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Author Correction: A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

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Nature communications 2022年第1期13卷 7573页

作者： Wei Jiao Gurnit Atwal Paz Polak Rosa Karlic Edwin Cuppen Alexandra Danyi Jeroen de Ridder Carla van Herpen Martijn P Lolkema Neeltje Steeghs Gad Getz Quaid D Morris Lincoln D Stein Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada. Department of Molecular Genetics University of Toronto Toronto ON Canada. Vector Institute Toronto ON Canada. Broad Institute of MIT and Harvard Cambridge MA USA. Harvard Medical School Boston MA USA. Center for Cancer Research Massachusetts General Hospital Boston MA USA. Department of Oncological Sciences Icahn School of Medicine at Mount Sinai 15 1425 Madison Ave. New York NY USA. Bioinformatics Group Division of Molecular Biology Department of Biology Faculty of Science University of Zagreb Horvatovac 102a Zagreb Croatia. Hartwig Medical Foundation Science Park 408 Amsterdam The Netherlands. Center for Molecular Medicine and Oncode Institute University Medical Center Utrecht Utrecht The Netherlands. Center for Molecular Medicine University Medical Center Utrecht Utrecht The Netherlands. Radboud University Medical Center Nijmegen The Netherlands. Department of Medical Oncology Erasmus MC Cancer Institute University Medical Center Rotterdam Dr. Molewaterplein 40 3015 GD Rotterdam The Netherlands. Department of Medical Oncology The Netherlands Cancer Institute Plesmanlaan 121 1066 CX Amsterdam The Netherlands. Department of Pathology Massachusetts General Hospital Boston MA USA. University of Toronto Toronto ON Canada. Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada. lincoln.stein@***. Department of Molecular Genetics University of Toronto Toronto ON Canada. lincoln.stein@***.

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Formalizing the representation of immune exposures for human immunology studies 9

Formalizing the representation of immune exposures for human...

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9th International Conference on Biological Ontology, ICBO 2018

作者： Vita, Randi Overton, James A. Cheung, Kei-Hoi Kleinstein, Steven H. Peters, Bjoern Division for Vaccine Discovery San Diego Institute for Allergy and Immunology San DiegoCA United States Department of Emergency Medicine Yale Center for Medical Informatics Yale School of Medicine New HavenCT United States Department of Pathology Yale School of Medicine New HavenCT United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT United States

Human immunology studies typically examine how immune exposures associated with vaccinations, infectious, allergic or autoimmune diseases, or transplantations perturb the immune system with the goal to develop diagnostic tools and therapeutic interventions. While there are established approaches to formally represent the experimental data generated in such studies, which often comprises gene expression data, flow cytometry data, or serology data, the description of the immune exposures themselves is not well standardized. We here present a formal approach to represent immune exposures at a high level of granularity. We capture the exposure process (e.g. 'vaccination' or 'occurrence of allergic disease'), exposure material (e.g. 'Tdap vaccine' or 'House dust mite'), and the associated disease name and stage (e.g. 'allergic rhinitis' and 'chronic'). This representation scheme has been used successfully in the IEDB and an extended version has been adopted by HIPC to capture studies in ImmPort. We are reporting here on this scheme, our ongoing attempts to map the terms used to existing ontologies, and the challenges encountered. © 2018 CEUR-WS.

关键词： Ontology

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Identification of Genetic Variants and Serum Metabolites Associated with Blood-derived Mitochondrial DNA Copy Number

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Alzheimer's & Dementia 2022年第S6期18卷

作者： Tong Tong John Farrell Congcong Zhu Zainab Khurshid Eden R. Martin William S. Bush Margaret A. Pericak-Vance Li-San Wang Gerard D. Schellenberg Jonathan L. Haines Kathryn L. Lunetta Lindsay A. Farrer Xiaoling Zhang Disease Sequencing Project Boston University School of Medicine Boston MA USA Boston University Bioinformatics Program Boston MA USA John P. Hussman Institute for Human Genomics University of Miami Miller School of Medicine Miami FL USA Case Western Reserve University School of Medicine Cleveland OH USA John P. Hussman Institute for Human Genomics Miller School of Medicine University of Miami Miami FL USA University of Pennsylvania Perelman School of Medicine Philadelphia PA USA University of Pennsylvania Perelman School of Medicine Path & Lab Med Stellar Chance Philadelphia PA USA Case Western Reserve University School of Medicine Department of Population & Quantitative Health Sciences Cleveland Institute for Computational Biology Cleveland OH USA Boston University School of Public Health Boston MA USA

