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Author Correction: Subclassification of obesity for precision prediction of cardiometabolic diseases

Nature medicine 2025年第2期31卷 695页

作者： Daniel E Coral Femke Smit Ali Farzaneh Alexander Gieswinkel Juan Fernandez Tajes Thomas Sparsø Carl Delfin Pierre Bauvin Kan Wang Marinella Temprosa Diederik De Cock Jordi Blanch José Manuel Fernández-Real Rafael Ramos M Kamran Ikram Maria F Gomez Maryam Kavousi Marina Panova-Noeva Philipp S Wild Carla van der Kallen Michiel Adriaens Marleen van Greevenbroek Ilja Arts Carel Le Roux Fariba Ahmadizar Timothy M Frayling Giuseppe N Giordano Ewan R Pearson Paul W Franks Genetic and Molecular Epidemiology Unit Lund University Diabetes Centre Department of Clinical Science Lund University Helsingborg Sweden. daniel.coral@med.lu.se. Maastricht Centre for Systems Biology (MaCSBio) Maastricht University Maastricht The Netherlands. f.smit@maastrichtuniversity.nl. Department of Epidemiology Erasmus MC University Medical Center Rotterdam The Netherlands. Preventive Cardiology and Preventive Medicine Center for Cardiology University Medical Center of the Johannes Gutenberg-University Mainz Mainz Germany. Genetic and Molecular Epidemiology Unit Lund University Diabetes Centre Department of Clinical Science Lund University Helsingborg Sweden. Department of Pharmacometrics Novo Nordisk A/S Søborg Denmark. Université de Lille Inserm CHU Lille Institut Pasteur de Lille U1190-EGID Lille France. Biostatistics and Bioinformatics Milken Institute School of Public Health George Washington University Rockville MD USA. Biostatistics and Medical Informatics Research Group Department of Public Health Vrije Universiteit Brussel Brussels Belgium. Nutrition Eumetabolism and Health Group Institut d'Investigació Biomèdica de Girona (IDIBGI-CERCA) Girona Spain. Department of Medical Sciences University of Girona Girona Spain. CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN) Instituto de Salud Carlos III Madrid Spain. Department of Diabetes Endocrinology and Nutrition Dr. Josep Trueta University Hospital Girona Spain. Departments of Epidemiology Erasmus MC University Medical Center Rotterdam The Netherlands. Diabetic Complications Unit Lund University Diabetes Centre Department of Clinical Science Lund University Malmö Sweden. Translational Medicine and Clinical Pharmacology Boehringer Ingelheim Pharma GmbH & Co. KG Ingelheim am Rhein Germany. Center for Thrombosis and Haemostasis University Medical Center of the Johannes Gutenberg-University Mainz Mainz Germany. DZHK (German Center for Cardiovascular Research) Partner Site Rhine-Ma

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Publisher Correction: Age at onset as stratifier in idiopathic Parkinson's disease - effect of ageing and polygenic risk score on clinical phenotypes

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NPJ Parkinson's disease 2022年第1期8卷 112页

作者： L Pavelka A Rauschenberger Z Landoulsi S Pachchek P May E Glaab R Krüger Clinical and Experimental Neuroscience Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg. lukas.pavelka@uni.lu. Parkinson's Research Clinic Centre Hospitalier de Luxembourg (CHL) Luxembourg Luxembourg. lukas.pavelka@uni.lu. Biomedical Data Science Group Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg. Bioinformatics Core Luxembourg Centre for Systems Biomedicine (LCSB) Esch-sur-Alzette Luxembourg. Clinical and Experimental Neuroscience Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg. Clinical and Experimental Neuroscience Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg. rejko.krueger@uni.lu. Parkinson's Research Clinic Centre Hospitalier de Luxembourg (CHL) Luxembourg Luxembourg. rejko.krueger@uni.lu. Transversal Translational Medicine Luxembourg Institute of Health (LIH) Strassen Luxembourg. rejko.krueger@uni.lu.

