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arXiv 2023年

作者： Shendure, Jay Ahituv, Nadav Kircher, Martin Agarwal, Vikram Blair, Andrew Chalkiadakis, Theofilos Chardon, Florence M. Dash, Pyaree M. Deng, Chengyu Hamazaki, Nobuhiko Keukeleire, Pia Kubo, Connor Lalanne, Jean-Benoît Maass, Thorben Martin, Beth McDiarmid, Troy Nobuhara, Mai Page, Nicholas F. Regalado, Sam Schubach, Max Sims, Jasmine Ushiki, Aki Zhao, Jingjing Starita, Lea M. Fowler, Douglas M. Best, Sabrina M. Boyle, Gabe Camp, Nathan Casadei, Silvia Da, Estelle Y. Dawood, Moez Dawson, Samantha C. Fayer, Shawn Hamm, Audrey James, Richard G. Jarvik, Gail P. McEwen, Abbye E. Moore, Nick Muffley, Lara A. Pendyala, Sriram Popp, Nicholas A. Post, Mason Rubin, Alan F. Shendure, Jay Smith, Nahum T. Rubin, Alan F. Stone, Jeremy Tejura, Malvika Wang, Ziyu R. Wheelock, Melinda K. Woo, Ivan Zapp, Brendan D. Engreitz, Jesse M. Quertermous, Thomas Amgalan, Dulguun Aradhana, Aradhana Arana, Sophia M. Bassik, Michael C. Bauman, Julia R. Bhattacharya, Asmita Cai, Xiangmeng Shawn Chen, Ziwei Conley, Stephanie Deshpande, Salil Doughty, Benjamin R. Du, Peter P. Gifford, Casey Greenleaf, William J. Gschwind, Andreas R. Guo, Katherine Isobe, Sarasa Jagoda, Evelyn Jain, Nimit Jones, Hank Kang, Helen Y. Kim, Samuel H. Kim, YeEun Klemm, Sandy Kundaje, Anshul Kundu, Soumya Lago-Docampo, Mauro Lee-Yow, Yannick C. Levin-Konigsberg, Roni Li, Daniel Y. Lindenhofer, Dominik Rosa Ma, X. Marinov, Georgi K. Martyn, Gabriella E. Metzl-Raz, Eyal Monteiro, Joao P. Montgomery, Michael T. Mualim, Kristy S. Munger, Chad Munson, Glen Nguyen, Tri C. Nguyen, Trieu Palmisano, Brian T. Pampari, Anusri Park, Chong Y. Rabinovitch, Marlene Ramste, Markus Ray, Judhajeet Roy, Kevin R. Rubio, Oriane M. Schaepe, Julia M. Schnitzler, Gavin Schreiber, Jacob Sharma, Disha Sheth, Maya U. Shi, Huitong Singh, Vasundhara Sinha, Riya Steinmetz, Lars M. Tan, Jason Tan, Anthony Tycko, Josh Valbuena, Raeline C. Pour Amiri, Valeh Valiollah van Kooten, Mariëlle J.F.M. Vaughan-Jackson, Alun Venida, Anthony Weldy, Chad S. Yao, David Zeng, Tony Zhou, Ronghao Vidal, Marc Calderw Department of Genome Sciences University of Washington SeattleWA United States Department of Bioengineering and Therapeutic Sciences Institute for Human Genetics University of California San Francisco San FranciscoCA United States Institute of Human Genetics University Medical Center Schleswig-Holstein University of Lübeck Lübeck23562 Germany Exploratory Diagnostic Sciences Berlin Institute of Health Charité-Universitätsmedizin Berlin Berlin10117 Germany Brotman Baty Institute for Precision Medicine SeattleWA United States Center of immunotherapy and Immunity Seattle Children's Research Institute SeattleWA United States Bioinformatics Division WEHI ParkvilleVIC Australia Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Laboratory Medicine and Pathology University of Washington SeattleWA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Basic Science and Engineering Initiative Stanford Children’s Health Betty Irene Moore Children’s Heart Center StanfordCA United States The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard CambridgeMA United States Division of Cardiovascular Medicine School of Medicine Stanford University United States Department of Computer Science Stanford University School of Medicine StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine StanfordCA United States Division of Pediatric Cardiology Cardiovascular Institute Stanford University School of Medicine Stanford University United States Genome Biology Unit Heidelberg Germany Department of Biology Stanford University StanfordCA United States Stanford Genome Techn

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations. © 2023, CC BY.

