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A universal language for finding mass spectrometry data patterns

Nature Methods 2025年 1-8页

作者： Damiani, Tito Jarmusch, Alan K. Aron, Allegra T. Petras, Daniel Phelan, Vanessa V. Zhao, Haoqi Nina Bittremieux, Wout Acharya, Deepa D. Ahmed, Mohammed M. A. Bauermeister, Anelize Bertin, Matthew J. Boudreau, Paul D. Borges, Ricardo M. Bowen, Benjamin P. Brown, Christopher J. Chagas, Fernanda O. Clevenger, Kenneth D. Correia, Mario S. P. Crandall, William J. Crüsemann, Max Fahy, Eoin Fiehn, Oliver Garg, Neha Gerwick, William H. Gilbert, Jeffrey R. Globisch, Daniel Gomes, Paulo Wender P. Heuckeroth, Steffen James, C. Andrew Jarmusch, Scott A. Kakhkhorov, Sarvar A. Kang, Kyo Bin Kessler, Nikolas Kersten, Roland D. Kim, Hyunwoo Kirk, Riley D. Kohlbacher, Oliver Kontou, Eftychia E. Liu, Ken Lizama-Chamu, Itzel Luu, Gordon T. Luzzatto Knaan, Tal Mannochio-Russo, Helena Marty, Michael T. Matsuzawa, Yuki McAvoy, Andrew C. McCall, Laura-Isobel Mohamed, Osama G. Nahor, Omri Neuweger, Heiko Niedermeyer, Timo H. J. Nishida, Kozo Northen, Trent R. Overdahl, Kirsten E. Rainer, Johannes Reher, Raphael Rodriguez, Elys Sachsenberg, Timo T. Sanchez, Laura M. Schmid, Robin Stevens, Cole Subramaniam, Shankar Tian, Zhenyu Tripathi, Ashootosh Tsugawa, Hiroshi van der Hooft, Justin J. J. Vicini, Andrea Walter, Axel Weber, Tilmann Xiong, Quanbo Xu, Tao Pluskal, Tomáš Dorrestein, Pieter C. Wang, Mingxun Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czech Republic Metabolomics Core Facility Immunity Inflammation and Disease Laboratory Division of Intramural Research National Institute of Environmental Health Sciences National Institutes of Health Research Triangle Park NC United States Department of Chemistry and Biochemistry University of Denver Denver CO United States Functional Metabolomics Lab CMFI Cluster of Excellence University of Tuebingen Tuebingen Germany Department of Biochemistry University of California Riverside Riverside CA United States Department of Pharmaceutical Sciences Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado Anschutz Medical Campus Aurora CO United States Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA United States Biologicals and Natural Products Discovery Crop Protection R & D Corteva Agrisciences Indianapolis IN United States BioMolecular Sciences School of Pharmacy University of Mississippi Oxford MS United States Pharmacognosy Faculty of Pharmacy Al-Azhar University Nasr City Egypt Department of Fundamental Chemistry Institute of Chemistry University of São Paulo São Paulo Brazil Department of Chemistry Case Western Reserve University Cleveland OH United States Walter Mors Institute of Research on Natural Products Federal University of Rio de Janeiro Rio de Janeiro Brazil Environmental Genomics and Systems Biology Division Lawrence Berkeley National Lab Berkeley CA United States The Joint Genome Institute Lawrence Berkeley National Lab Berkeley CA United States Mass Spectrometry Center of Expertise Regulatory and Stewardship Corteva Agrisciences Indianapolis IN United States Biologicals and Natural Products Crop Protection R & D Corteva Agrisciences Indianapolis IN United States Department of Chemistry – BMC Science for Life Laborat

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized;most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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COVID-19 Testing Pipeline: Lesson Learned