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

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arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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Author Correction: Inferring structural variant cancer cell fraction

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Nature communications 2022年第1期13卷 7568页

作者： Marek Cmero Ke Yuan Cheng Soon Ong Jan Schröder Niall M Corcoran Tony Papenfuss Christopher M Hovens Florian Markowetz Geoff Macintyre Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. cmerom@unimelb.edu.au. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. cmerom@unimelb.edu.au. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. cmerom@unimelb.edu.au. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. cmerom@unimelb.edu.au. Murdoch Children's Research Institute Parkville VIC 3052 Australia. cmerom@unimelb.edu.au. School of Computing Science University of Glasgow Sir Alwyn Williams Building Glasgow G12 8RZ UK. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. Electrical and Electronic Engineering University of Melbourne Parkville VIC 3010 Australia. Machine Learning Research Group Data61 Canberra ACT 2601 Australia. Research School of Computer Science Australian National University Canberra ACT 2601 Australia. Bioinformatics Division The Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia. Department of Surgery Division of Urology Royal Melbourne Hospital and University of Melbourne Parkville VIC 3050 Australia. The Epworth Prostate Centre Epworth Hospital Richmond VIC 3121 Australia. Department of Computing and Information Systems University of Melbourne Parkville VIC 3010 Australia. geoff.macintyre@cruk.cam.ac.uk. Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE UK. geoff.macintyre@cruk.cam.ac.uk.

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas

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Revista Española de Cardiología (English Edition) 2021年第9期74卷 790-799页

作者： Matthew J. Page Joanne E. McKenzie Patrick M. Bossuyt Isabelle Boutron Tammy C. Hoffmann Cynthia D. Mulrow Larissa Shamseer Jennifer M. Tetzlaff Elie A. Akl Sue E. Brennan Roger Chou Julie Glanville Jeremy M. Grimshaw Asbjørn Hróbjartsson Manoj M. Lalu Tianjing Li Elizabeth W. Loder Evan Mayo-Wilson David Moher School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Clinical Epidemiology Biostatistics and Bioinformatics Amsterdam University Medical Centres University of Amsterdam Amsterdam Países Bajos Université de Paris Centre of Epidemiology and Statistics (CRESS) Inserm F 75004 Paris Francia Institute for Evidence-Based Healthcare Faculty of Health Sciences and Medicine Bond University Gold Coast Australia University of Texas Health Science Center at San Antonio San Antonio Texas Estados Unidos Annals of Internal Medicine Knowledge Translation Program Li Ka Shing Knowledge Institute Toronto Canadá School of Epidemiology and Public Health Faculty of Medicine University of Ottawa Ottawa Canada Evidence Partners Ottawa Canadá Clinical Research Institute American University of Beirut Beirut Líbano Department of Health Research Methods Evidence and Impact McMaster University Hamilton Ontario Canadá Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University Portland Oregón Estados Unidos York Health Economics Consortium (YHEC Ltd) University of York York Reino Unido Clinical Epidemiology Program Ottawa Hospital Research Institute Ottawa Canadá School of Epidemiology and Public Health University of Ottawa Ottawa Canadá Department of Medicine University of Ottawa Ottawa Canadá Centre for Evidence-Based Medicine Odense Odense University Hospital Odense Dinamarca Department of Clinical Research University of Southern Denmark Odense Dinamarca Open Patient data Explorative Network Odense University Hospital Odense Dinamarca Department of Anesthesiology and Pain Medicine The Ottawa Hospital Ottawa Canadá Clinical Epidemiology Program Blueprint Translational Research Group Ottawa Hospital Research Institute Ottawa Canadá Regenerative Medicine Program Ottawa Hospital Research Institute Ottawa Canadá Department of Ophthalmology School of Medicine University of Colorado Denver Den

La declaración PRISMA ( Preferred Reporting Items for Systematic reviews and Meta-Analyses ), publicada en 2009, se diseñó para ayudar a los autores de revisiones sistemáticas a documentar de manera transparente el porqué de la revisión, qué hicieron los autores y qué encontraron. Durante la última década, ha habido muchos avances en la metodología y terminología de las revisiones sistemáticas, lo que ha requerido una actualización de esta guía. La declaración prisma 2020 sustituye a la declaración de 2009 e incluye una nueva guía de presentación de las publicaciones que refleja los avances en los métodos para identificar, seleccionar, evaluar y sintetizar estudios. La estructura y la presentación de los ítems ha sido modificada para facilitar su implementación. En este artículo, presentamos la lista de verificación PRISMA 2020 con 27 ítems, y una lista de verificación ampliada que detalla las recomendaciones en la publicación de cada ítem, la lista de verificación del resumen estructurado PRISMA 2020 y el diagrama de flujo revisado para revisiones sistemáticas. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews. Full English text available from : ***

关键词： Guía de publicación Revisión sistemática Metanálisis Escritura médica Publishing guide Systematic review Meta-analysis Medical writing

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Author Correction: High p16 expression and heterozygous RB1 loss are biomarkers for CDK4/6 inhibitor resistance in ER+ breast cancer