关键词： Genome

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Front Cover: Hyperpolarized13C Magnetic Resonance Imaging of Fumarate Metabolism by Parahydrogen-induced Polarization: A Proof-of-Conceptin vivoStudy (ChemPhysChem 10/2021)

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ChemPhysChem 2021年第10期22卷

作者： Dr. Neil J. Stewart Hitomi Nakano Shuto Sugai Mitsushi Tomohiro Yuki Kase Yoshiki Uchio Dr. Toru Yamaguchi Yujirou Matsuo Dr. Tatsuya Naganuma Dr. Norihiko Takeda Dr. Ikuya Nishimura Dr. Hiroshi Hirata Dr. Takuya Hashimoto Dr. Shingo Matsumoto Division of Bioengineering & Bioinformatics Graduate School of Information Science & Technology Hokkaido University North 14 West 9 Kita-ku Sapporo Hokkaido 060-0814 Japan Division of Computational Chemistry Transition State Technology Co. Ltd. 2-16-1 Tokiwadai Ube Yamaguchi 755-8611 Japan R&D Department Japan REDOX Ltd. 4-29-49-805 Chiyo Hakata-ku Fukuoka 812-0044 Japan Division of Cardiology and Metabolism Center for Molecular Medicine Jichi Medical University 3311-1 Yakushiji Shimotsuke-shi Tochigi 329-0498 Japan Chiba Iodine Resource Innovation Center and Department of Chemistry Graduate School of Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan

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A multiple peptides vaccine against COVID-19 designed from the nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the immunoinformatics approach

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Informatics in Medicine Unlocked 2020年 21卷 100476页

作者： Abd Albagi, Sahar Obi Al-Nour, Mosab Yahya Elhag, Mustafa Tageldein Idris Abdelihalim, Asaad Musa Haroun, Esraa Adam Essa, Mohammed Elmujtba Abubaker, Mustafa Deka, Hemchandra Ghosh, Arabinda Hassan, Mohammed A. Department of Microbiology and Immunology AL Neelain University Khartoum Sudan Department of Pharmaceutical Chemistry Faculty of Pharmacy Omdurman Islamic University Khartoum Sudan Faculty of Medicine University of Seychelles-American Institute of Medicine Seychelles Clinical Immunology Council Sudan Medical Specialization Board Sudan Faculty of Medical Pharmacology Ahfad University for Women Sudan Department of Clinical Medical Medical and Cancer Research Institute Nyala Sudan Faculty of Medical Laboratory Sciences Sudan University of Science and Technology Sudan Department of Bioengineering and Technology GUIST Gauhati University Guwahati Assam India Microbiology Division Department of Botany Gauhati University Guwahati Assam India Department of Bioinformatics DETAGEN Genetics Diagnostic Center Kayseri Turkey

Due to the current Coronavirus (COVID-19) pandemic, the rapid discovery of a safe and effective vaccine is an essential issue. Consequently, this study aims to predict a potential COVID-19 peptide-based vaccine utilizing the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics approach. To achieve this goal, several Immune Epitope Database (IEDB) tools, molecular docking, and safety prediction servers were used. According to the results, The Spike peptide SQCVNLTTRTQLPPAYTNSFTRGVY is predicted to have the highest binding affinity to the B-Cells. The Spike peptide FTISVTTEI has the highest binding affinity to the Major Histocompatibility Complex class 1 (MHC I) Human Leukocyte Allele HLA-B*1503 (according to the MDockPeP and HPEPDOCK servers, docking scores were −153.9 and −229.356, respectively). The Nucleocapsid peptides KTFPPTEPK and RWYFYYLGTGPEAGL have the highest binding affinity to the MHC I HLA-A0202 allele and the three the Major Histocompatibility Complex class 2 (MHC II) Human Leukocyte Allele HLA-DPA1*01:03/DPB1*02:01, HLA-DQA1*01:02/DQB1-*06:02, HLA-DRB1, respectively. Docking scores of peptide KTFPPTEPK were −153.9 and −220.876. In contrast, docking scores of peptide RWYFYYLGTGPEAGL were ranged from 218 to 318. Furthermore, those peptides were predicted as non-toxic and non-allergen. Therefore, the combination of those peptides is predicted to stimulate better immunological responses with respectable safety. © 2020

关键词： And immunoinformatics COVID-19 Multiple peptides vaccine Nucleocapsid Spike

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Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions

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Nature Communications 2025年第1期16卷 1-13页

作者： Angela Wei Valerie A. Arboleda Sriram Sankararaman Noah Zaitlen Richard Border Boyang Fu Seon-Kyeong Jang Sinéad Cullina Eimear E. Kenny Nadav Brandes Vasilis Ntranos Miriam S. Udler Interdepartmental Bioinformatics Program UCLA Los Angeles CA USA Department of Pathology and Laboratory Medicine David Geffen School of Medicine UCLA Los Angeles CA USA Department of Computational Medicine David Geffen School of Medicine UCLA Los Angeles CA USA Department of Human Genetics David Geffen School of Medicine UCLA Los Angeles CA USA Department of Computer Science UCLA Los Angeles CA USA Department of Neurology David Geffen School of Medicine UCLA Los Angeles CA USA Institute for Genomic Health Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA Division of Genomic Medicine Department of Medicine Icahn School of Medicine at Mount Sinai New York NY USA Center for Translational Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Epidemiology & Biostatistics UCSF San Francisco CA USA Department of Bioengineering & Therapeutic Sciences (HIVE) UCSF San Francisco CA USA Bakar Computational Health Sciences Institute UCSF San Francisco CA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA The Broad Institute Boston MA USA

Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (marginal epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai BioMeBiobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable carrier penetrance and disease severity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores modify phenotypes amongst pathogenic carriers and that genetic background additionally alters the effects of pathogenic variants through interactions.

