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Correction to: The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation

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Conservation Genetics 2019年第1期20卷 135-136页

作者： Violeta Muñoz-Fuentes Pilar Cacheiro Terrence F. Meehan Juan Antonio Aguilar-Pimentel Arthur L. Beaudet Steve D. M. Brown Mary E. Dickinson Ann M. Flenniken Paul Flicek Antonella Galli Hamed Haseli Mashhadi Jason D. Heaney Martin Hrabě de Angelis Jong Kyoung Kim K. C. Kent Lloyd Colin McKerlie Hugh Morgan Stephen A. Murray Lauryl M. J. Nutter Patrick T. Reilly John R. Seavitt Je Kyung Seong Michelle Simon Hannah Wardle-Jones Ann-Marie Mallon Damian Smedley Helen E. Parkinson European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Clinical Pharmacology William Harvey Research Institute School of Medicine and Dentistry Queen Mary University of London London UK German Mouse Clinic Institute of Experimental Genetics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany Department of Molecular and Human Genetics Baylor College of Medicine Houston USA Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre) Harwell UK Department of Molecular Physiology and Biophysics Baylor College of Medicine Houston USA The Centre for Phenogenomics Toronto Canada Mount Sinai Hospital Toronto Canada Wellcome Trust Sanger Institute Cambridge UK German Center for Diabetes Research (DZD) Neuherberg Germany School of Life Science Weihenstephan Technische Universität München Freising Germany Department of New Biology DGIST Daegu Republic of Korea Mouse Biology Program University of California Davis USA The Hospital for Sick Children Toronto Canada The Jackson Laboratory Bar Harbor USA PHENOMIN-iCS Illkirch Cedex France Laboratory of Developmental Biology and Genomics College of Veterinary Medicine Interdisciplinary Program for Bioinformatics and Program for Cancer Biology Seoul National University Seoul Republic of Korea

The original publication of this article unfortunately contained the following mistakes.

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Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

arXiv

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arXiv 2020年

作者： Law, Jeffrey N. Akers, Kyle Tasnina, Nure Della Santina, Catherine M. Deutsch, Shay Kshirsagar, Meghana Klein-Seetharaman, Judith Crovella, Mark Rajagopalan, Padmavathy Kasif, Simon Murali, T.M. Interdisciplinary Ph.D. Program in Genetics Bioinformatics and Computational Biology BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Department of Biomedical Engineering Boston University BostonMA United States Department of Mathematics University of California Los AngelesCA United States AI for Good Lab Microsoft RedmondWA United States Department of Chemistry Colorado School of Mines GoldenCO United States Department of Computer Science Boston University BostonMA United States Department of Chemical Engineering Virginia Tech BlacksburgVA United States

Background: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. Results: We design a network propagation framework with two novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. Conclusions: We examine how our provenance tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly-emerging viruses. © 2020, CC BY-NC-SA.

关键词： SARS

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Melanoma Detection by AFM Indentation of Histological Specimens

Research Square

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Research Square 2021年

作者： Jeon, ByoungJun Jung, Hyo Gi Lee, Sang Won Lee, Gyudo Shim, Jung Hee Kim, Mi Ok Kim, Byung Jun Kim, Sang-Hyon Lee, Hyungbeen Lee, Sang Woo Yoon, Dae Sung Jo, Seong Jin Choi, Tae Hyun Lee, Wonseok Interdisciplinary Program for Bioengineering Seoul National University Seoul08826 Korea Republic of School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Department of Plastic and Reconstructive Surgery Research Services Seoul National University Bundang Hospital Seongnam13620 Korea Republic of Department of Plastic and Reconstructive Surgery Institute of Human Environment Interface Biology Seoul National University College of Medicine Seoul03087 Korea Republic of Department of Internal Medicine Keimyung University Dongsan Medical Center Daegu41931 Korea Republic of Department of Biomedical Engineering Yonsei University Wonju26493 Korea Republic of R&D Center of Curigin Ltd. Seoul04778 Korea Republic of Department of Dermatology Seoul National University College of Medicine Seoul03087 Korea Republic of Department of Electrical Engineering Korea National University of Transportation Chungju27469 Korea Republic of