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Nature communications 2022年第1期13卷 6928页

作者： Marta Palafox Laia Monserrat Meritxell Bellet Guillermo Villacampa Abel Gonzalez-Perez Mafalda Oliveira Fara Brasó-Maristany Nusaibah Ibrahimi Srinivasaraghavan Kannan Leonardo Mina Maria Teresa Herrera-Abreu Andreu Òdena Mònica Sánchez-Guixé Marta Capelán Analía Azaro Alejandra Bruna Olga Rodríguez Marta Guzmán Judit Grueso Cristina Viaplana Javier Hernández Faye Su Kui Lin Robert B Clarke Carlos Caldas Joaquín Arribas Stefan Michiels Alicia García-Sanz Nicholas C Turner Aleix Prat Paolo Nuciforo Rodrigo Dienstmann Chandra S Verma Nuria Lopez-Bigas Maurizio Scaltriti Monica Arnedos Cristina Saura Violeta Serra Experimental Therapeutics Group Vall d'Hebron Institute of Oncology Barcelona Spain. Breast Cancer and Melanoma Group Vall d'Hebron Institute of Oncology Barcelona Spain. Department of Medical Oncology Hospital Vall d'Hebron Barcelona Spain. Oncology Data Science Group Vall d'Hebron Institute of Oncology Barcelona Spain. Institute for Research in Biomedicine (IRB Barcelona) Barcelona Spain. Research Program on Biomedical Informatics Universitat Pompeu Fabra Barcelona Spain. Translational Genomics and Targeted Therapies in Solid Tumors August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Spain. Service de Biostatistique et d'Epidémiologie Gustave Roussy Villejuif France. Oncostat U1018 Inserm University Paris-Saclay Villejuif France. Bioinformatics Institute (A*STAR) Singapore Singapore. Medica Scientia Innovation Research (MedSIR) Barcelona Spain. The Breast Cancer Now Research Centre London UK. Preclinical Modelling of Pediatric Cancer Evolution Group The Institute of Cancer Research London UK. Translational Molecular Pathology Vall d'Hebron Institute of Research (VHIR) Barcelona Spain. Novartis Pharmaceuticals East Hanover NJ USA. Genentech Inc. South San Francisco California USA. Breast Biology Group Manchester Breast Centre Manchester UK. Cancer Research UK Cambridge UK. CIBERONC Vall d'Hebron Institute of Oncology Barcelona Spain. Growth Factors Laboratory Vall d'Hebron Institute of Oncology Barcelona Spain. Department of Biochemistry and Molecular Biology Universitat Autònoma de Barcelona Barcelona Spain. IMIM (Hospital del Mar Medical Research Institute) Barcelona Spain. Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. University of Barcelona Barcelona Spain. Department of Medical Oncology Hospital Clinic Barcelona Spain. SOLTI Breast Cancer Research Group Barcelona Spain. Department of Oncology IOB Institute of Oncology Barcelona Spain. Molecular Oncology Group Vall d'Hebron Institute of On

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Best match graphs and reconciliation of gene trees with species trees

arXiv

引用

arXiv 2019年

作者： Geiß, Manuela González, Marcos E. Sánchez, Alitzel López Valdivia, Dulce I. Hellmuth, Marc Rosales, Maribel Hernández Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center of Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany CONACYT-Instituto de Matemáticas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Querétaro QRO76230 Mexico Universidad Autónoma de Aguascalientes Centro de Ciencias Básicas Av. Universidad 940 Aguascalientes AGS20131 Mexico Instituto de Matemáticas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Querétaro QRO76230 Mexico Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Straße 47 GreifswaldD-17487 Germany Center for Bioinformatics Saarland University Building E 2.1 P.O. Box 151150 SaarbrückenD-66041 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Research Center for Civilization Diseases Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max-Planck-Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

A wide variety of problems in computational biology, most notably the assessment of orthology, are solved with the help of reciprocal best matches. Using an evolutionary definition of best matches that captures the intuition behind the concept we clarify rigorously the relationships between reciprocal best matches, orthology, and evolutionary events under the assumption of duplication/loss scenarios. We show that the orthology graph is a subgraph of the reciprocal best match graph (RBMG). We furthermore give conditions under which an RBMG that is a cograph identifies the correct orthlogy relation. Using computer simulations we find that most false positive orthology assignments can be identified as so-called good quartets - and thus corrected - in the absence of horizontal transfer. Horizontal transfer, however, may introduce also false-negative orthology assignments. Copyright © 2019, The Authors. All rights reserved.

