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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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Digital is the New Blood: Enabling the Present and the Future

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Alzheimer's & Dementia 2023年第S21期19卷

作者： Rhoda Au Zachary T Popp Spencer Low Phillip H Hwang Ileana De Anda-Duran Stephanie Li Salman Rahman Huitong Ding Akwaugo Igwe Cody Karjadi Ting Fang Alvin Ang Sherral A. Devine Ashita S Gurnani Jesse B. Mez Lindsay A. Farrer Preeti Sunderaraman Honghuang Lin Vijaya B. Kolachalama Boston University School of Public Health Boston MA USA Framingham Heart Study Boston University School of Medicine Boston MA USA Davos Alzheimer’s Collaborative Philadelphia PA USA Department of Epidemiology Boston University School of Public Health Boston MA USA Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University School of Public Heatlh Boston MA USA Tulane School of Public Health and Tropical Medicine New Orleans LA USA Slone Epidemiology Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framigham Heart Study - Boston University Framingham MA USA Boston University Bioinformatics Program Boston MA USA Departments of Medicine (Biomedical Genetics) Neurology Ophthalmology Biostatistics and Epidemiology Boston University Schools of Medicine and Public Health Boston MA USA Department of Neurology - Boston University Chobanian & Avedisian School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Framingham Heart Study/Boston University BOSTON MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)-related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet-connected applications and devices that allow real-time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum. Method Beginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research center (BU ADRC) leverages digital sensors in multiple internet-connected devices that longitudinally collect digital data, in both in-person and remote settings. Result The first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020-current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants. Conclusion Opportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologi

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Systemic analysis of osteoblast-specific DNA methylation marks reveals novel epigenetic basis of osteoblast differentiation

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Bone Reports 2017年 6卷 109-119页

作者： Yu, Fangtang Shen, Hui Deng, Hong-Wen Center for Bioinformatics and Genomics Department of Global Biostatistics and Data Science School of Public Health and Tropical Medicine Tulane University New Orleans 70112 LA United States College of Life Sciences Hunan Normal University Changsha 410081 Hunan China College of Life Sciences and Bioengineering Beijing Jiaotong University Beijing 100044 China

DNA methylation is an important epigenetic modification that contributes to the lineage commitment and specific functions of different cell types. In this study, we compared ENCODE-generated genome-wide DNA methylation profiles of human osteoblast with 21 other types of human cells in order to identify osteoblast-specific methylation events. For most of the cell strains, data from two isogenic replicates were included, resulting in a total of 51 DNA methylation datasets. We identified 852 significant osteoblast-specific differentially methylated CpGs (DMCs) and 295 significant differentially methylated regions (DMRs). Significant DMCs/DMRs were not enriched in CpG islands (CGIs) and promoters, but more strongly enriched in CGI shores/shelves and in gene body and intergenic regions. The genes associated with significant DMRs were highly enriched in biological processes related to transcriptional regulation and critical for regulating bone metabolism and skeletal development under physiologic and pathologic conditions. By integrating the DMR data with the extensive gene expression and chromatin epigenomics data, we observed complex, context-dependent relationships between DNA methylation, chromatin states, and gene expression, suggesting diverse DNA methylation-mediated regulatory mechanisms. Our results also highlighted a number of novel osteoblast-relevant genes. For example, the integrated evidences from DMR analysis, histone modification and RNA-seq data strongly support that there is a novel isoform of neurexin-2 (NRXN2) gene specifically expressed in osteoblast. NRXN2 was known to function as a cell adhesion molecule in the vertebrate nervous system, but its functional role in bone is completely unknown and thus worth further investigation. In summary, we reported a comprehensive analysis of osteoblast-specific DNA methylation profiles and revealed novel insights into the epigenetic basis of osteoblast differentiation and activity. © 2017 The Authors

关键词： Alternative splicing Cell-specific Differential methylation analysis Methylation Osteoblast Transcription

