","url":null,"abstract":"CRISPR-based genome editing technologies, including nuclease-based editing, base editing and prime editing, have revolutionized biological research and modern medicine by enabling precise, programmable modification of the genome and offering new therapeutic strategies for a wide range of genetic diseases. Artificial intelligence (AI), including machine learning and deep learning models, is now further advancing the field by accelerating the optimization of gene editors for diverse targets, guiding the engineering of existing tools and supporting the discovery of novel genome-editing enzymes. In this Review, we summarize key AI methodologies underlying these advances and discuss their recent noteworthy applications to genome editing technologies. We also discuss emerging opportunities, such as AI-powered virtual cell models, which can guide genome editing through target selection or prediction of functional outcomes. Finally, we identify key directions where the integration of AI methods is poised to have a substantial impact going forward. CRISPR-based genome editing has revolutionized biotechnology, enabling precise DNA modifications for research and therapy. The authors review how artificial intelligence, including deep learning, is advancing genome editing by improving guide RNA design, editor protein engineering, novel effector discovery and predicting editing outcomes.","PeriodicalId":19067,"journal":{"name":"Nature Reviews Genetics","volume":"27 3","pages":"212-230"},"PeriodicalIF":52.0,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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