关键词： Phylogenetic

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Introducing evolutionary biologists to the analysis of big data: Guidelines to organize extended bioinformatics training courses

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Evolution: Education and Outreach 2018年第1期11卷 1-10页

作者： Faria, Rui Triant, Deborah Perdomo-Sabogal, Alvaro Overduin, Bert Bleidorn, Christoph Santana, Clara Isabel Bermudez Langenberger, David Indrischek, Henrike Aerts, Jan Engelhardt, Jan Engelken, Johannes Liebal, Katja Fasold, Mario Robb, Sofia Grath, Sonja Kolora, Sree Rohit Raj Carvalho, Tiago Salzburger, Walter Jovanovic, Vladimir Nowick, Katja CIBIO Centro de Investigação em Biodiversidade e Recursos Genéticos InBIO Laboratório Associado Universidade Do Porto Campus Agrário de Vairão Vairão 4485-661 Portugal IBE Institute of Evolutionary Biology (CSIC-UPF) Department of Experimental and Health Sciences Pompeu Fabra University Doctor Aiguader 88 Barcelona 08003 Spain Department of Animal and Plant Sciences University of Sheffield Sheffield United Kingdom Department of Biochemistry and Molecular Genetics University of Virginia Charlottesville 22908 VA United States TFome Research Group Bioinformatics Group Interdisciplinary Center of Bioinformatics Department of Computer Science University of Leipzig Härtelstrasse 16-18 Leipzig 04107 Germany Paul-Flechsig-Institute for Brain Research University of Leipzig Jahnallee 59 Leipzig 04109 Germany Faculty for Biology Chemistry and Pharmacy Freie Universität Berlin Königin-Luise-Straße 1-3 Berlin 14195 Germany Edinburgh Genomics University of Edinburgh Ashworth Laboratories King's Buildings Charlotte Auerbach Road Edinburgh EH9 3FL United Kingdom Museo Nacional de Ciencias Naturales Spanish Research Council (CSIC) José Gutiérrez Abascal 2 Madrid 28006 Spain German Centre for Integrative Biodiversity Research (IDiv) Halle-Jena-Leipzig Deutscher Platz 5e Leipzig 04103 Germany Grupo Rnomica Teórica y Computacional Departamento de Biologia Universidad Nacional de Colombia Bogota Colombia EcSeq Bioinformatics GmbH Sternwartenstraße 29 Leipzig 04103 Germany Computational Biology Department of Target Sciences GlaxoSmithKline Gunnels Wood Road Stevenage SG1 2NY United Kingdom Computational EvoDevo Group Department of Computer Science University of Leipzig Härtelstrasse 16-18 Leipzig 04107 Germany Bioinformatics Group Department of Computer Science University of Leipzig Härtelstrasse 16-18 Leipzig 04107 Germany Visual Data Analysis Lab ESAT/STADIUS KU Leuven Kasteelpark Arenberg 10 Leuven 3001 Belgium Imec KU Leuven Leuven Belgiu

Research in evolutionary biology has been progressively influenced by big data such as massive genome and transcriptome sequencing data, scalar measurements of several phenotypes on tens to thousands of individuals, as well as from collecting worldwide environmental data at an increasingly detailed scale. The handling and analysis of such data require computational skills that usually exceed the abilities of most traditionally trained evolutionary biologists. Here we discuss the advantages, challenges and considerations for organizing and running bioinformatics training courses of 2-3 weeks in length to introduce evolutionary biologists to the computational analysis of big data. Extended courses have the advantage of offering trainees the opportunity to learn a more comprehensive set of complementary topics and skills and allowing for more time to practice newly acquired competences. Many organizational aspects are common to any course, as the need to define precise learning objectives and the selection of appropriate and highly motivated instructors and trainees, among others. However, other features assume particular importance in extended bioinformatics training courses. To successfully implement a learning-by-doing philosophy, sufficient and enthusiastic teaching assistants (TAs) are necessary to offer prompt help to trainees. Further, a good balance between theoretical background and practice time needs to be provided and assured that the schedule includes enough flexibility for extra review sessions or further discussions if desired. A final project enables trainees to apply their newly learned skills to real data or case studies of their interest. To promote a friendly atmosphere throughout the course and to build a close-knit community after the course, allow time for some scientific discussions and social activities. In addition, to not exhaust trainees and TAs, some leisure time needs to be organized. Finally, all organization should be done while keeping

关键词： Active learning bioinformatics Evolutionary biology Extended course Genomics High-throughput-sequencing Programming Sustainable course

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