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Topological data analysis can extract subgroups with high incidence rates of Type 2 diabetes

Topological data analysis can extract subgroups with high in...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Hyung Sun Kim Changwoo Yi Yongkang Kim Uhnmee Park Woong Kook Hyuk Kim Taesung Park Interdisciplinary Program in bioinformatics Seoul National University The Research Institute of Basic Sciences College of Natural Sciences Seoul National University Department of Statistics Seoul National University Department of Bioscience and biotechnology University of Suwon Department of Mathematical Sciences and Institute for Mathematical Data Analytics Research Center Seoul National University Department of Mathematical Sciences Seoul National University

ISBN: (纸本)9781538654897;9781538654880

Type 2 diabetes (T2D) is now a rapidly increasing, worldwide scourge, and the identification of genetic contributors is vital. However, current analyses of multiple, disease-contributing factors, and their combined interactions, remains quite difficult, using traditional approaches. Topological data analysis (TDA) shows what shape a data set can have, facilitating clustering analysis, by determining which components are close to each other. Thus, TDA can generate a network, using single-nucleotide polymorphism (SNP) data, revealing the genetic relatedness of specific individuals, and can derive multiple ordered subgroups, from one with a low patient concentration, to one with a high patient concentration. Since it is widely accepted that T2D pathogenesis is affected by multiple genetic factors, we performed TDA on T2D data from the Korea Association REsource (KARE) project, a population-based, genome-wide association study of the Korean adult population. Since KARE data contains follow-up information about the incidence of T2D, we compared the T2D status of each individual, at baseline, with that of 10 years later. For the TDA network-driven subgroups, ordered by prevalence, we compared the T2D incidence rate, after 10 years, for individuals initially without T2D. As a result, we found that the TDA network-driven, ordered subgroups had significantly increased incidence rates, linearly correlated with prevalence (p-value = 0.006914). Our results demonstrate the usefulness of TDA in both identifying genetic contributors (e.g., SNPs), and their interrelationships, in the pathology of complex diseases.

关键词： data analysis Genetics bioinformatics Diabetes Diseases Conferences data mining

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm’s Arthropod Community Modelling

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Procedia Computer Science 2018年 135卷 377-384页

作者： Rezzy Eko Caraka Shamarina Shohaimi Isma Dwi Kurniawan Riki Herliansyah Arif Budiarto Shinta Purnama Sari Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Department of Mathematics Kalimantan Institute of Technology (ITK) Kalimantan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave.

关键词： Ecology GLLVM Negative Binomial Show cave Wild Cave

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Identification of targetable vulnerabilities of PLK1-overexpressing cancers by synthetic dosage lethality

Cell Genomics

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Cell Genomics 2025年第6期5卷 100876页

