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Kontou PI, Bagos PG. The goldmine of GWAS summary statistics: a systematic review of methods and tools. BioData Min 2024; 17:31. [PMID: 39238044 PMCID: PMC11375927 DOI: 10.1186/s13040-024-00385-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 08/27/2024] [Indexed: 09/07/2024] Open
Abstract
Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.
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Affiliation(s)
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece.
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2
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Ősz Á, Pongor LS, Szirmai D, Győrffy B. A snapshot of 3649 Web-based services published between 1994 and 2017 shows a decrease in availability after 2 years. Brief Bioinform 2020; 20:1004-1010. [PMID: 29228189 PMCID: PMC6585384 DOI: 10.1093/bib/bbx159] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 10/20/2017] [Indexed: 01/07/2023] Open
Abstract
Background The long-term availability of online Web services is of utmost importance to ensure reproducibility of analytical results. However, because of lack of maintenance following acceptance, many servers become unavailable after a short period of time. Our aim was to monitor the accessibility and the decay rate of published Web services as well as to determine the factors underlying trends changes. Methods We searched PubMed to identify publications containing Web server-related terms published between 1994 and 2017. Automatic and manual screening was used to check the status of each Web service. Kruskall–Wallis, Mann–Whitney and Chi-square tests were used to evaluate various parameters, including availability, accessibility, platform, origin of authors, citation, journal impact factor and publication year. Results We identified 3649 publications in 375 journals of which 2522 (69%) were currently active. Over 95% of sites were running in the first 2 years, but this rate dropped to 84% in the third year and gradually sank afterwards (P < 1e-16). The mean half-life of Web services is 10.39 years. Working Web services were published in journals with higher impact factors (P = 4.8e-04). Services published before the year 2000 received minimal attention. The citation of offline services was less than for those online (P = 0.022). The majority of Web services provide analytical tools, and the proportion of databases is slowly decreasing. Conclusions. Almost one-third of Web services published to date went out of service. We recommend continued support of Web-based services to increase the reproducibility of published results.
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Affiliation(s)
- Ágnes Ősz
- MTA TTK Lendület Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudósok körútja 2, H-1117, Budapest, Hungary
| | - Lőrinc Sándor Pongor
- MTA TTK Lendület Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudósok körútja 2, H-1117, Budapest, Hungary
| | - Danuta Szirmai
- MTA TTK Lendület Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudósok körútja 2, H-1117, Budapest, Hungary
| | - Balázs Győrffy
- MTA TTK Lendület Cancer Biomarker Research Group, Hungarian Academy of Sciences, Institute of Enzymology, Magyar tudósok körútja 2, H-1117, Budapest, Hungary
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3
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Bagnacani A, Wolfien M, Wolkenhauer O. Tools for Understanding miRNA-mRNA Interactions for Reproducible RNA Analysis. Methods Mol Biol 2019; 1912:199-214. [PMID: 30635895 DOI: 10.1007/978-1-4939-8982-9_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. The use of RNA data in gene expression analysis has become increasingly important to gain insights into the regulatory mechanisms behind miRNA-mRNA interactions. As a result, we are confronted with a growing landscape of tools, while standards for reproducibility and benchmarking lag behind. This work identifies the challenges for reproducible RNA analysis, and highlights best practices on the processing and dissemination of scientific results. We found that the success of a tool does not solely depend on its performances: equally important is how a tool is received, and then supported within a community. This leads us to a detailed presentation of the RNA workbench, a community effort for sharing workflows and processing tools, built on top of the Galaxy framework. Here, we follow the community guidelines to extend its portfolio of RNA tools with the integration of the TriplexRNA ( https://triplexrna.org ). Our findings provide the basis for the development of a recommendation system, to guide users in the choice of tools and workflows.
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Affiliation(s)
- Andrea Bagnacani
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
| | - Markus Wolfien
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany
- Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre, Stellenbosch University, Stellenbosch, South Africa
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4
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Callahan A, Winnenburg R, Shah NH. U-Index, a dataset and an impact metric for informatics tools and databases. Sci Data 2018; 5:180043. [PMID: 29557976 PMCID: PMC5859919 DOI: 10.1038/sdata.2018.43] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 02/08/2018] [Indexed: 01/28/2023] Open
Abstract
Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources' impact over time. Our dataset differentiates the context in which citations occur to distinguish between 'awareness' and 'usage', and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.
