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Special issue on genome wide association study

Quantitative Biology 2021年第2期9卷 105-106页

作者： Lin Hou Qiongshi Lu Can Yang Hongyu Zhao Center for Statistical Science Department of Industrial EngineeringMOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing 100084China Department of Biostatistics and Medical Informatics Department of StatisticsCenter for Demography of Health and AgingUniversity of Wisconsin-MadisonWl 53726USA Department of Mathematics the Hong Kong University of Science and TechnologyHong KongChina Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Since the first success of genome wide association study(GWAS)was reported in 2005 by Hoh and colleagues on the identification of a major gene for age related macular degeneration,many thousands of published studies have reported reproducible association signals between hundreds of thousands of genetic variants and thousands of traits and diseases in the past 16 *** is a growing number of biobanks with up to one million participants developed across the world to facilitate genetic studies of complex traits through ***,many consortium projects have been initiated to collect diverse types of data that have proved very valuable for integrated analysis of GWAS *** rich data present both great opportunities and challenges to scientists on data management,computation,analysis,integration,and *** special issue of Quantitative Biology collects nine papers that cover major progresses,challenges,and future directions for GWAS analysis.

关键词： hundreds GWAS initiated

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The rapid diagnosis of intraamniotic infection with nanopore sequencing

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American Journal of Obstetrics and Gynecology 2025年

作者： Chaemsaithong, Piya Romero, Roberto Pongchaikul, Pisut Warintaksa, Puntabut Mongkolsuk, Paninee Bhuwapathanapun, Maolee Kotchompoo, Kanyaphat Nimsamer, Pattaraporn Kruasuwan, Worarat Amnuaykiatlert, Orrakanya Vivithanaporn, Pornpun Meyyazhagan, Arun Awonuga, Awoniyi Settacomkul, Rapeewan Singhsnaeh, Arunee Laolerd, Warawut Santanirand, Pitak Thaipisuttikul, Iyarit Wongsurawat, Thidathip Jenjaroenpun, Piroon Department of Obstetrics and Gynecology Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok Thailand Program in Translational Medicine Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok Thailand Pregnancy Research Branch Division of Obstetrics and Maternal-Fetal Medicine Division of Intramural Research Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health United States Department of Health and Human Services Bethesda MD United States Department of Obstetrics and Gynecology University of Michigan Ann Arbor MI United States Department of Epidemiology and Biostatistics Michigan State University East Lansing MI United States Chakri Naruebodindra Medical Institute Faculty of Medicine Ramathibodi Hospital Mahidol University Samut Prakarn Thailand Integrative Computational BioScience Center Mahidol University Nakhon Pathom Thailand Institute of Infection Veterinary and Ecological Sciences University of Liverpool Liverpool United Kingdom Division of Medical Bioinformatics Research Department Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Siriraj Long-Read Lab (Si-LoL) Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Mahidol University International Demonstration School Mahidol University Nakhon Pathom Thailand Department of Obstetrics and Gynecology Wayne State University School of Medicine Detroit MI United States Department of Life Sciences Christ University Bengaluru India Department of Pathology Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok Thailand Department of Microbiology Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand Department of Biomedical Informatics University of Arkansas for Medical Sciences Little Rock AR United States

Background: Intraamniotic infection (defined as intraamniotic inflammation with microorganisms) is an important cause of the preterm labor syndrome. Methods for the detection of microorganisms in amniotic fluid are culture and/or polymerase chain reaction assay. However, both methods take time, and the results are rarely available for clinical decision-making. Nanopore sequencing technology offers real-time, long-read sequencing that can produce rapid results. Objective: To determine 1) the diagnostic performance of the 16S rDNA nanopore sequencing method for the identification of microorganisms in patients with intraamniotic inflammation and 2) the relationship between microbial burden and the intensity of the amniotic fluid inflammatory response. Study Design: We performed a prospective cohort study that included singleton pregnancies presenting with symptoms of preterm labor with intact membranes or of preterm prelabor rupture of the membranes. Amniotic fluid samples were obtained for the evaluation of bacteria in the amniotic cavity using cultivation and polymerase chain reaction-based 16S Sanger sequencing methods. Participants were classified into 4 groups according to the results of an amniotic fluid culture, 16S Sanger sequencing, and an amniotic fluid interleukin 6 concentration: 1) no intraamniotic infection and intraamniotic inflammation (interleukin 6 <2.6 ng/mL, and no microorganisms in the amniotic cavity, as determined by culture or 16S Sanger sequencing);2) microbial invasion of the amniotic cavity without intraamniotic inflammation, defined by the presence of bacteria detected by culture or 16S Sanger sequencing;3) sterile intraamniotic inflammation (interleukin 6 ≥2.6 ng/mL without microbial invasion of the amniotic cavity);and 4) intraamniotic infection (interkeukin 6 ≥2.6 ng/mL with microbial invasion of the amniotic cavity). Patients who underwent a mid-trimester amniocentesis, had no intraamniotic infection or intraamniotic inflammation, and de