作者： Cunningham, Chelsea E. Vizeacoumar, Frederick S. Zhang, Yue Kyrylenko, Liliia Both, Simon Maranda, Vincent Dong, He Price, Jared D.W. Gao, Peng Wagner, Konrad Wu, Yingwen Lazell-Wright, Mary Ganapathysamy, Ashtalakshmi Hari, Rithik Bhanumathy, Kalpana K. Denomy, Connor Saxena, Anjali Vizeacoumar, Jeff P. Morales, Alain Morejon Khan, Faizaan Mosley, Shayla Chen, Angie Katrii, Tetiana Zoller, Ben G.E. Rajamanickam, Karthic Walke, Prachi Gong, Lihui Patel, Hardikkumar Elhasasna, Hussain Dahiya, Renuka Abuhussein, Omar Dmitriev, Anton Freywald, Tanya Munhoz, Erika Prando Ruppin, Eytan Lee, Joo Sang Rox, Katharina Koebel, Martin Hopkins, Laura Lee, Cheng Han Yadav, Sunil Gasparoni, Gilles Walter, Jörn Krishnan, Anand Datla, Raju Toosi, Behzad Baker, Kristi Meens, Jalna Cescon, David W. Ailles, Laurie Leary, Scot C. Wu, Yuliang Empting, Martin Kiemer, Alexandra K. Freywald, Andrew Vizeacoumar, Franco J. Department of Oncology College of Medicine University of Saskatchewan Saskatoon S7N 5E5 SK Canada Department of Pathology and Laboratory Medicine University of Saskatchewan Saskatoon S7N 5E5 SK Canada Department of Pharmacy Pharmaceutical Biology Saarland University PharmaScienceHub Saarbrücken 66123 Germany Global Institute for Food Security University of Saskatchewan Saskatoon S7N 4L8 SK Canada Agriculture and Agri-Food Canada Saskatoon Research and Development Centre 107 Science Place Saskatoon S7N 0X2 SK Canada Antiviral & Antivirulence Drugs (AVID) Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) Helmholtz Centre for Infection Research (HZI) and Department of Pharmacy Saarland University Saarbrücken 66123 Germany Department of Oncology University of Alberta Edmonton T6G 1Z2 AB Canada Department of Anatomy Physiology and Pharmacology University of Saskatchewan and Cameco MS Neuroscience Research Centre 701 Queen St. Saskatoon S7K 0M7 SK Canada Cancer Data Science Laboratory National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Center for Bioinformatics and Computational Biology and Department of Computer Sciences University of Maryland College Park 20742 MD United States Department of Precision Medicine School of Medicine and Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 South Korea Department of Chemical Biology (CBIO) Helmholtz Center for Infection Research (HZI) Inhoffenstrasse 7 Braunschweig 38124 Germany Department of Pathology University of Calgary Calgary AB Canada Department of Laboratory Medicine and Pathology University of Alberta Edmonton AB Canada Department of Genetics Saarland University Saarbrücken Germany Western College of Veterinary Medicine University of Saskatchewan Room 2343 52 Campus Drive Saskatoon S7N 5B4 Canada Princess Margaret Cancer Centre University Health Network Toronto ON Canada Department of Biochemistry Microb

Chromosomal instability (CIN) drives tumor heterogeneity, complicating cancer therapy. Although Polo-like kinase 1 (PLK1) overexpression induces CIN, direct inhibition of PLK1 has shown limited clinical benefits. We therefore performed a genome-wide synthetic dosage lethality (SDL) screen to identify effective alternative targets and validated over 100 candidates using in vivo and in vitro secondary CRISPR screens. We employed direct-capture Perturb-seq to assess the transcriptional consequences and viability of each SDL perturbation at a single-cell resolution. This revealed IGF2BP2 as a critical genetic dependency that, when targeted, downregulated PLK1 and significantly restricted tumor growth. Mechanistic analyses showed that IGF2BP2 loss disrupted cellular energy metabolism and mitochondrial ATP production by downregulating PLK1 levels as well as genes associated with oxidative phosphorylation. Consistent with this, pharmacological inhibition of IGF2BP2 severely impacts the viability of PLK1-overexpressing cancer cells addicted to higher metabolic rates. Our work offers a novel therapeutic strategy against PLK1-driven heterogeneous malignancies. © 2025 The Author(s)

关键词： chromosomal instability IGF2BP2 IMP2 in vivo CRISPR screen perturb-seq PLK1 single-cell CRISPR screening synthetic dosage lethality tumor heterogeneity

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The mutational constraint spectrum quantified from variation in 141,456 humans (vol 581, pg 434, 2020)

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NATURE 2021年第7846期590卷 E53-E53页

