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Graph Artificial Intelligence in Medicine

Biomedical Data Science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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Microchip-Based Structure Determination of Small Proteins Using Cryo-EM

SSRN

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SSRN 2020年

作者： Casasanta, Michael A. Jonaid, G.M. Kaylor, Liam Luqiu, William Y. Solares, Maria J. Schroen, Mariah L. Dearnaley, William J. Wilson, Jarad Dukes, Madeline J. Kelly, Deborah F. Department of Biomedical Engineering Pennsylvania State University University ParkPA16802 United States Materials Research Institute Pennsylvania State University University ParkPA16802 United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States RayBiotech Life Peachtree CornersGA30092 United States Applications Science Protochips Inc MorrisvilleNC27560 United States

Interest in cryo-Electron Microscopy (EM) imaging has skyrocketed in recent years due to its pristine views of macromolecules and materials. As advances in instrumentation and computing algorithms spurred this progress, there is renewed focus to address specimen-related challenges. Here we contribute a microchip-based toolkit to perform complementary structural and biochemical analysis on low molecular-weight proteins. As a model system, we used the SARS-CoV-2 nucleocapsid (N) protein (48 kDa) due to its stability and important role in therapeutic development. Cryo-EM structures of the N protein monomer revealed a flexible N-terminal “top hat” motif and a helical-rich C-terminal domain. To complement our structural findings, we engineered microchip-based immunoprecipitation assays that led to the discovery of the first antibody binding site on the N protein. The data also facilitated molecular modeling of a variety of pandemic and common cold-related coronavirus proteins. Such insights may guide future pandemic-preparedness protocols through immuno-engineering strategies to mitigate viral outbreaks. © 2020, The Authors. All rights reserved.

关键词： Proteins

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Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function (vol 571, pg 403, 2019)

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NATURE 2019年第7773期573卷 E2-E2页

作者： Bailis, Will Shyer, Justin A. Zhao, Jun Canaveras, Juan Carlos Garcia Al Khazal, Fatimah J. Qu, Rihao Steach, Holly R. Bielecki, Piotr Khan, Omair Jackson, Ruaidhri Kluger, Yuval Maher, Louis J., III Rabinowitz, Joshua Craft, Joe Flavell, Richard A. Department of Immunobiology Yale School of Medicine New Haven USA Department of Pathology Children’s Hospital of Philadelphia Philadelphia USA Department of Pathology Yale School of Medicine New Haven USA Program of Computational Biology and Bioinformatics Yale University New Haven USA Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton USA Department of Chemistry Princeton University Princeton USA Diabetes Research Center University of Pennsylvania Philadelphia USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and Science Rochester USA Applied Mathematics Program Yale University New Haven USA Department of Internal Medicine (Rheumatology) Yale School of Medicine New Haven USA Howard Hughes Medical Institute Chevy Chase USA

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

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The cell surface marker CD36 selectively identifies matured,mitochondria-rich hPSC-cardiomyocytes

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细胞研究（英文版） 2020年第7期30卷 626-629页

作者： Ellen Ngar-Yun Poon Xiao-ling Luo Sarah E.Webb Bin Yan Rui Zhao Stanley Chun Ming Wu Yong Yang Peng Zhang Huajun Bai Jiaofang Shao Ching Man Chan Godfrey Chi-Fung Chan Suk Ying Tsang Rebekah L.Gundry Huang-Tian Yang Kenneth R.Boheler Department of Medicine and Therapeutics and Centre for Cardiovascular Genomics and Medicine The Chinese University of Hong Kong (CUHK) HKSAR China CAS Key Laboratory of Tissue Microenvironment and Tumor Laboratory of Molecular CardiologyShanghai Institute of Nutrition and Health University of Chinese Academy of Sciences (CAS) CAS Shanghai China Division of Life Science and State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology HKSAR China Department of Computer Science The University of Hong Kong (HKU) HKSAR China Intervention and Cell Therapy Center Peking University Shenzhen Hospital Shenzhen China School of Life Sciences CUHK HKSAR China Department of Paediatrics and Adolescent Medicine HKU HKSAR China School of Biomedical Sciences HKU HKSAR China SUSTech Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology (SUSTech) Shenzhen China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical University Nanjing Jiangsu China State Key Laboratory of Agrobiotechnology CUHK HKSAR China Department of Biochemistry and Center for Biomedical Mass Spectrometry Research Medical College of Wisconsin Milwaukee WIUSA CardiOmics Program Center for Heart and Vascular Research Division of Cardiovascular Medicine and Department of Cellular and Integrative Physiology University of Nebraska Medical Center Omaha NE USA Institute for Stem Cell and Regeneration Medicine CASBeijing China Division of Cardiology Department of Medicineand Department of Biomedical Engineering The Johns Hopkins University Baltimore MD USA

