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Journal articles on the topic 'Bioinformatics infrastructure'

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Published: 4 June 2021
Last updated: 4 February 2022

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1

ROBBINS, ROBERT J. "Bioinformatics: Essential Infrastructure for Global Biology1." Journal of Computational Biology 3, no. 3 (1996): 465–78. http://dx.doi.org/10.1089/cmb.1996.3.465.

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2

Perriere, Guy. "ReNaBi-IFB: The French Bioinformatics Infrastructure." EMBnet.journal 18, no. 1 (2012): 12. http://dx.doi.org/10.14806/ej.18.1.502.

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3

Whitfield, Eleanor J., Manuela Pruess, and Rolf Apweiler. "Bioinformatics database infrastructure for biotechnology research." Journal of Biotechnology 124, no. 4 (2006): 629–39. http://dx.doi.org/10.1016/j.jbiotec.2006.04.006.

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4

Blomberg, Niklas, Arlindo Oliveira, Barend Mons, Bengt Persson, and Inge Jonassen. "The ELIXIR channel in F1000Research." F1000Research 4 (December 18, 2015): 1471. http://dx.doi.org/10.12688/f1000research.7587.1.

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ELIXIR, the European life science infrastructure for biological information, is a unique initiative to consolidate Europe’s national centres, services, and core bioinformatics resources into a single, coordinated infrastructure. ELIXIR brings together Europe’s major life-science data archives and connects these with national bioinformatics infrastructures - the ELIXIR Nodes. This editorial introduces the ELIXIR channel in F1000Research; the aim of the channel is to collect and present ELIXIR’s scientific and operational output, engage with the broad life science community and encourage discussion on proposed infrastructure solutions. Submissions will be assessed by the ELIXIR channel Editorial Board to ensure they are relevant to ELIXIR community, and subjected to F1000Research open peer review process.
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Blomberg, Niklas, Arlindo Oliveira, Barend Mons, Bengt Persson, and Inge Jonassen. "The ELIXIR channel in F1000Research." F1000Research 4 (May 4, 2016): 1471. http://dx.doi.org/10.12688/f1000research.7587.2.

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ELIXIR, the European life science infrastructure for biological information, is a unique initiative to consolidate Europe’s national centres, services, and core bioinformatics resources into a single, coordinated infrastructure. ELIXIR brings together Europe’s major life-science data archives and connects these with national bioinformatics infrastructures - the ELIXIR Nodes. This editorial introduces the ELIXIR channel in F1000Research; the aim of the channel is to collect and present ELIXIR’s scientific and operational output, engage with the broad life science community and encourage discussion on proposed infrastructure solutions. Submissions will be assessed by the ELIXIR channel Advisory Board to ensure they are relevant to ELIXIR community, and subjected to F1000Research open peer review process.
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6

Roterman, Irena. "E-infrastructure technologies triggering of Bioinformatics development." Bioinformation 2, no. 4 (2007): 126–27. http://dx.doi.org/10.6026/97320630002126.

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7

Butte, Atul J. "Challenges in bioinformatics: infrastructure, models and analytics." Trends in Biotechnology 19, no. 5 (2001): 159–60. http://dx.doi.org/10.1016/s0167-7799(01)01603-1.

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8

Mulder, Nicola J., Ezekiel Adebiyi, Marion Adebiyi, et al. "Development of Bioinformatics Infrastructure for Genomics Research." Global Heart 12, no. 2 (2017): 91. http://dx.doi.org/10.1016/j.gheart.2017.01.005.

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9

Ison, Jon, Hervé Ménager, Bryan Brancotte, et al. "Community curation of bioinformatics software and data resources." Briefings in Bioinformatics 21, no. 5 (2019): 1697–705. http://dx.doi.org/10.1093/bib/bbz075.

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Abstract The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.
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10

Kadri, Sabah. "Building Infrastructure and Workflows for Clinical Bioinformatics Pipelines." Advances in Molecular Pathology 3 (November 2020): 157–67. http://dx.doi.org/10.1016/j.yamp.2020.07.014.