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Dian Amirulloh Digdo Sudigyo Arif Budiarto Ika Nurlaila Erlin Listiyaningsih Andrew Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia 12940 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information System Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) in Indonesia is seen to be uncontrollably increasing that urges the government to leverage the capacity for the disease detections. Real-time polymerase chain reaction (RT-PCR), rapid test and computed tomography (CT) scan are the most common methods to determine if one has been infected regardless of whether or not the common symptoms of such Corona Virus Disease 2019 (COVID-19) surface. Among these three, RT-PCR is considered the gold standard for qualitative and quantitative assessment of SARS CoV-2 detection. The present paper aims at elaborating the framework of Roche's RT-PCR machine employed specifically for SARS CoV-2 detection performed by Genetics Indonesia which is deemed to be efficient and relatively quicker than other detection kits. RT-PCR machine detected SARS Cov-2 with RNA amplification curve equals to 10 copies RNA below the cut off value of Crossing point (Cp) positive control. Also elucidated in the paper is the implementations of EAV RNA and LightCycler® 96 RT-PCR System through which analysis time, amounts of individual required sample, as well as the reagents, can be accordingly reduced.

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TREM2 Burden associates solely with Alzheimer’s Disease, and decreases the Chance to become a Centenarian

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Alzheimer's & Dementia 2023年第S12期19卷

作者： Janna I.R. Dijkstra Lisa Vermunt Geert Jan Biessels den Anouk Braber Jurgen A.H.R. Claassen Peter Paul De Deyn Mohsen Ghanbari Martijn Huisman Marc Hulsman Inez H.G.B. Ramakers Marcel J. T. Reinders Harro Seelaar Niccoló Tesi Pieter Jelle Visser Wiesje M. van der Flier Charlotte E. Teunissen Henne Holstege Sven J Van Der Lee Amsterdam Neuroscience Amsterdam Netherlands Genomics of Neurodegenerative Diseases and Aging Human Genetics Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Amsterdam Netherlands Alzheimer Center Amsterdam Neurology Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Neurochemistry Laboratory Amsterdam UMC Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands Department of Neurology UMC Utrecht Brain Center University Medical Center Utrecht Utrecht Netherlands Department of Biological Psychology Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Department of Geriatrics & Radboudumc Alzheimer Center Radboud University Medical Center Nijmegen Netherlands Laboratory of Neurochemistry and Behaviour University of Antwerp Antwerp Belgium University Medical Center Groningen Neurology and Alzheimer Center Groningen Netherlands Department of Neurology and Memory Clinic Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken Antwerp Antwerp Belgium Department of Epidemiology Erasmus Medical Centre Rotterdam Netherlands Aging & Later Life Amsterdam Public Health Amsterdam Amsterdam Netherlands Department of Epidemiology & Data Science Amsterdam UMC location Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Sociology Amsterdam UMC location Vrije Universiteit Amsterdam Amsterdam Netherlands Genomics of Neurodegenerative Diseases and Aging Human Genetics Vrije Universiteit Amsterdam Amsterdam UMC location VUmc Amsterdam Netherlands Alzheimer Center Limburg School for Mental Health and Neuroscience (MHeNs) Maastricht University Maastricht Netherlands Delft Bioinformatics Lab Delft University of Technology Delft Delft Netherlands Department of Neurology Erasmus Medical Center Rotterdam Rotterdam Netherlands Department of Neurobiology Care Sciences and Society Division of Neurog

Background Rare TREM2 variants are major risk factors for Alzheimer’s disease (AD), but TREM2 variants may also confer a higher risk for other neurodegenerative diseases and a lower probability for cognitively healthy aging. We studied the TREM2 burden associated with different types of dementia and cognitively healthy centenarians (CHCs). Method We included participants of the 100-Plus Study, Amsterdam Dementia Cohort, Longitudinal Aging Study Amsterdam and various Dutch memory clinics. TREM2 variants (R47H, R62H, Q33X, R62C, T96K) were genotyped with a high density array or imputed using standard methods (TOPMED reference panel). To avoid family bias and ethnic differences, related individuals and those of non-European ancestry were excluded. Frequencies of individual variants and the burden of TREM2 variants were compared in AD dementia, dementia with Lewy Bodies (DLB), frontotemporal dementia (FTD), cognitively normal controls and CHCs (correcting for 5 principal components). Result We identified 242 carriers of TREM2 mutations and 6,683 controls without a TREM2 mutation with a mean age of 69 years (SD ±14 years), 50% female (Table 1). TREM2 variants were significantly associated with AD dementia (OR 1.4, 95%CI 1.1-1.8, p = 0.01) (Table 2). R47H conferred the highest risk for AD (OR 2.8, 95%CI 1.4-5.6, p = 0.004), compared to 1.3-fold increased risk of R62H (95%CI 0.9-1.7, p = 0.11). The carrier frequency was not increased in DLB (OR 1.4, 95%CI 0.7-2.6, p = 0.34) and was not increased in FTD compared to controls (OR 0.59, 95%CI 0.3-1.3, p = 0.18). Of interest, the allele frequency of TREM2 variants in centenarians is half of the expected frequency (OR = 0.46, 95%CI 0.1-1.1, p = 0.10), and only concerns R62H. Conclusion We replicate the association of TREM2 with increased risk of AD. In contrast to previous reports no association was observed with DLB or FTD. Cognitively healthy centenarians show a negative trend for TREM2 allele frequency suggesting that carryin