关键词： 16S 16S PCR 16S rDNA genes 16S rRNA genes amniocentesis amniotic cavity amniotic fluid bacteria bacterial infection cultivation culture DNA DNA sequencing Escherichia coli (E. coli) infection intraamniotic infection intraamniotic inflammation long-read sequencing microbial invasion of the amniotic cavity (MIAC) microorganism molecular microbiologic technique Mycoplasma species nanopore Oxford nanopore technology (ONT) polymerase chain reaction (PCR) pregnancy preterm preterm labor preterm prelabor rupture of the membranes (PROM) preterm PROM rapid sequencing removal of false positive RNA Sanger sequencing sequencing short-read sterile inflammation third-generation sequencing Ureaplasma species

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Trends and factors associated with initial and recurrent methicillin-resistant Staphylococcus aureus (MRSA) skin and soft-tissue infections among HIV-infected persons: An 18-year study

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Journal of the International Association of Providers of AIDS Care 2014年第3期13卷 206-213页

作者： Vyas, Kartavya J. Shadyab, Aladdin H. Lin, Chii-Dean Crum-Cianflone, Nancy F. Infectious Disease Clinical Research Program Uniformed Services University of the Health Sciences Bethesda MD United States Infectious Disease Clinic Naval Medical Center San Diego San Diego CA United States Department of Bioinformatics and Medical Informatics San Diego State University San Diego CA United States Graduate School of Public Health San Diego State University San Diego CA United States Deployment Health Research Department Naval Health Research Center San Diego CA United States

Background: Factors associated with initial methicillin-resistant Staphylococcus aureus (MRSA) skin and soft-tissue infections (SSTIs) and their recurrence have not been fully elucidated among HIV-infected persons. Methods: We retrospectively evaluated a large cohort of HIV-infected patients from 1993 to 2010 for culture-proven MRSA SSTIs. Separate logistic regression models evaluated factors associated with initial and recurrent infections. Results: Of the 794 patients, 63 (8%) developed an initial infection (19.8 infections/1000 person years [PY]);risk factors included CD4 count <500 cells/mm3 and HIV RNA level ≥400 copies/mL (P < .01), US Centers for Disease Control and Prevention (CDC) stage C versus A/B (P < .01), and injection drug use (IDU, P < .01). In all, 27% developed recurrence (206 infections/1000 PY);risk factors included hospital admission (P = .02). Minocycline for treatment of the initial infection was associated with an 80% decreased odds for recurrence (P = .03). Conclusion: HIV control and avoidance of IDU may be useful in reducing rates of MRSA SSTIs among HIV-infected persons. © The Author(s) 2013.

关键词： epidemiology HIV MRSA recurrence risk factors

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Analysis of cancer omics data in a semantic web framework

Analysis of cancer omics data in a semantic web framework

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Workshop on Semantic Web Applications and Tools for Life Sciences 2010, SWAT4LS 2010

作者： Holford, Matthew E. McCusker, James P. Cheung, Kei-Hoi Krauthammer, Michael Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Department of Pathology Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States

Our work concerns the elucidation of the cancer (epi)genome, transcriptome and proteome to better understand the complex interplay between a cancer cell's molecular state and its response to anti-cancer therapy. To study the problem, we have previously focused on data warehousing technologies and statistical data integration. In this paper, we present recent work on extending our analytical capabilities using Semantic Web technology. A key new component presented here is a SPARQL endpoint to our existing data warehouse. This endpoint allows the merging of observed quantitative data with existing data from semantic knowledge sources such as Gene Ontology (GO). We show how such variegated quantitative and functional data can be integrated and accessed in a universal manner using Semantic Web tools. We also demonstrate how Description Logic (DL) reasoning can be used to infer previously unstated conclusions from existing knowledge bases. As proof of concept, we illustrate the ability of our setup to answer complex queries on resistance of cancer cells to Decitabine, a demethylating agent.