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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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The need to implement FAIR principles in biomolecular simulations

arXiv

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein Data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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Accuracy validation of a newly developed cuffless blood pressure estimation method using only an iPhone

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological Engineering 2018年第PROC期56卷 13-13页

作者： Harada, Ippei Yamakoshi, Takehiro Toda, Sogo Matsumura, Kenta Information and Systems Engineering Graduate School of Engineering Fukuoka Institute of Technology Japan Department of Information Systems Engineering Faculty of Information Engineering Fukuoka Institute of Technology Japan Division of Bioengineering and Bioinformatics Graduate School of Information Science and Technology Hokkaido University Japan Computer Science Laboratory Comprehensive Research Organization Fukuoka Institute of Technology Japan

The mainstream method for cuffless blood pressure is that based on pulse transit time. However, there are many inconveniences in this method such as requiring simultaneous measurement of electrocardiogram and photoplethysmogram and frequent calibration using a cuff sphygmomanometer. So, we developed a far simpler method to estimate blood pressure only using a heart rate and modified normalized pulse volume that can be measured using a smartphone alone. In this study, we examined its accuracy against conventional brachial cuff sphygmomanometer. Mental arithmetic task was used to alter blood pressure in 13 healthy subjects. Analysis revealed that the correlation coefficients between these two were.73,.73, and.72 for mean blood pressure, systolic blood pressure, and diastolic blood pressure, respectively. Results suggest that this simple method can yield moderately reliable measures of blood pressure. (132/150). © 2018, Japan Soc. of Med. Electronics and Biol. Engineering. All rights reserved.

关键词： Blood pressure

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Enabling pan-repository reanalysis for big data science of public metabolomics data

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Nature communications 2025年第1期16卷 4838页

作者： Yasin El Abiead Michael Strobel Thomas Payne Eoin Fahy Claire O'Donovan Shankar Subramamiam Juan Antonio Vizcaíno Ozgur Yurekten Victoria Deleray Simone Zuffa Shipei Xing Helena Mannochio-Russo Ipsita Mohanty Haoqi Nina Zhao Andres M Caraballo-Rodriguez Paulo Wender P Gomes Nicole E Avalon Trent R Northen Benjamin P Bowen Katherine B Louie Pieter C Dorrestein Mingxun Wang Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California Riverside Riverside CA USA. European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Wellcome Genome Campus Hinxton Cambridge CB10 1SD UK. Department of Bioengineering and San Diego Supercomputer Center University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0505 USA. Faculty of Chemistry Federal University of Pará Belém PA Brazil. Center for Marine Biotechnology and Biomedicine Scripps Institution of Oceanography University of California San Diego 9500 Gilman Drive La Jolla CA 92093 USA. Department of Pharmaceutical Sciences University of California Irvine California 92697 Irvine USA. Environmental Genomics and Systems Biology Division Lawrence Berkeley National Lab Berkeley CA 94720 USA. The DOE Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Department of Pharmacology University of California San Diego La Jolla CA 92093 USA. Center for Microbiome Innovation University of California San Diego La Jolla CA 92093 USA. Department of Computer Science and Engineering University of California Riverside Riverside CA USA. mingxun.wang@cs.ucr.edu.

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities.

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Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission

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四川生理科学杂志 2022年第6期44卷 1118-1118页

作者： Smruthi Karthikeyan Department of Pediatrics University of California San Diego La Jolla CA USA Department of Biomedical Informatics University of California San Diego La Jolla California USA Department of Computer Science and Engineering University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA USA Expedited COVID Identification Environment (EXCITE) Laboratory Department of Pediatrics University of California San Diego La Jolla CA USA Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA USA COVID-19 Detection Investigation Surveillance Clinical and Outbreak Response California Department of Public Health Richmond CA USA Sanford Consortium of Regenerative Medicine University of California San Diego La Jolla CA USA Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Student Health and Well-Being University of California San Diego La Jolla CA USA Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Operational Strategic Initiatives University of California San Diego La Jolla CA USA Return to Learn University of California San Diego La Jolla CA USA Student Affairs University of California San Diego La Jolla CA USA Academic Affairs University of California San Diego La Jolla CA USA Department of Pathology University of California San Diego La Jolla CA USA Scripps Health San Diego La Jolla CA USA Rady Children’s Institute for Genomic Medicine San Diego CA USA Sharp Healthcare San Diego CA USA Helix San Mateo CA USA County of San Diego Health and Human Services Agency San Diego CA USA Herbert Wertheim School of Public Health and Human Longevity Science Univers

As SARS-CoV-2 continues to spread and evolve,detecting emerging variants early is critical for public health *** lineage prevalence by clinical testing is infeasible at scale,especially in areas with limited resources,participation,or testing/sequencing capacity,which can also introduce ***-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,*** virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants.

关键词： wastewater Wastewater testing

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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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