作者： Karczewski, Konrad J. Francioli, Laurent C. Tiao, Grace Cummings, Beryl B. Alfoldi, Jessica Wang, Qingbo Collins, Ryan L. Laricchia, Kristen M. Ganna, Andrea Birnbaum, Daniel P. Gauthier, Laura D. Brand, Harrison Solomonson, Matthew Watts, Nicholas A. Rhodes, Daniel Singer-Berk, Moriel England, Eleina M. Seaby, Eleanor G. Kosmicki, Jack A. Walters, Raymond K. Tashman, Katherine Farjoun, Yossi Banks, Eric Poterba, Timothy Wang, Arcturus Seed, Cotton Whiffin, Nicola Chong, Jessica X. Samocha, Kaitlin E. Pierce-Hoffman, Emma Zappala, Zachary O'Donnell-Luria, Anne H. Minikel, Eric Vallabh Weisburd, Ben Lek, Monkol Ware, James S. Vittal, Christopher Armean, Irina M. Bergelson, Louis Cibulskis, Kristian Connolly, Kristen M. Covarrubias, Miguel Donnelly, Stacey Ferriera, Steven Gabriel, Stacey Gentry, Jeff Gupta, Namrata Jeandet, Thibault Kaplan, Diane Llanwarne, Christopher Munshi, Ruchi Novod, Sam Petrillo, Nikelle Roazen, David Ruano-Rubio, Valentin Saltzman, Andrea Schleicher, Molly Soto, Jose Tibbetts, Kathleen Tolonen, Charlotte Wade, Gordon Talkowski, Michael E. Aggregation, Genome Neale, Benjamin M. Daly, Mark J. MacArthur, Daniel G. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine Finland Helsinki Finland Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Vertex Pharmaceuticals Inc Boston MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA Department of Neurology Harvard Medical School Boston MA USA Cardiac Arrhythmia Service and Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA USA Diabetes Unit Massachusetts General Hospital Boston MA USA Program in Metabolism Broad Institute of MIT and Harvard Cambridge MA USA Department of Medicine Harvard Medical School Boston MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Department of Public Health Faculty of Medicine University of Helsinki Helsinki Finland Cardiovascular Disease Initiative and Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Genomics Diabetes and Endocrinology Lund University Lund Sweden Folkhälsan Institute of Genetics Folkhälsan Research C

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Hybrid support vector regression in electric load during national holiday season

Hybrid support vector regression in electric load during nat...

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2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017

作者： Caraka, Rezzy Eko Bakar, Sakhinah Abu Pardamean, Bens Budiarto, Arif School of Mathematical Sciences Faculty of Science and Technology National University of Malaysia Malaysia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538640456

This paper studies non-parametric time-series approach to electric load in national holiday seasons based on historical hourly data in state electric company of Indonesia consisting of historical data of the Northern Sumatera also South and Central Sumatra electricity load. Given a baseline for forecasting performance, we apply our hybrid models and computation platform with combining parameter of the kernel. To facilitate comparison to results of our analysis, we highlighted the results around MAPE-based and R2-based techniques. In order to get more accurate results, we need to improve, investigate, also develop the appropriate statistical tools. Electric load forecasting is a fundamental aspect of infrastructure development decisions and can reduce the energy usage of the nation. © 2017 IEEE.

关键词： Time series

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The mutational constraint spectrum quantified from variation in 141,456 humans (vol 581, pg 434, 2020)

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NATURE 2021年第7874期597卷 E3-E4页