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Correction: Variability of temperature measurements recorded by a wearable device by biological sex

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Biology of sex differences 2023年第1期14卷 82页

作者： Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun K Viswanath Wendy Hartogensis Stephan Dilchert Frederick M Hecht Anoushka Chowdhary Claudine Anglo Leena Pandya Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E Mason Benjamin L Smarr Department of Biomedical Informatics UC San Diego Health University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. Bioinformatics and Systems Biology University of California San Diego San Diego CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Osher Center for Integrative Health University of California San Francisco San Francisco CA USA. Department of Management Zicklin School of Business Baruch College The City University of New York New York NY USA. San Diego Supercomputer Center University of California San Diego San Diego CA USA. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. bsmarr@eng.ucsd.edu. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. bsmarr@eng.ucsd.edu.

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Erratum for Sodsai et al., "Complete Genome Sequences of Mycobacterium farcinogenes Strains Isolated from Clinical Specimens from Patients in Thailand"

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Microbiology resource announcements 2021年第49期10卷 e0114521页

作者： Pimpayao Sodsai Piroon Jenjaroenpun Nattiya Hirankarn Thidathip Wongsurawat Suwatchareeporn Rotcheewaphan Center of Excellence in Immunology and Immune-Mediated Diseases Department of Microbiology Faculty of Medicine Chulalongkorn University Bangkok Thailand. Division of Immunology Department of Microbiology Faculty of Medicine Chulalongkorn University Bangkok Thailand. Division of Bioinformatics and Data Management for Research Research Group and Research Network Division Research Department Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand. Division of Bacteriology Department of Microbiology Faculty of Medicine Chulalongkorn University Bangkok Thailand.

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Genome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23 (vol 42, pg 2000, 2021)

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EUROPEAN HEART JOURNAL 2021年第20期42卷 2011-2011页

作者： Garnier, S. Sorbonne Université INSERM UMR-S1166 Research Unit on Cardiovascular Disorders Metabolism and Nutrition Team Genomics & Pathophysiology of Cardiovascular Diseases Paris 75013 France ICAN Institute for Cardiometabolism and Nutrition Paris 75013 France Department of Cardiology Division Heart & Lungs University Medical Center Utrecht Utrecht University Utrecht the Netherlands Regenerative Medicine Center University Medical Center Utrecht Utrecht the Netherlands Interfaculty Institute for Genetics and Functional Genomics Department of Functional Genomics University Medicine Greifswald Greifswald Germany DZHK (German Centre for Cardiovascular Research) partner site Greifswald Greifswald Germany Laboratory of Clinical Chemistry and Haematology University Medical Center Heidelberglaan 100 Utrecht the Netherlands Laboratory of Experimental Cardiology University Medical Center Utrecht Heidelberglaan 100 Utrecht the Netherlands Institute of Gender in Medicine and Center for Cardiovascular Research Charite University Hospital Berlin Germany DZHK (German Center for Cardiovascular Research) Berlin Germany Department of Internal Medicine Division of Cardiology Medical University of Vienna Austria Department of Internal Medicine Medical University of Regensburg Germany Penn Cardiovascular Institute and Department of Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Cardiology Department APHP Pitié-Salpêtrière Hospital Paris France IRCCS Fondazione Policlinico San Matteo Pavia Italy National Heart and Lung Institute Imperial College London London UK National Heart Centre Singapore Singapore Duke-NUS Singapore Center for Applied Genomics Children’s Hospital of Philadelphia Philadelphia PA USA Department of Genetic Epidemiology University of Regensburg Regensburg Germany Royal Brompton Hospital London UK Medical Research Council Clinical Sciences Centre Faculty of Medicine Imperial College London South Kensington Campus London