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Affiliation(s)
- Alison Callahan
- Stanford Center for Biomedical Informatics Research, Stanford University, Medical School Office Building X215, Stanford, CA 94305, USA
| | - Rainer Winnenburg
- Stanford Center for Biomedical Informatics Research, Stanford University, Medical School Office Building X215, Stanford, CA 94305, USA
| | - Nigam H Shah
- Stanford Center for Biomedical Informatics Research, Stanford University, Medical School Office Building X215, Stanford, CA 94305, USA
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5
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Abstract
Software produced for research, published and otherwise, suffers from a number of common problems that make it difficult or impossible to run outside the original institution or even off the primary developer's computer. We present ten simple rules to make such software robust enough to be run by anyone, anywhere, and thereby delight your users and collaborators.
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Affiliation(s)
- Morgan Taschuk
- Genome Sequence Informatics, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Greg Wilson
- Software Carpentry Foundation, Austin, Texas, United States of America
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6
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Gnimpieba EZ, VanDiermen MS, Gustafson SM, Conn B, Lushbough CM. Bio-TDS: bioscience query tool discovery system. Nucleic Acids Res 2017; 45:D1117-D1122. [PMID: 27924016 PMCID: PMC5210639 DOI: 10.1093/nar/gkw940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 09/24/2016] [Accepted: 10/17/2016] [Indexed: 11/12/2022] Open
Abstract
Bioinformatics and computational biology play a critical role in bioscience and biomedical research. As researchers design their experimental projects, one major challenge is to find the most relevant bioinformatics toolkits that will lead to new knowledge discovery from their data. The Bio-TDS (Bioscience Query Tool Discovery Systems, http://biotds.org/) has been developed to assist researchers in retrieving the most applicable analytic tools by allowing them to formulate their questions as free text. The Bio-TDS is a flexible retrieval system that affords users from multiple bioscience domains (e.g. genomic, proteomic, bio-imaging) the ability to query over 15 000 analytic tool descriptions integrated from well-established, community repositories. One of the primary components of the Bio-TDS is the ontology and natural language processing workflow for annotation, curation, query processing, and evaluation. The Bio-TDS’s scientific impact was evaluated using sample questions posed by researchers retrieved from Biostars, a site focusing on biological data analysis. The Bio-TDS was compared to five similar bioscience analytic tool retrieval systems with the Bio-TDS outperforming the others in terms of relevance and completeness. The Bio-TDS offers researchers the capacity to associate their bioscience question with the most relevant computational toolsets required for the data analysis in their knowledge discovery process.
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Affiliation(s)
- Etienne Z Gnimpieba
- Biomedical Engineering Department, University of South Dakota, 4800 North Career Ave, Sioux Falls, SD 57107, USA .,BioSNTR, Brookings, SD 57006, USA
| | - Menno S VanDiermen
- Biomedical Engineering Department, University of South Dakota, 4800 North Career Ave, Sioux Falls, SD 57107, USA
| | - Shayla M Gustafson
- Biomedical Engineering Department, University of South Dakota, 4800 North Career Ave, Sioux Falls, SD 57107, USA
| | - Bill Conn
- Biomedical Engineering Department, University of South Dakota, 4800 North Career Ave, Sioux Falls, SD 57107, USA
| | - Carol M Lushbough
- Biomedical Engineering Department, University of South Dakota, 4800 North Career Ave, Sioux Falls, SD 57107, USA.,BioSNTR, Brookings, SD 57006, USA
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7
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Ison J, Rapacki K, Ménager H, Kalaš M, Rydza E, Chmura P, Anthon C, Beard N, Berka K, Bolser D, Booth T, Bretaudeau A, Brezovsky J, Casadio R, Cesareni G, Coppens F, Cornell M, Cuccuru G, Davidsen K, Vedova GD, Dogan T, Doppelt-Azeroual O, Emery L, Gasteiger E, Gatter T, Goldberg T, Grosjean M, Grüning B, Helmer-Citterich M, Ienasescu H, Ioannidis V, Jespersen MC, Jimenez R, Juty N, Juvan P, Koch M, Laibe C, Li JW, Licata L, Mareuil F, Mičetić I, Friborg RM, Moretti S, Morris C, Möller S, Nenadic A, Peterson H, Profiti G, Rice P, Romano P, Roncaglia P, Saidi R, Schafferhans A, Schwämmle V, Smith C, Sperotto MM, Stockinger H, Vařeková RS, Tosatto SCE, de la Torre V, Uva P, Via A, Yachdav G, Zambelli F, Vriend G, Rost B, Parkinson H, Løngreen P, Brunak S. Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res 2015; 44:D38-47. [PMID: 26538599 PMCID: PMC4702812 DOI: 10.1093/nar/gkv1116] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 10/13/2015] [Indexed: 01/24/2023] Open
Abstract
Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.
