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Expression of concern: Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy

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Nanoscale 2024年第9期16卷 4919页

作者： Michael A Casasanta G M Jonaid Liam Kaylor William Y Luqiu Maria J Solares Mariah L Schroen William J Dearnaley Jarad Wilson Madeline J Dukes Deborah F Kelly Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA. Debkelly@psu.edu. Materials Research Institute Pennsylvania State University University Park PA 16802 USA. Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Department of Electrical and Computer Engineering Duke University Durham NC 27708 USA. RayBiotech Life Peachtree Corners GA 30092 USA. Applications Science Protochips Inc. Morrisville NC 27560 USA.

Expression of concern for 'Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy' by Michael A. Casasanta , , 2021, , 7285-7293, https://***/10.1039/D1NR00388G.

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Characterization of primary Sjögren's syndrome in the Taiwan Han population through a genome-wide association study and polygenic risk score analysis

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Clinical Immunology 2024年 269卷 110381-110381页

作者： Liu, Ting-Yuan Lin, Min-Rou Lu, Hsing-Fang Chen, Yu-Chia Lin, Hui-Ju Chou, Wan-Hsuan Wei, Chun-Yu Palupi, Poppy Diah Liao, Chi-Chou Chang, Yen-Ting Chang, Wei-Chiao Tsai, Fuu-Jen Department of Medical Research China Medical University Hospital China Medical University Taichung 40447 Taiwan Department of Clinical Pharmacy School of Pharmacy Taipei Medical University Taipei 110 Taiwan Department of Ophthalmology Eye Center China Medical University Hospital Taichung 40447 Taiwan Master Program in Clinical Genomics and Proteomics School of Pharmacy Taipei Medical University Taipei 110 Taiwan Core Laboratory of Neoantigen Analysis for Personalized Cancer Vaccine Office of R&D Taipei Medical University Taipei 110 Taiwan Integrative Research Center in Critical Care Wanfang Hospital Taipei Medical University Taipei 116 Taiwan Department of Pharmacy Wan Fang Hospital Taipei Medical University Taipei 110 Taiwan Department of Pharmacology National Defense Medical Center Taipei 110 Taiwan Department of Medical Research China Medical University Hospital Taichung 40402 Taiwan School of Chinese Medicine China Medical University Taichung 40402 Taiwan Division of Pediatric Medical Genetics Children's Hospital of China Medical University Taichung 40447 Taiwan Department of Biotechnology and Bioinformatics Asia University Taichung 41354 Taiwan

Background: Sjögren's syndrome (SS) is an autoimmune disorder that primarily affects the exocrine glands, leading to dryness of mucous membranes and systemic manifestations. This study aimed to identify genetic markers associated with primary SS (pSS) in the Taiwan Han population through a hospital-based genome-wide association study (GWAS) and polygenic risk score (PRS) analysis, addressing the lack of genetic research. Results: This study included 11,390 patients diagnosed with pSS and 113,900 controls. GWAS identified one known locus and eight novel loci. Known HLA alleles, including HLA-DRB1*15:01 and HLA-DQA1*03:01, were successfully replicated in a consistent effect direction. PRS analysis revealed that several autoimmune diseases share similar genetic backgrounds with pSS, including rheumatoid arthritis and systemic lupus erythematosus. Conclusion: This study represents the largest cohort to date on the genetics of pSS in the Taiwan Han population. Our findings provide valuable insights into the pathogenesis of pSS and emphasize the comorbidities associated with it as an autoimmune disease. © 2024

关键词： Gene polymorphism Genome-wide association study (GWAS) Human leukocyte antigen (HLA) Phenome-wide association study (PheWAS) Polygenic risk score (PRS) Sjögren's syndrome

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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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Author Correction: Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

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Nature genetics 2024年第6期56卷 1319页

作者： Sneha Mitra Rohan Malik Wilfred Wong Afsana Rahman Alexander J Hartemink Yuri Pritykin Kushal K Dey Christina S Leslie Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. Rye Country Day School Rye NY USA. Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA. Hunter College City University of New York New York City NY USA. Department of Computer Science Duke University Durham NC USA. Program in Computational Biology and Bioinformatics Duke University Durham NC USA. Center for Genomic and Computational Biology Duke University Durham NC USA. Department of Computer Science Princeton University Princeton NJ USA. Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. deyk@***. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA. lesliec@***.

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Distinguishing genetic correlation from causation across 52 diseases and complex traits (vol 50, pg 1728, 2018)

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NATURE GENETICS 2018年第12期50卷 1753-1753页

作者： O'Connor, Luke J. Price, Alkes L. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Graduate School of Arts and Sciences Cambridge MA USA Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA USA Program in Medical and Population Genetics Broad Institute Cambridge MA USA

Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments \({\it{E}}({\it{\alpha }}_1^2{\it{\alpha }}_1{\it{\alpha }}_2)\) and \({\it{E}}\left( {{\it{\alpha }}_2^2{\it{\alpha }}_1{\it{\alpha }}_2} \right)\) of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large \({\it{\alpha }}_1^2\)) will have correlated effects on trait 2 (large α₁α₂), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.