关键词： Semantic Web

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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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An information theoretic framework for genomic data analysis

An information theoretic framework for genomic data analysis

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Aaron McKenna Gil Alterovitz Bioinformatics Program Boston University Boston MA USA Harvard Partners Center for Genetics and Genomics Harvard Medical School Boston MA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Children's Hospital Informatics Program Boston MA

The breadth of biological data collected in the last decade has far outstripped the methods available to process it. To effectively investigate and explore this abundance of data, novel automated collection and analysis approaches must be devised. We have developed a new open software framework, the Open Genomic Analysis Platform (OGAP), to aid in the analysis of genomic data. It is capable of analyzing a variety of data source, and focuses on using information theory to characterize data. The frameworks has is capable of import a variety of genome tied data, and provides custom analysis and visualization of results. We then demonstrate the use of this framework analyzing the Prochlorococcus Marinus organism. We show a strong correlation between the information content of sequence data and up regulation of gene expression during lytic infection.

关键词： Genomics bioinformatics Data analysis Biological information theory Open source software Information analysis Information theory Data visualization Organisms Gene expression

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Forecasting a moving target: Ensemble models for ILI case count predictions 14

Forecasting a moving target: Ensemble models for ILI case co...

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14th SIAM International Conference on Data Mining, SDM 2014

作者： Chakraborty, Prithwish Khadivi, Pejman Lewis, Bryan Mahendiran, Aravindan Chen, Jiangzhuo Butler, Patrick Nsoesie, Elaine O. Mekaru, Sumiko R. Brownstein, John S. Marathe, Madhav V. Ramakrishnan, Naren Dept. of Computer Science Virginia Tech BlacksburgVA United States Discovery Analytics Center Virginia Tech BlacksburgVA United States Network Dynamics and Simulation Science Laboratory Virginia Bioinformatics Institute Virginia Tech BlacksburgVA United States Childrens Hospital Informatics Program Boston Childrens Hospital BostonMA United States Department of Pediatrics Harvard Medical School BostonMA United States

ISBN: (纸本)9781510811515

Modern epidemiological forecasts of common illnesses, such as the flu, rely on both traditional surveillance sources as well as digital surveillance data. However, most published studies have been retrospective. Concurrently, the reports about flu activity generally lags by several weeks and even when published are revised for several weeks more. We posit that effectively handling this uncertainty is one of the key challenges for a real-time prediction system in this sphere. In this paper, we present a detailed prospective analysis on the generation of robust quantitative predictions about temporal trends of flu activity, using several surrogate data sources for 15 Latin American countries. We present our findings about the limitations and possible advantages of correcting the uncertainty associated with official flu estimates. We also compare the prediction accuracy between model-level fusion of different surrogate data sources against data-level fusion. Finally, we present a novel matrix factorization approach using neighborhood embedding to predict flu case counts. Comparing our proposed ensemble method against several baseline methods helps us demarcate the importance of different data sources for the countries under consideration. Copyriqht © SIAM.

关键词： Forecasting

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Insights into the solution structure of the actin-binding tail domain of metavinculin by small angle X-ray scattering and molecular dynamics simulations

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International Journal of Biological Macromolecules 2025年 144376页

作者： Zeynep Aslıhan Durer Hande İpek İnce Zeynep Şevval Düvenci Emel Timuçin Tobias Gräwert Oya Orun Beki Kan Zehra Sayers School of Medicine Department of Biophysics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey Faculty of Pharmacy Department of Biochemistry Acibadem Mehmet Ali Aydinlar University Istanbul Turkey Institute of Health Sciences Department of Biophysics Marmara University Istanbul Turkey Institute of Health Sciences Department of Biostatistics and Bioinformatics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey School of Medicine Department of Biostatistics and Medical Informatics Acibadem Mehmet Ali Aydinlar University Istanbul Turkey European Molecular Biology Laboratory - Hamburg Unit Biological Small Angle Scattering Group Hamburg Germany School of Medicine Department of Biophysics Marmara University Istanbul Turkey Faculty of Engineering and Natural Sciences Molecular Biology Genetics and Bioengineering Program Sabanci University İstanbul Turkey