Aims Our objective was to better understand the genetic bases of dilated cardiomyopathy (DCM), a leading cause of systolic heart *** and results We conducted the largest genome-wide association study performed so far in DCM, with 2719 cases and 4440 controls in the discovery population. We identified and replicated two new DCM-associated loci on chromosome 3p25.1 [lead single-nucleotide polymorphism (SNP) rs62232870, P = 8.7 × 10−11 and 7.7 × 10−4 in the discovery and replication steps, respectively] and chromosome 22q11.23 (lead SNP rs7284877, P = 3.3 × 10−8 and 1.4 × 10−3 in the discovery and replication steps, respectively), while confirming two previously identified DCM loci on chromosomes 10 and 1, BAG3 and HSPB7. A genetic risk score constructed from the number of risk alleles at these four DCM loci revealed a 3-fold increased risk of DCM for individuals with 8 risk alleles compared to individuals with 5 risk alleles (median of the referral population). In silico annotation and functional 4C-sequencing analyses on iPSC-derived cardiomyocytes identify SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine transporter whose involvement in myocardial dysfunction and DCM is supported by numerous observations in humans and animals. At the 22q11.23 locus, in silico and data mining annotations, and to a lesser extent functional analysis, strongly suggest SMARCB1 as the candidate culprit *** This study provides a better understanding of the genetic architecture of DCM and sheds light on novel biological pathways underlying heart failure.

关键词： Dilated cardiomyopathy Heart failure GWAS Imputation 4C-sequencing Genetic risk score

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The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking

arXiv

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arXiv 2019年

作者： von Cube, Maja Schumacher, Martin Putter, Hein Timsit, Jéan-François de Velde, Cornelis van Wolkewitz, Martin Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modelling University of Freiburg Freiburg Germany Leiden University Medical Centre Department of Medical Statistics and Bioinformatics UMR 1137 IAME Inserm Université Paris Diderot Paris France APHP Medical and Infectious Diseases ICU Bichat Hospital Paris France Leiden University Medical Centre Department of Surgery Leiden Netherlands

The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure could be extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Author Correction: An inflammatory state remodels the immune microenvironment and improves risk stratification in acute myeloid leukemia

Nature cancer

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Nature cancer 2023年第1期4卷 149页

作者： Audrey Lasry Bettina Nadorp Maarten Fornerod Deedra Nicolet Huiyun Wu Christopher J Walker Zhengxi Sun Matthew T Witkowski Anastasia N Tikhonova Maria Guillamot-Ruano Geraldine Cayanan Anna Yeaton Gabriel Robbins Esther A Obeng Aristotelis Tsirigos Richard M Stone John C Byrd Stanley Pounds William L Carroll Tanja A Gruber Ann-Kathrin Eisfeld Iannis Aifantis Department of Pathology New York University School of Medicine New York NY USA. Laura & Isaac Perlmutter Cancer Center New York University School of Medicine New York NY USA. Applied Bioinformatics Laboratories New York University School of Medicine New York NY USA. Department of Cell Biology Erasmus Medical Center Rotterdam The Netherlands. The Ohio State University Comprehensive Cancer Center Clara D. Bloomfield Center for Leukemia Outcomes Research Columbus OH USA. Alliance Statistics and Data Center The Ohio State University Comprehensive Cancer Center Columbus OH USA. Department of Biostatistics St. Jude Children's Research Hospital Memphis TN USA. Department of Oncology St. Jude Children's Research Hospital Memphis TN USA. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA. Department of Internal Medicine University of Cincinnati Cincinnati OH USA. Department of Pediatrics Stanford University School of Medicine Stanford CA USA. tagruber@stanford.edu. The Ohio State University Comprehensive Cancer Center Clara D. Bloomfield Center for Leukemia Outcomes Research Columbus OH USA. ann-kathrin.eisfeld@osumc.edu. Division of Hematology Department of Internal Medicine The Ohio State University Comprehensive Cancer Center Columbus OH USA. ann-kathrin.eisfeld@osumc.edu. Pelotonia Institute for Immuno-Oncology The Ohio State University Columbus OH USA. ann-kathrin.eisfeld@osumc.edu. Department of Pathology New York University School of Medicine New York NY USA. ioannis.aifantis@***. Laura & Isaac Perlmutter Cancer Center New York University School of Medicine New York NY USA. ioannis.aifantis@***.