Dear Editor,Human pluripotent stem cell (hPSC)-derived cardiomyocytes (CMs) are of significant translational value to in vitro studies of human cardiac development, drug and cardiotoxicity testing and cardiac disease *** of hPSCs to CMs,however, yields mixed cultures of atrial-, ventricular-, and pacemaker-like cells as well as non-CMs in variable proportions.1

关键词： Stem-cell differentiation Stem cells

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Author Correction: Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes

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Nature communications 2021年第1期12卷 827页

作者： Qingbo Wang Emma Pierce-Hoffman Beryl B Cummings Jessica Alföldi Laurent C Francioli Laura D Gauthier Andrew J Hill Anne H O'Donnell-Luria Konrad J Karczewski Daniel G MacArthur Program in Medical and Population Genetics The Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA 02114 USA. Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA 02115 USA. Program in Biomedical and Biological Sciences Harvard Medical School Boston MA 02115 USA. Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Department of Genome Sciences University of Washington Seattle WA 98195 USA. Program in Medical and Population Genetics The Broad Institute of MIT and Harvard Cambridge MA 02142 USA. danmac@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA 02114 USA. danmac@***. Centre for Population Genomics Garvan Institute of Medical Research and UNSW Sydney Sydney Australia. danmac@***. Centre for Population Genomics Murdoch Children's Research Institute Melbourne Australia. danmac@***.

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Single‐cell eQTLGen Consortium: A personalized understanding of disease

arXiv

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arXiv 2019年

作者： van der Wijst, M.G. de Vries, D.H. Groot, H.E. Trynka, G. Hon, C.C. Nawijn, M.C. Idaghdour, Y. van der Harst, P. Ye, C.J. Powell, J. Theis, F.J. Mahfouz, A. Heinig, M. Franke, L. Department of Genetics Oncode Institute University of Groningen University Medical Center Groningen Groningen Netherlands Department of Cardiology University of Groningen University Medical Center Groningen Groningen Netherlands Wellcome Trust Sanger Institute Open Targets Wellcome Genome Campus Hinxton Cambridge United Kingdom RIKEN Center for Integrative Medical Sciences Yokohama Japan Department of Pathology and Medical Biology GRIAC Research institute University of Groningen University Medical Center Groningen Netherlands Program in Biology Public Health Research Center New York University Abu Dhabi United Arab Emirates Institute for Human Genetics Bakar Computational Health Sciences Institute Bakar ImmunoX Initiative Division of Rheumatology Department of Medicine Department of Bioengineering and Therapeutic Sciences Department of Epidemiology and Biostatistics Chan Zuckerberg Biohub University of California San Francisco San Francisco United States Garvan‐Weizmann Centre for Cellular Genomics Garvan Institute Sydney Australia UNSW Cellular Genomics Futures Institute University of New South Wales Sydney Australia Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Mathematics Technical University of Munich Garching bei München Germany Leiden Computational Biology Center Leiden University Medical Center Leiden Netherlands Delft Bioinformatics Lab Delft University of Technology Delft Netherlands Institute of Computational Biology Helmholtz Zentrum München Neuherberg Germany Department of Informatics Technical University of Munich Garching bei München Germany

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease‐associated genetic risk factors through so‐called expression quantitative trait locus (eQTL) analysis. Single‐cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. The enormous increase in throughput and reduction in cost per cell now allow this technology to be applied to large‐scale population genetics studies. Therefore, we have founded the single‐cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing disease‐causing genetic variants and identifying the cellular contexts in which they affect gene expression. Ultimately, this information can enable development of personalized medicine. Here, we outline the goals, approach, potential utility and early proofs‐of-concept of the sc‐eQTLGen consortium. We also provide a set of study design considerations for future single‐cell eQTL studies. Copyright © 2019, The Authors. All rights reserved.