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Affiliation(s)
- Jon Ison
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Kristoffer Rapacki
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Hervé Ménager
- Centre d'Informatique pour la Biologie, C3BI, Institut Pasteur, France
| | - Matúš Kalaš
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
| | - Emil Rydza
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Piotr Chmura
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Christian Anthon
- Department of Veterinary Clinical and Animal Sciences, Faculty for Health and Medical Sciences, University of Copenhagen, Denmark
| | - Niall Beard
- School of Computer Science, University of Manchester, UK
| | - Karel Berka
- Department of Physical Chemistry, RCPTM, Faculty of Science, Palacky University, Czech Republic
| | - Dan Bolser
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Tim Booth
- NEBC Wallingford, Centre for Ecology and Hydrology, UK
| | - Anthony Bretaudeau
- INRA, UMR Institut de Génétique, Environnement et Protection des Plantes (IGEPP), BioInformatics Platform for Agroecosystems Arthropods (BIPAA), France INRIA, IRISA, GenOuest Core Facility, France
| | - Jan Brezovsky
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, Czech Republic
| | - Rita Casadio
- Bologna Biocomputing Group, University of Bologna, Italy
| | | | - Frederik Coppens
- Department of Plant Systems Biology, VIB, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Belgium
| | | | | | - Kristian Davidsen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | - Tunca Dogan
- UniProt, European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Laura Emery
- The European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Thomas Gatter
- Faculty of Technology and Center for Biotechnology, Universität Bielefeld, Germany
| | | | - Marie Grosjean
- Institut Français de Bioinformatique (French Institute of Bioinformatics), CNRS, UMS3601, France
| | - Björn Grüning
- Albert-Ludwigs-Universität Freiburg, Fahnenbergplatz, 79085 Freiburg
| | | | - Hans Ienasescu
- Bioinformatics Centre, Department of Biology, University of Copenhagen, Denmark
| | | | - Martin Closter Jespersen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | - Nick Juty
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Peter Juvan
- Centre for Functional Genomics and Biochips, Faculty of Medicine, University of Ljubljana, Slovenia
| | | | - Camille Laibe
- The European Bioinformatics Institute (EMBL-EBI), UK
| | - Jing-Woei Li
- Faculty of Medicine, The Chinese University of Hong Kong, China Hong Kong Bioinformatics Centre, School of Life Sciences,The Chinese University of Hong Kong, China
| | - Luana Licata
- Dept. of Biology, University of Rome Tor Vergata, Italy
| | - Fabien Mareuil
- Centre d'Informatique pour la Biologie, C3BI, Institut Pasteur, France
| | - Ivan Mičetić
- Department of Biomedical Sciences, University of Padua, Italy
| | | | - Sebastien Moretti
- SIB Swiss Institute of Bioinformatics, Switzerland Department of Ecology and Evolution, Biophore, Evolutionary Bioinformatics group, University of Lausanne, Switzerland
| | | | - Steffen Möller
- Department of Dermatology, University of Lübeck, Germany Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Germany
| | | | - Hedi Peterson
- Institute of Computer Science, University of Tartu, Estonia
| | | | - Peter Rice
- Department of Computing, William Penney Laboratory, Imperial College London, UK
| | | | | | - Rabie Saidi
- UniProt, European Bioinformatics Institute (EMBL-EBI), UK
| | | | - Veit Schwämmle
- Protein Research Group, Department for Biochemistry and Molecular Biology, University of Southern Denmark, Denmark
| | | | - Maria Maddalena Sperotto
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | | | | | | | - Victor de la Torre
- National Bioinformatics Institute Unit (INB), Fundacion Centro Nacional de Investigaciones Oncologicas, Spain
| | | | - Allegra Via
- Dept. of Physics, Sapienza University, Italy
| | - Guy Yachdav
- Department of Informatics, Bioinformatics-I12, TUM, Germany
| | - Federico Zambelli
- Institute of Biomembranes and Bioenergetics, National Research Council (CNR), and Dept. of Biosciences, University of Milano, Italy
| | - Gert Vriend
- Radboud University Medical Centre, CMBI, Netherlands
| | - Burkhard Rost
- Department of Informatics, Bioinformatics-I12, TUM, Germany
| | | | - Peter Løngreen
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark
| | - Søren Brunak
- Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Denmark Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
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Abstract