作者： Gudmundsson, Sanna Karczewski, Konrad J. Francioli, Laurent C. Tiao, Grace Cummings, Beryl B. Alfoldi, Jessica Wang, Qingbo Collins, Ryan L. Laricchia, Kristen M. Ganna, Andrea Birnbaum, Daniel P. Gauthier, Laura D. Brand, Harrison Solomonson, Matthew Watts, Nicholas A. Rhodes, Daniel Singer-Berk, Moriel England, Eleina M. Seaby, Eleanor G. Kosmicki, Jack A. Walters, Raymond K. Tashman, Katherine Farjoun, Yossi Banks, Eric Poterba, Timothy Wang, Arcturus Seed, Cotton Whiffin, Nicola Chong, Jessica X. Samocha, Kaitlin E. Pierce-Hoffman, Emma Zappala, Zachary O'Donnell-Luria, Anne H. Minikel, Eric Vallabh Weisburd, Ben Lek, Monkol Ware, James S. Vittal, Christopher Armean, Irina M. Bergelson, Louis Cibulskis, Kristian Connolly, Kristen M. Covarrubias, Miguel Donnelly, Stacey Ferriera, Steven Gabriel, Stacey Gentry, Jeff Gupta, Namrata Jeandet, Thibault Kaplan, Diane Llanwarne, Christopher Munshi, Ruchi Novod, Sam Petrillo, Nikelle Roazen, David Ruano-Rubio, Valentin Saltzman, Andrea Schleicher, Molly Soto, Jose Tibbetts, Kathleen Tolonen, Charlotte Wade, Gordon Talkowski, Michael E. Neale, Benjamin M. Daly, Mark J. MacArthur, Daniel G. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Institute for Molecular Medicine Finland Helsinki Finland Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA National Heart & Lung Institute and MRC London Institute of Medical Sciences Imperial College London London UK Cardiovascular Research Centre Royal Brompton & Harefield Hospitals NHS Trust London UK Vertex Pharmaceuticals Inc Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Broad Genomics Broad Institute of MIT and Harvard Cambridge MA USA Department of Neurology Harvard Medical School Boston MA USA Cardiac Arrhythmia Service and Cardiovascular Research Center Massachusetts General Hospital Boston MA USA Estonian Genome Center Institute of Genomics University of Tartu Tartu Estonia Data Sciences Platform Broad Institute of MIT and Harvard Cambridge MA USA Diabetes Unit Massachusetts General Hospital Boston MA USA Program in Metabolism Broad Institute of MIT and Harvard Cambridge MA USA Department of Medicine Harvard Medical School Boston MA USA Division of General Internal Medicine Massachusetts General Hospital Boston MA USA Department of Public Health Faculty of Medicine University of Helsinki Helsinki Finland Cardiovascular Disease Initiative and Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Genomics Diabetes and Endocrinology Lund University Lund Sweden Folkhälsan Institute of Genetics Folkhälsan Research C

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Integrated systems approach identifies pathways from the genome to triglycerides through a metabolomic causal network

arXiv

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arXiv 2018年

作者： Yazdani, Akram Lorenzi, Philip L. Samiei, Ahmad Yazdani, Azam Climax Data Pattern HoustonTX77030 United States Department of Genetics and Genomic Sciences Icahn School of Medicine Mount SinaiNY10029 United States Proteomics and Metabolomics Core Facility Department of Bioinformatics and Computational Biology University of Texas MD Anderson Cancer Center HoustonTX77054 United States Hasso Plattner Institute Potsdam14482 Germany

Introduction: To leverage functionality and clinical relevance into understanding systems biology, one needs to understand the pathway of the genetic effects on risk factors/disease through intermediate molecular levels, such as metabolomics. Systems approaches integrate multi-omic information to find pathways to disease endpoints and make optimal inference decisions. Method: Here, we introduce a multi-stage approach to integrate causal networks in observational studies and GWAS to facilitate mechanistic understanding through identification of pathways from the genome to risk factors/disease via metabolomics. The pathways in causal networks reveal the underlying relationships behind observations, which do not play a significant role in more traditional correlative analyses, where one variable at a time is considered. Results: We identified a causal network over the metabolomic level using the genome directed acyclic graph (G-DAG), to systematically assess whether variations in the genome lead to variations in triglyceride levels as a risk factor of cardiovascular disease. We found LRRC46 and LRRC69 harboring loss-of-function mutations have significant effect on two metabolites with direct effects on triglyceride levels. We also found pathways of FAM198B and C6orf25 to triglycerides through indirect paths from metabolites. Conclusion: Integrating causal networks with GWAS facilitates mechanistic understanding in comparison to one-variable-at-a-time approaches due to accounting for relationships among components at intermediate molecular levels. This approach is complementary to experimental studies to identify efficacious targets in the age of big data sets. Copyright © 2018, The Authors. All rights reserved.

关键词： Bayesian networks

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