Background Mitochondrial DNA Copy Number (mtDNA-CN) can be used to estimate mitochondrial function and it varies across tissues and cell types. Blood-derived mtDNA-CN has been studied in aging-related diseases including Alzheimer’s Disease (AD). However, little is known about the genetic contribution to variation of blood-derived mtDNA-CN and how this variation affects the level of serum metabolites. Method We developed a fast computational pipeline to accurately estimate mtDNA-CN using whole genome sequence data from blood samples from European ancestry (EA, 2,885 AD cases and 2,777 controls), African American (AA, 1,118 AD cases and 1,725 controls), and Caribbean Hispanic (CH, 1,022 AD cases and 1,949 controls) participants of the Alzheimer’s Disease Sequencing Project (ADSP). In the combined sample and within each population group. We tested association of blood-derived mtDNA-CN with 17,569,705 variants (minor allele count≥20) genome-wide using a linear mixed effect model including covariates for age, sex, sequencing center, PCR, AD status, polygenic risk score of blood cell count, and principal components of ancestry. We also tested the associations of mtDNA-CN and levels of 175 serum metabolites in 1,521 subjects from Alzheimer’s Disease Neuroimaging Initiative. Result In the total sample, we identified genome-wide significant (GWS) associations with variants from 24 independent loci. The top-ranked SNP (rs1455130415 in NDUFS8 , (p=3.25x10 -42 ) was also GWS in EAs (p=3.96x10 -28 ) and AAs (p=2.20x10 -27 ). SNP rs997412864 (p=5.13x10 -9 ) in GCAT was found associated with mtDNA-CN in EAs, but not in other groups. Association of SNP rs1237233197 (p=1.40x10 -8 ) in NDUFS7 was specific to AAs. Measurements of two long-chain acylcarnitine, C14:2-OH (p=4.91x10 -7 ) and C18:2 (p=1.81x10 -5 ), were negatively associated with mtDNA-CN after Bonferroni correction (P<2.9x10 -4 ). Ornithine, a non-proteinogenic amino acid, was also negatively associated with mtDNA-CN (p=2

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QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

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Current Protocols in bioinformatics 2020年第1期70卷 e100页

作者： Estaki, Mehrbod Jiang, Lingjing Bokulich, Nicholas A. McDonald, Daniel González, Antonio Kosciolek, Tomasz Martino, Cameron Zhu, Qiyun Birmingham, Amanda Vázquez-Baeza, Yoshiki Dillon, Matthew R. Bolyen, Evan Caporaso, J. Gregory Knight, Rob Department of Pediatrics University of California San Diego La Jolla CA United States Division of Biostatistics University of California San Diego La Jolla CA United States Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ United States Department of Biological Sciences Northern Arizona University Flagstaff AZ United States Małopolska Centre of Biotechnology Jagiellonian University Kraków Poland Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA United States Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://***. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://***. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data. Support Protocol: Further microbiome analyses. © 2020 The Authors.

关键词： bioinformatics metagenomics microbiome QIIME 2 Qiita

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Author Correction: T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control

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Nature immunology 2023年第4期24卷 729页

作者： Maria M Steele Abhinav Jaiswal Ines Delclaux Ian D Dryg Dhaarini Murugan Julia Femel Sunny Son Haley du Bois Cameron Hill Sancy A Leachman Young H Chang Lisa M Coussens Niroshana Anandasabapathy Amanda W Lund Ronald O. Perelman Department of Dermatology NYU Grossman School of Medicine New York University Langone Health New York NY USA. Department of Cell Developmental and Cancer Biology and Knight Cancer Institute Oregon Health and Science University Portland OR USA. Department of Dermatology Microbiology and Immunology Meyer Cancer Center Englander Institute of Precision Medicine Weill Cornell Medicine New York NY USA. Applied Bioinformatics Laboratories NYU Langone Health New York NY USA. Department of Dermatology Oregon Health and Science University Portland OR USA. Department of Biomedical Engineering and Computational Biology Program Oregon Health and Science University Portland OR USA. OHSU Center for Spatial Systems Biomedicine Oregon Health and Science University Portland OR USA. Ronald O. Perelman Department of Dermatology NYU Grossman School of Medicine New York University Langone Health New York NY USA. amanda.lund@***. Department of Cell Developmental and Cancer Biology and Knight Cancer Institute Oregon Health and Science University Portland OR USA. amanda.lund@***. Department of Pathology NYU Grossman School of Medicine New York University Langone Health New York NY USA. amanda.lund@***. Laura and Isaac Perlmutter Cancer Center NYU Grossman School of Medicine New York University Langone Health New York NY USA. amanda.lund@***.

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

Research Square

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Research Square 2021年

作者： Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome. © 2021, CC BY.

关键词： Coronavirus

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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: Widespread exposure to SARS-CoV-2 in wildlife communities

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Nature communications 2024年第1期15卷 6940页

作者： Amanda R Goldberg Kate E Langwig Katherine L Brown Jeffrey M Marano Pallavi Rai Kelsie M King Amanda K Sharp Alessandro Ceci Christopher D Kailing Macy J Kailing Russell Briggs Matthew G Urbano Clinton Roby Anne M Brown James Weger-Lucarelli Carla V Finkielstein Joseph R Hoyt Department of Biological Sciences Virginia Tech Blacksburg VA USA. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. Department of Biomedical Sciences and Pathobiology Virginia Tech Blacksburg VA USA. Translational Biology Medicine and Health Graduate Program Virginia Tech Roanoke VA USA. Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg VA USA. Department of Biochemistry Virginia Tech Blacksburg VA USA. Data Services University Libraries Virginia Tech Blacksburg VA USA. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. Academy of Integrated Science Virginia Tech Blacksburg VA USA. Department of Biological Sciences Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. finkielc@vt.edu. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. finkielc@vt.edu. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Academy of Integrated Science Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Department of Biological Sciences Virginia Tech Blacksburg VA USA. hoytjosephr@***.

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