With the availability of numerous curated databases, researchers are now able to efficiently use the multitude of biological data by integrating these resources via hyperlinks and cross-references. A large proportion of bioinformatics research tasks, however, may include labor-intensive tasks such as fetching, parsing, and merging datasets and functional annotations from distributed multi-domain databases. This data integration issue is one of the key challenges in bioinformatics. We aim to provide an identifier conversion and data aggregation system as a part of solution to solve this problem with a service named G-Links, 1) by gathering resource URI information from 130 databases and 30 web services in a gene-centric manner so that users can retrieve all available links about a given gene, 2) by providing RESTful API for easy retrieval of links including facet searching based on keywords and/or predicate types, and 3) by producing a variety of outputs as visual HTML page, tab-delimited text, and in Semantic Web formats such as Notation3 and RDF. G-Links as well as other relevant documentation are available at http://link.g-language.org/.
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Affiliation(s)
- Kazuki Oshita
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan
| | - Kazuharu Arakawa
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan
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9
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Henry VJ, Bandrowski AE, Pepin AS, Gonzalez BJ, Desfeux A. OMICtools: an informative directory for multi-omic data analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau069. [PMID: 25024350 PMCID: PMC4095679 DOI: 10.1093/database/bau069] [Citation(s) in RCA: 136] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Recent advances in ‘omic’ technologies have created unprecedented opportunities for biological research, but current software and database resources are extremely fragmented. OMICtools is a manually curated metadatabase that provides an overview of more than 4400 web-accessible tools related to genomics, transcriptomics, proteomics and metabolomics. All tools have been classified by omic technologies (next-generation sequencing, microarray, mass spectrometry and nuclear magnetic resonance) associated with published evaluations of tool performance. Information about each tool is derived either from a diverse set of developers, the scientific literature or from spontaneous submissions. OMICtools is expected to serve as a useful didactic resource not only for bioinformaticians but also for experimental researchers and clinicians. Database URL:http://omictools.com/
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Affiliation(s)
- Vincent J Henry
- Haute-Normandie-INSERM ERI-28, Institute for Research and Innovation in Biomedicine of Rouen University, 76183 Rouen, France, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA and STATSARRAY, 76300 Sotteville-lès-Rouen, France
| | - Anita E Bandrowski
- Haute-Normandie-INSERM ERI-28, Institute for Research and Innovation in Biomedicine of Rouen University, 76183 Rouen, France, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA and STATSARRAY, 76300 Sotteville-lès-Rouen, France
| | - Anne-Sophie Pepin
- Haute-Normandie-INSERM ERI-28, Institute for Research and Innovation in Biomedicine of Rouen University, 76183 Rouen, France, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA and STATSARRAY, 76300 Sotteville-lès-Rouen, France
| | - Bruno J Gonzalez
- Haute-Normandie-INSERM ERI-28, Institute for Research and Innovation in Biomedicine of Rouen University, 76183 Rouen, France, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA and STATSARRAY, 76300 Sotteville-lès-Rouen, France
| | - Arnaud Desfeux
- Haute-Normandie-INSERM ERI-28, Institute for Research and Innovation in Biomedicine of Rouen University, 76183 Rouen, France, Center for Research in Biological Systems, University of California, San Diego, 9500 Gilman Dr. La Jolla, CA 92093, USA and STATSARRAY, 76300 Sotteville-lès-Rouen, France
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10
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Li M, Chen YB, Clintworth WA. Expanding roles in a library-based bioinformatics service program: a case study. J Med Libr Assoc 2014; 101:303-9. [PMID: 24163602 DOI: 10.3163/1536-5050.101.4.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
QUESTION How can a library-based bioinformatics support program be implemented and expanded to continuously support the growing and changing needs of the research community? SETTING A program at a health sciences library serving a large academic medical center with a strong research focus is described. METHODS The bioinformatics service program was established at the Norris Medical Library in 2005. As part of program development, the library assessed users' bioinformatics needs, acquired additional funds, established and expanded service offerings, and explored additional roles in promoting on-campus collaboration. RESULTS Personnel and software have increased along with the number of registered software users and use of the provided services. CONCLUSION With strategic efforts and persistent advocacy within the broader university environment, library-based bioinformatics service programs can become a key part of an institution's comprehensive solution to researchers' ever-increasing bioinformatics needs.