关键词： Genetics Genome-wide association studies

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Author Correction: A B-ARR-mediated cytokinin transcriptional network directs hormone cross-regulation and shoot development

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Nature communications 2023年第1期14卷 5988页

作者： Mingtang Xie Hongyu Chen Ling Huang Ryan C O'Neil Maxim N Shokhirev Joseph R Ecker Plant Biology Laboratory and Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA 92037 USA. Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA 92037 USA. Department of Computer Science Dartmouth College Hanover NH 03755 USA. The Razavi Newman Integrative Genomics and Bioinformatics Core Facility The Salk Institute for Biological Studies La Jolla CA 92037 USA. Bioinformatics Program University of California at San Diego La Jolla CA 92093 USA. Plant Biology Laboratory and Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA 92037 USA. ecker@salk.edu. Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA 92037 USA. ecker@salk.edu.

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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: Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data

Nature computational science

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Nature computational science 2023年第9期3卷 805页

作者： Xi Chen Yuan Wang Antonio Cappuccio Wan-Sze Cheng Frederique Ruf Zamojski Venugopalan D Nair Clare M Miller Aliza B Rubenstein German Nudelman Alicja Tadych Chandra L Theesfeld Alexandria Vornholt Mary-Catherine George Felicia Ruffin Michael Dagher Daniel G Chawla Alessandra Soares-Schanoski Rachel R Spurbeck Lishomwa C Ndhlovu Robert Sebra Steven H Kleinstein Andrew G Letizia Irene Ramos Vance G Fowler Christopher W Woods Elena Zaslavsky Olga G Troyanskaya Stuart C Sealfon Center for Computational Biology Flatiron Institute New York NY USA. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. Department of Computer Science Princeton University Princeton NJ USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. Division of Infectious Diseases Department of Medicine Duke University School of Medicine Durham NC USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT USA. Battelle Memorial Institute Columbus OH USA. Division of Infectious Diseases Department of Medicine Weill Cornell Medicine New York NY USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. Department of Pathology and Department of Immunobiology Yale School of Medicine New Haven CT USA. Naval Medical Research Center Silver Spring MD USA. Precision Immunology Institute Icahn School of Medicine at Mount Sinai New York NY USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. elena.zaslavsky@mssm.edu. Center for Computational Biology Flatiron Institute New York NY USA. ogt@genomics.princeton.edu. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Computer Science Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. stuart.sealfon@mssm.edu.

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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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GREGoR: Accelerating genomics for Rare Diseases

arXiv

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arXiv 2024年

作者： Dawood, Moez Heavner, Ben Wheeler, Marsha M. Ungar, Rachel A. LoTempio, Jonathan Wiel, Laurens Berger, Seth Bernstein, Jonathan A. Chong, Jessica X. Délot, Emmanuèle C. Eichler, Evan E. Gibbs, Richard A. Lupski, James R. Shojaie, Ali Talkowski, Michael E. Wagner, Alex H. Wei, Chia-Lin Wellington, Christopher Wheeler, Matthew T. Carvalho, Claudia M.B. Gifford, Casey A. May, Susanne Miller, Danny E. Rehm, Heidi L. Sedlazeck, Fritz J. Vilain, Eric O'Donnell-Luria, Anne Posey, Jennifer E. Chadwick, Lisa H. Bamshad, Michael J. Montgomery, Stephen B. Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX United States Medical Scientist Training Program Baylor College of Medicine HoustonTX United States Department of Biostatistics University of Washington SeattleWA United States Department of Genetics School of Medicine Stanford University StanfordCA United States Department of Pathology School of Medicine Stanford University Stanford CA United States Stanford Center for Biomedical Ethics School of Medicine Stanford University Stanford CA United States Institute for Clinical and Translational Science University of California IrvineCA United States Division of Cardiovascular Medicine School of Medicine Stanford University Stanford CA United States Division of Genetics and Metabolism Children’s National Rare Disease Institute WashingtonDC United States Center for Genetic Medicine Research Children’s National Rare Disease Institute WashingtonDC United States Department of Genomics and Precision Medicine George Washington University WashingtonDC United States Department of Pediatrics School of Medicine Stanford University Stanford CA United States Department of Pediatrics Dvision of Genetic Medicine University of Washington SeattleWA United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Howard Hughes Medical Institute University of Washington SeattleWA United States Department of Pediatrics Baylor College of Medicine HoustonTX United States Center for Genomic Medicine Massachusetts General Hospital BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard BostonMA United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States Stanley Center for

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://***/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics. © 2024, CC BY.

关键词： Genome

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