关键词： RNA

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Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder

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Nature Genetics 2025年 1-13页

作者： Strom, Nora I. Gerring, Zachary F. Galimberti, Marco Yu, Dongmei Halvorsen, Matthew W. Abdellaoui, Abdel Rodriguez-Fontenla, Cristina Sealock, Julia M. Bigdeli, Tim Coleman, Jonathan R. Mahjani, Behrang Thorp, Jackson G. Bey, Katharina Burton, Christie L. Luykx, Jurjen J. Zai, Gwyneth Alemany, Silvia Andre, Christine Askland, Kathleen D. Bäckman, Julia Banaj, Nerisa Barlassina, Cristina Nissen, Judith Becker Bienvenu, O. Joseph Black, Donald Bloch, Michael H. Børte, Sigrid Bosch, Rosa Breen, Michael Brennan, Brian P. Brentani, Helena Buxbaum, Joseph D. Bybjerg-Grauholm, Jonas Byrne, Enda M. Cabana-Dominguez, Judit Camarena, Beatriz Camarena, Adrian Cappi, Carolina Carracedo, Angel Casas, Miguel Cavallini, Maria Cristina Ciullo, Valentina Cook, Edwin H. Crosby, Jesse Cullen, Bernadette A. De Schipper, Elles J. Delorme, Richard Djurovic, Srdjan Elias, Jason A. Estivill, Xavier Falkenstein, Martha J. Fundin, Bengt T. Garner, Lauryn Gironda, Christina Goes, Fernando S. Grados, Marco A. Grove, Jakob Guo, Wei Haavik, Jan Hagen, Kristen Harrington, Kelly Havdahl, Alexandra Höffler, Kira D. Hounie, Ana G. Hucks, Donald Hultman, Christina Janecka, Magdalena Jenike, Eric Karlsson, Elinor K. Kelley, Kara Klawohn, Julia Krasnow, Janice E. Krebs, Kristi Lange, Christoph Lanzagorta, Nuria Levey, Daniel Lindblad-Toh, Kerstin Macciardi, Fabio Maher, Brion Mathes, Brittany McArthur, Evonne McGregor, Nathaniel McLaughlin, Nicole C. Meier, Sandra Miguel, Euripedes C. Mulhern, Maureen Nestadt, Paul S. Nurmi, Erika L. O’Connell, Kevin S. Osiecki, Lisa Ousdal, Olga Therese Palviainen, Teemu Pedersen, Nancy L. Piras, Federica Potluri, Sriramya Rabionet, Raquel Ramirez, Alfredo Rauch, Scott Reichenberg, Abraham Riddle, Mark A. Ripke, Stephan Rosário, Maria C. Sampaio, Aline S. Schiele, Miriam A. Skogholt, Anne Heidi Sloofman, Laura G. Smit, Jan Artigas, María Soler Thomas, Laurent F. Tifft, Eric Vallada, Homero van Kirk, Nathanial Veenstra-VanderWeele, Jeremy Vulink, Nienke N. Walker, Christopher P. Wang, Ying Wendland, Jens R. Winsvold Department of Psychology Humboldt-Universität zu Berlin Berlin Germany Department of Psychiatric Phenomics and Genomics (IPPG) Ludwig-Maximilians University Munich Munich Germany Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet and Stockholm Health Services Region Stockholm Stockholm Sweden Department of Biomedicine Aarhus University Aarhus Denmark University Hospital of Psychiatry and Psychotherapy University of Bern Bern Switzerland Department of Mental Health and Neuroscience Translational Neurogenomics QIMR Berghofer Medical Research Institute Brisbane QLD Australia Department of Population Health and Immunity Healthy Development and Ageing Walter and Eliza Hall Institute of Medical Research Melbourne VIC Australia Department of Psychiatry Human Genetics Yale University New Haven CT United States VA Connecticut Healthcare System West Haven CT United States Department of Center for Genomic Medicine Psychiatric and Neurodevelopmental Genetics Unit Massachusetts General Hospital Boston MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA United States Department of Genetics University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Psychiatry Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands Center for Research in Molecular Medicine and Chronic Diseases (CiMUS) Genomics and Bioinformatics University of Santiago de Compostela Santiago de Compostela Spain Grupo de Medicina Xenómica Genetics Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS) Santiago de Compostela Spain Vanderbilt Genetics Institute Vanderbilt University Nashville TN United States Department of Psychiatry and Behavioral Sciences SUNY Downstate Health Sciences University Brooklyn NY United States VA NY Harbor Healthcare System Brooklyn NY United States Social Genetic and Developmental Psychiatry Centre Insti

Obsessive–compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D₁ and D₂ type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index. © The Author(s) 2025.