Vinculin is a ubiquitously expressed focal adhesion protein that plays an important role in cell-matrix and cell-to-cell junctions. Metavinculin, a muscle-specific splice variant of vinculin, contains a 68-amino acid disordered insert region in its actin binding tail domain (MVt). Mutations in this insert are linked to cardiomyopathies. This study investigates the solution structures and structural ensembles of wild-type (WT) and two mutant MVts, ΔLeu954 and R975W, which have been associated with cardiomyopathies, using small-angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations. SAXS analyses revealed subtle differences in the estimated maximum dimensions and corroborated the elongated shape of the MVts. Quantitative comparisons of SAXS profiles indicated similarity between the WT and ΔLeu954, whereas R975W exhibited differences in the small-angle region. MD simulations demonstrated reduced conformational flexibility and greater packing of the insert in WT compared to mutants. Notably, a salt-bridge observed between R975 and D928 in a WT simulation provides a structural basis for the destabilization caused by the R975W mutation. These findings provide insights into the structure and dynamics of WT and mutant MVt, reflecting the promise of SAXS combined with MD simulations to elucidate the structural properties of proteins with structural disorder.

关键词： Metavinculin Molecular dynamics simulations Mutation SAXS Tail domain

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Deciphering sequence and structure features of disease-causing small insertions and deletions

Deciphering sequence and structure features of disease-causi...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Xinjun Zhang Huiying Zhao Yadong Wang Guohua Wang Yaoqi Zhou Yunlong Liu School of Informatics and Computing Indiana UniversityBloomingtonIN47408USA Center for Computational Biology and Bioinformatics Indiana University School of MedicineIndianapolisIN 46202USA Bioinformatics program School of InformaticsIndiana University Purdue University IndianapolisIndianapolisIN 46202USA School of Computer Sciences and Technology Harbin Institute of Technology Department of Medical and Molecular Genetics Indiana University School of MedicineIndianapolisIN 46202USA

Background:Small insertions and deletions(INDELs) compose of the second largest category of genetic variants(next to single nucleotide polymorphism) in the human genome, which accounts for 18%of all the variants *** from the 1,000 Genome Project indicated an estimate of 1 million INDELs per human *** to SNPs(Single Nucleotide Polymorphisms) and large structural variations,INDELs are of significant interest due to their potentials in affecting gene molecular functions and therefore cause *** of its functional importance,lacking a highly accurate bioinformatics tool for INDEL classifications has become a major obstacle in understanding the molecular functions. Methods:In this study,we develop a bioinformatics tool for INDEL *** constructed a training dataset that includes all the disease-causing and neutral INDELs documented in the Human Genome Mutation Database(HGMD) and 1,000 Genome *** the two sets of INDELs,we systematically investigated a series of features on their potentials in disrupting both protein structures and pre-mRNA *** then designed a predictor to best distinguish the disease-causing and neutral INDELs using machine-learning technique. Results:In this study,we focused on the disease relevance of the INDELs in the exonic regions,which include 25,923 and 4,643 disease-causing and neutral INDELs,*** found that the disease-causing INDELs more likely occur in predicted structured regions and neutral INDELs are more likely in unstructured,intrinsically disordered *** also found that INDEL sites matched more to homologous sequences are more like *** RNA processing, we found disease-causing INDELs are twice likely to disrupt binding sites of RNA-binding proteins. We also found that disease-causing INDELs tend to be longer,and be closer to the splice *** addition,the genomic loci for disease-causing INDELs are more evolutionarily conserved,and tend to locat

关键词： insertion deletions INDELs genetic variants next generation sequencing

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A linked data graph approach to integration of immunological data

A linked data graph approach to integration of immunological...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Syed Ahmad Chan Bukhari Jeff Mandell Steven H. Kleinstein Kei-Hoi Cheung Division of Computer Science Mathematics and Science Lesley H. & William L. Collins College of Professional Studies St. John's University Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Pathology Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Emergency Medicine and Yale Center for Medical Informatics Computationa Biology and Bioinformaticcs Yale University

ISBN: (纸本)9781728118680

Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationship-based, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIH/NIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graph-based query language (Cypher) for data retrieval, we develop a web-based dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graph-based database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.

关键词： ontology knowledgebase graph database immunology influenza vaccine graphic library Knowledge Bases Influenza Vaccines Ontology Immunology data integration Passwords DNA Integration integration data types Data sources SQL

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