Melanoma is visible unlike other types of cancer, but it is still challenging to diagnose correctly because of the difficulty in distinguishing between benign nevus and melanoma. We conducted a robust investigation of melanoma, identifying considerable differences in local elastic properties between nevus and melanoma tissues by using atomic force microscopy (AFM) indentation of histological specimens. Specifically, the histograms of the elasticity of melanoma displayed multimodal Gaussian distributions, exhibiting the heterogeneous mechanical properties, in contrast with the unimodal distributions of elasticity in the benign nevus. We identified this notable signature was consistent regardless of blotch incidence by sex, age, anatomical site (e.g., thigh, calf, arm, eyelid, and cheek), or cancer stage (I, IV, and V). In addition, we found that the non-linearity of the force-distance curves for melanoma is increased compared to benign nevus. We believe that AFM indentation of histological specimens may technically complement conventional histopathological analysis for earlier and more precise melanoma detection. © 2021, CC BY.

关键词： Elasticity

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Erratum to: UBCG2: Up-to-Date Bacterial Core Genes and Pipeline for Phylogenomic Analysis

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Journal of microbiology (Seoul, Korea) 2023年第10期61卷 937页

作者： Jihyeon Kim Seong-In Na Dongwook Kim Jongsik Chun Interdisciplinary Program in Bioinformatics Seoul National University Seoul 00826 Republic of Korea. Institute of Molecular Biology & Genetics Seoul National University Seoul 00826 Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul 00826 Republic of Korea. jchun@snu.ac.kr. Institute of Molecular Biology & Genetics Seoul National University Seoul 00826 Republic of Korea. jchun@snu.ac.kr. School of Biological Sciences Seoul National University Seoul 00826 Republic of Korea. jchun@snu.ac.kr.

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Differentially expressed genes in adipose tissues reveals that ECM-receptor interaction affects on the depot-specific adipogenesis in Hanwoo

Differentially expressed genes in adipose tissues reveals th...

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第11届世界畜产大会(WCAP2013)

作者： Mi Jang Hyun-Jeong Lee Hyeongmin Kim Woori Kwak WonCheoul Park Jae-Yeon Hwang Chang-Kyu Lee Gul Won Jang Mi Na Park Hyeong-Cheol Kim Jin Young Jeong Kang Seok Seo Heebal Kim Seoae Cho Bo-Young Lee C&K Genomics Seoul National University NakSeoungDaeGwanakguSeoulKorea Department of Agricultural Biotechnology and Research Institute for Agriculture and Life ScienceSeoul National UniversitySeoulKorea Department of Animal Science and TechnologyCollege of Life Science and Natural ResourcesSunchon National UniversitySeoulRepublic of Korea C&K GenomicsSeoul National University NakSeoungDaeGwanakguSeoulKorea C&K Genomics Seoul National University NakSeoungDaeGwanakguSeoulKorea Department of Agricultural Biotechnology and Research Institute of Population GenomicsSeoul National UniversitySeoulRepublic of Korea Department of Animal Science and Technology College of Life Science and Natural ResourcesSunchon National UniversitySeoulRepublic of Korea Division of Animal Genomics and BioinformaticsNational Institute of Animal scienceRural Development AdministrationSuwonRepublic of Korea Department of Agricultural Biotechnology and Research Institute of Population GenomicsSeoul National UniversitySeoulRepublic of Korea Interdisciplinary Program in BioinformaticsSeoul National UniversityKorea Division of Animal Genomics and Bioinformatics National Institute of Animal scienceRural Development AdministrationSuwonRepublic of Korea

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Comparative analysis of different approaches for dealing with candidate regions in the context of a genome-wide association study

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BMC proceedings 2009年第7-Sup期3 Suppl 7卷 S93页

作者： Francesca Lantieri Min A Jhun Jungsun Park Taesung Park Marcella Devoto The Children's Hospital of Philadelphia 3615 Civic Center Boulevard Philadelphia Pennsylvania 19104 USA. Dipartimento di Scienze della Salute sezione di Biostatistica Universita' degli Studi di Genova via Pastore 1 16132 Genoa Italy. Interdisciplinary Program in Bioinformatics Seoul National University Seoul 56-1 Shillim-Dong Kwanak-Gu Seoul 151-742 Korea. Department of Statistics Seoul National University Seoul 56-1 Shillim-Dong Kwanak-Gu 151-747 Korea. Department of Pediatrics and CCEB University of Pennsylvania 3615 Civic Center Boulevard Philadelphia Pennsylvania 19104 USA. Dipartimento di Medicina Sperimentale Universita' La Sapienza Viale Regina Elena 324 00161 Roma Italy.

Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphisms (SNPs) for association to a trait, treating each marker equally and ignoring prior evidence of association to specific regions. Typically, promising regions are selected for further investigation based on p-values obtained from simple tests of association. However, loci that exert only a weak, low-penetrant role on the trait, producing modest evidence of association, are not detectable in the context of a GWAS. Implementing prior knowledge of association in GWAS could increase power, help distinguish between false and true positives, and identify better sets of SNPs for follow-up *** we performed a GWAS on rheumatoid arthritis (RA) patients and controls (Problem 1, Genetic Analysis Workshop 16). In order to include prior information in the analysis, we applied four methods that distinctively deal with markers in candidate genes in the context of GWAS. SNPs were divided into a random and a candidate subset, then we applied empirical correction by permutation, false-discovery rate, false-positive report probability, and posterior odds of association using different prior probabilities. We repeated the same analyses on two different sets of candidate markers defined on the basis of previously reported association to RA following two different approaches. The four methods showed similar relative behavior when applied to the two sets, with the proportion of candidate SNPs ranked among the top 2,000 varying from 0 to 100%. The use of different prior probabilities changed the stringency of the methods, but not their relative performance.

关键词： Rheumatoid Arthritis Prior Probability Candidate Marker Candidate SNPs Genetic Analysis Workshop

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

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细胞研究（英文版） 2020年第7期30卷 626-629页

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

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

关键词： Stem-cell differentiation Stem cells

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Erratum to: Identifying candidate positive selection genes in Korean imported pig breeds

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Genes & Genomics 2017年第6期39卷 687-687页

作者： Wonseok Lee Mengistie Taye Taehyung Kwon Joon Yoon Dongsung Jang Shunsuke Suzuki Heebal Kim Department of Agricultural Biotechnology Animal Biotechnology and Research Institute of Agriculture and Life Sciences Seoul National University Seoul Republic of Korea Bahir Dar University College of Agriculture and Environmental Sciences Bahir Dar Ethiopia Department of Natural Science Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea Interdisciplinary Program in Agricultural Genomics Seoul National University Seoul Republic of Korea Epigenomics Division Frontier Agriscience and Technology Center Faculty of Agriculture Shinshu University Kami‑Ina Japan Department of Interdisciplinary Genome Sciences and Cell Metabolism Institute for Biomedical Sciences Interdisciplinary Cluster for Cutting Edge Research Shinshu University Kami‑Ina Japan Institute for Biomedical Sciences Shinshu University Kami‑Ina Japan

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Erratum to: Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer

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BMC genomics 2017年第1期18卷 88页

作者： Min-Seok Kwon Yongkang Kim Seungyeoun Lee Junghyun Namkung Taegyun Yun Sung Gon Yi Sangjo Han Meejoo Kang Sun Whe Kim Jin-Young Jang Taesung Park Interdisciplinary program in Bioinformatics Seoul National University Seoul Korea. Department of Statistics Seoul National University Seoul Korea. Department of Mathematics and Statistics Sejong University Seoul Korea. Immunodiagnostics R&D Team IVD Business Unit New Business Division SK telecom Co Seongnam Korea. Department of Surgery Seoul National University Hospital Seoul Korea. Department of Surgery Seoul National University Hospital Seoul Korea. jangjy4@***. Interdisciplinary program in Bioinformatics Seoul National University Seoul Korea. tspark@stats.snu.ac.kr. Department of Statistics Seoul National University Seoul Korea. tspark@stats.snu.ac.kr.

A correction to the article 'Integrative Analysis of Multiomics Data for Identifying Multi-Markers for Diagnosing Pancreatic Cancer,' published in a previous issue is prpresented

关键词： PANCREATIC cancer TUMOR markers

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