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Erratum to 'Core Biopsies from Prostate Cancer Patients in Active Surveillance Protocols Harbor PTEN and MYC Alterations'[European Urology Oncology 2 (2019) 277-285]

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European urology oncology 2021年第1期4卷 129页

作者： Paolo Gandellini Nicola Casiraghi Tiziana Rancati Matteo Benelli Valentina Doldi Alessandro Romanel Maurizio Colecchia Cristina Marenghi Riccardo Valdagni Francesca Demichelis Nadia Zaffaroni Molecular Pharmacology Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Laboratory of Computational and Functional Oncology Centre for Integrative Biology (CIBIO) University of Trento Trento Italy. Prostate Cancer Program Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Bioinformatics Unit Hospital of Prato Prato Italy. Laboratory of Bioinformatics and Computational Genomics Centre for Integrative Biology (CIBIO) University of Trento Trento Italy. Department of Pathology and Laboratory Medicine Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Department of Radiation Oncology 1 Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Department of Oncology and Hemato-oncology Università degli Studi di Milano Milan Italy. Laboratory of Computational and Functional Oncology Centre for Integrative Biology (CIBIO) University of Trento Trento Italy. Electronic address: f.demichelis@unitn.it. Molecular Pharmacology Unit Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy. Electronic address: nadia.zaffaroni@istitutotumori.mi.it.

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Genome structures resolve the early diversification of teleost fishes

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Science (New York, N.Y.) 2023年第6632期379卷 572-575页

作者： Elise Parey Alexandra Louis Jerome Montfort Olivier Bouchez Céline Roques Carole Iampietro Jerome Lluch Adrien Castinel Cécile Donnadieu Thomas Desvignes Christabel Floi Bucao Elodie Jouanno Ming Wen Sahar Mejri Ron Dirks Hans Jansen Christiaan Henkel Wei-Jen Chen Margot Zahm Cédric Cabau Christophe Klopp Andrew W Thompson Marc Robinson-Rechavi Ingo Braasch Guillaume Lecointre Julien Bobe John H Postlethwait Camille Berthelot Hugues Roest Crollius Yann Guiguen Institut de Biologie de l'ENS (IBENS) Département de Biologie École Normale Supérieure CNRS INSERM Université PSL Paris France. INRAE LPGP Rennes France. GeT-PlaGe INRAE Genotoul Castanet-Tolosan France. Institute of Neuroscience University of Oregon Eugene OR USA. Department of Ecology and Evolution University of Lausanne Lausanne Switzerland. SIB Swiss Institute of Bioinformatics Lausanne Switzerland. State Key Laboratory of Developmental Biology of Freshwater Fish College of Life Science Hunan Normal University Changsha China. Florida Atlantic University Harbor Branch Oceanographic Institute Fort Pierce FL USA. Future Genomics Technologies Leiden Netherlands. Institute of Biology University of Leiden Leiden Netherlands. Faculty of Veterinary Medicine Norwegian University of Life Sciences Ås Norway. Institute of Oceanography National Taiwan University Taipei 10617 Taiwan. Sigenae GenPhySE INRAE ENVT Université de Toulouse Castanet Tolosan France. Sigenae Genotoul Bioinfo MIAT UR875 INRAE Castanet Tolosan France. Department of Integrative Biology Michigan State University East Lansing MI USA. Ecology Evolution and Behavior Program Michigan State University East Lansing MI USA. Department of Biological Sciences Western Michigan University Kalamazoo MI USA Institut Systématique Evolution Biodiversité (ISYEB) Muséum national d'Histoire naturelle CNRS SU EPHE UA Paris France. Institut Pasteur Université Paris Cité CNRS UMR 3525 INSERM UA12 Comparative Functional Genomics group F-75015 Paris France.

Accurate species phylogenies are a prerequisite for all evolutionary research. Teleosts are the largest and most diversified group of extant vertebrates, but relationships among their three oldest extant lineages remain unresolved. On the basis of seven high-quality new genome assemblies in Elopomorpha (tarpons, eels), we revisited the topology of the deepest branches of the teleost phylogeny using independent gene sequence and chromosomal rearrangement phylogenomic approaches. These analyses converged to a single scenario that unambiguously places the Elopomorpha and Osteoglossomorpha (arapaima, elephantnose fish) in a monophyletic sister group to all other teleosts, i.e., the Clupeocephala lineage (zebrafish, medaka). This finding resolves more than 50 years of controversy on the evolutionary relationships of these lineages and highlights the power of combining different levels of genome-wide information to solve complex phylogenies.

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