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Reciprocal best match graphs

arXiv

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arXiv 2019年

作者： Geiß, Manuela Stadler, Peter F. Hellmuth, Marc Bioinformatics Group Department of Computer Scienceand Interdisciplinary Center of Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Research Center for Civilization Diseases Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max-Planck-Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Straße 47 GreifswaldD-17487 Germany Center for Bioinformatics Saarland University Building E 2.1 P.O. Box 151150 SaarbrückenD-66041 Germany

Reciprocal best matches play an important role in numerous applications in computational biology, in particular as the basis of many widely used tools for orthology assessment. Nevertheless, very little is known about their mathematical structure. Here, we investigate the structure of reciprocal best match graphs (RBMGs). In order to abstract from the details of measuring distances, we define reciprocal best matches here as pairwise most closely related leaves in a gene tree, arguing that conceptually this is the notion that is pragmatically approximated by distance- or similarity-based heuristics. We start by showing that a graph G is an RBMG if and only if its quotient graph w.r.t. a certain thinness relation is an RBMG. Furthermore, it is necessary and sufficient that all connected components of G are RBMGs. The main result of this contribution is a complete characterization of RBMGs with 3 colors/species that can be checked in polynomial time. For 3 colors, there are three distinct classes of trees that are related to the structure of the phylogenetic trees explaining them. We derive an approach to recognize RBMGs with an arbitrary number of colors;it remains open however, whether a polynomial-time for RBMG recognition exists. In addition, we show that RBMGs that at the same time are cographs (co-RBMGs) can be recognized in polynomial time. Co-RBMGs are characterized in terms of hierarchically colored cographs, a particular class of vertex colored cographs that is introduced here. The (least resolved) trees that explain co-RBMGs can be constructed in polynomial time. Copyright © 2019, The Authors. All rights reserved.

关键词： bioinformatics

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Community Curation and Expert Curation of Human Long Noncoding RNAs with LncRNAWiki and LncBook

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Current Protocols in bioinformatics 2019年第1期67卷 e82页

作者： Ma, Lina Cao, Jiabao Liu, Lin Li, Zhao Shireen, Huma Pervaiz, Nashaiman Batool, Fatima Raza, Rabail Z. Zou, Dong Bao, Yiming Abbasi, Amir A. Zhang, Zhang BIG Data Center Beijing Institute of Genomics Chinese Academy of Sciences Beijing China CAS Key Laboratory of Genome Sciences and Information Beijing Institute of Genomics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China National Center for Bioinformatics Programme of Comparative and Evolutionary Genomics Faculty of Biological Sciences Quaid-i-Azam University Islamabad Pakistan

In recent years, the number of human long noncoding RNAs (lncRNAs) that have been identified has increased exponentially. However, these lncRNAs are poorly annotated compared to protein-coding genes, posing great challenges for a better understanding of their functional significance and elucidating their complex functioning molecular mechanisms. Here we employ both community and expert curation to yield a comprehensive collection of human lncRNAs and their annotations. Specifically, LncRNAWiki (http://***/***/Main_Page) uses a wiki-based community curation model, thus showing great promise in dealing with the flood of biological knowledge, while LncBook (http://***/lncbook) is an expert curation–based database that provides a complement to LncRNAWiki. LncBook features a comprehensive collection of human lncRNAs and a systematic curation of lncRNAs by multi-omics data integration, functional annotation, and disease association. These protocols provide step-by-step instructions on how to browse and search a specific lncRNA and how to obtain a range of related information including expression, methylation, variation, function, and disease association. © 2019 by John Wiley & Sons, Inc. © 2019 John Wiley & Sons, Inc.

关键词： community curation disease expert curation long noncoding RNA multi-omics data

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