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11
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Ladner JT, Beitzel B, Chain PSG, Davenport MG, Donaldson EF, Frieman M, Kugelman JR, Kuhn JH, O'Rear J, Sabeti PC, Wentworth DE, Wiley MR, Yu GY, Sozhamannan S, Bradburne C, Palacios G. Standards for sequencing viral genomes in the era of high-throughput sequencing. mBio 2014; 5:e01360-14. [PMID: 24939889 PMCID: PMC4068259 DOI: 10.1128/mbio.01360-14] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Thanks to high-throughput sequencing technologies, genome sequencing has become a common component in nearly all aspects of viral research; thus, we are experiencing an explosion in both the number of available genome sequences and the number of institutions producing such data. However, there are currently no common standards used to convey the quality, and therefore utility, of these various genome sequences. Here, we propose five "standard" categories that encompass all stages of viral genome finishing, and we define them using simple criteria that are agnostic to the technology used for sequencing. We also provide genome finishing recommendations for various downstream applications, keeping in mind the cost-benefit trade-offs associated with different levels of finishing. Our goal is to define a common vocabulary that will allow comparison of genome quality across different research groups, sequencing platforms, and assembly techniques.
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Affiliation(s)
- Jason T Ladner
- Center for Genome Sciences, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, USA
| | - Brett Beitzel
- Center for Genome Sciences, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, USA
| | - Patrick S G Chain
- Bioinformatics and Analytics Team, Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
| | - Matthew G Davenport
- National Security Systems Biology Center, Asymmetric Operations Sector, Johns Hopkins University, Applied Physics Laboratory, Laurel, Maryland, USA
| | - Eric F Donaldson
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA Filovirus Animal Nonclinical Group (FANG) Well Characterized Challenge Material Working Group
| | - Matthew Frieman
- Department of Microbiology and Immunology, University of Maryland at Baltimore, Baltimore, Maryland, USA
| | - Jeffrey R Kugelman
- Center for Genome Sciences, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, USA
| | - Jens H Kuhn
- Filovirus Animal Nonclinical Group (FANG) Well Characterized Challenge Material Working Group Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Fort Detrick, Frederick, Maryland, USA
| | - Jules O'Rear
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA Filovirus Animal Nonclinical Group (FANG) Well Characterized Challenge Material Working Group
| | | | | | - Michael R Wiley
- Center for Genome Sciences, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, USA
| | - Guo-Yun Yu
- Center for Genome Sciences, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, USA
| | | | - Christopher Bradburne
- National Security Systems Biology Center, Asymmetric Operations Sector, Johns Hopkins University, Applied Physics Laboratory, Laurel, Maryland, USA
| | - Gustavo Palacios
- Center for Genome Sciences, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, USA Filovirus Animal Nonclinical Group (FANG) Well Characterized Challenge Material Working Group
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12
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Brazas MD, Ouellette BFF. Navigating the changing learning landscape: perspective from bioinformatics.ca. Brief Bioinform 2013; 14:556-62. [PMID: 23515468 PMCID: PMC3771234 DOI: 10.1093/bib/bbt016] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
With the advent of YouTube channels in bioinformatics, open platforms for problem solving in bioinformatics, active web forums in computing analyses and online resources for learning to code or use a bioinformatics tool, the more traditional continuing education bioinformatics training programs have had to adapt. Bioinformatics training programs that solely rely on traditional didactic methods are being superseded by these newer resources. Yet such face-to-face instruction is still invaluable in the learning continuum. Bioinformatics.ca, which hosts the Canadian Bioinformatics Workshops, has blended more traditional learning styles with current online and social learning styles. Here we share our growing experiences over the past 12 years and look toward what the future holds for bioinformatics training programs.
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Affiliation(s)
- Michelle D Brazas
- Informatics and Bio-computing Program, Ontario Institute for Cancer Research, 101 College, Toronto, Ontario M5G 1L7, Canada. Tel/Fax: +416-673-8502.
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