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11

Williams, Jason J., and Tracy K. Teal. "A vision for collaborative training infrastructure for bioinformatics." Annals of the New York Academy of Sciences 1387, no. 1 (2016): 54–60. http://dx.doi.org/10.1111/nyas.13207.

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12

Tauch, Andreas, and Arwa Al-Dilaimi. "Bioinformatics in Germany: toward a national-level infrastructure." Briefings in Bioinformatics 20, no. 2 (2017): 370–74. http://dx.doi.org/10.1093/bib/bbx040.

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13

Mayer, Gerhard, Christian Quast, Janine Felden, et al. "A generally applicable lightweight method for calculating a value structure for tools and services in bioinformatics infrastructure projects." Briefings in Bioinformatics 20, no. 4 (2017): 1215–21. http://dx.doi.org/10.1093/bib/bbx140.

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Abstract Sustainable noncommercial bioinformatics infrastructures are a prerequisite to use and take advantage of the potential of big data analysis for research and economy. Consequently, funders, universities and institutes as well as users ask for a transparent value model for the tools and services offered. In this article, a generally applicable lightweight method is described by which bioinformatics infrastructure projects can estimate the value of tools and services offered without determining exactly the total costs of ownership. Five representative scenarios for value estimation from a rough estimation to a detailed breakdown of costs are presented. To account for the diversity in bioinformatics applications and services, the notion of service-specific ‘service provision units’ is introduced together with the factors influencing them and the main underlying assumptions for these ‘value influencing factors’. Special attention is given on how to handle personnel costs and indirect costs such as electricity. Four examples are presented for the calculation of the value of tools and services provided by the German Network for Bioinformatics Infrastructure (de.NBI): one for tool usage, one for (Web-based) database analyses, one for consulting services and one for bioinformatics training events. Finally, from the discussed values, the costs of direct funding and the costs of payment of services by funded projects are calculated and compared.
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14

Carter, Tonia C., and Max M. He. "Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine." Journal of Healthcare Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/3617572.

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Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.
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15

Ali, Muhammad Muddassir, Muhammad Hamid, Muhammad Saleem, et al. "Status of Bioinformatics Education in South Asia: Past and Present." BioMed Research International 2021 (April 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/5568262.

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Bioinformatics education has been a hot topic in South Asia, and the interest in this education peaks with the start of the 21st century. The governments of South Asian countries had a systematic effort for bioinformatics. They developed the infrastructures to provide maximum facility to the scientific community to gain maximum output in this field. This article renders bioinformatics, measures, and its importance of implementation in South Asia with proper ways of improving bioinformatics education flaws. It also addresses the problems faced in South Asia and proposes some recommendations regarding bioinformatics education. The information regarding bioinformatics education and institutes was collected from different existing research papers, databases, and surveys. The information was then confirmed by visiting each institution’s website, while problems and solutions displayed in the article are mostly in line with South Asian bioinformatics conferences and institutions’ objectives. Among South Asian countries, India and Pakistan have developed infrastructure and education regarding bioinformatics rapidly as compared to other countries, whereas Bangladesh, Sri Lanka, and Nepal are still in a progressing phase in this field. To advance in a different sector, the bioinformatics industry has to be revolutionized, and it will contribute to strengthening the pharmaceutical, agricultural, and molecular sectors in South Asia. To advance in bioinformatics, universities’ infrastructure needs to be on a par with the current international standards, which will produce well-trained professionals with skills in multiple fields like biotechnology, mathematics, statistics, and computer science. The bioinformatics industry has revolutionized and strengthened the pharmaceutical, agricultural, and molecular sectors in South Asia, and it will serve as the standard of education increases in the South Asian countries. A framework for developing a centralized database is suggested after the literature review to collect and store the information on the current status of South Asian bioinformatics education. This will be named as the South Asian Bioinformatics Education Database (SABE). This will provide comprehensive information regarding the bioinformatics in South Asian countries by the country name, the experts of this field, and the university name to explore the top-ranked outputs relevant to queries.
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16

Covitz, P. A., F. Hartel, C. Schaefer, et al. "caCORE: A common infrastructure for cancer informatics." Bioinformatics 19, no. 18 (2003): 2404–12. http://dx.doi.org/10.1093/bioinformatics/btg335.

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17

Stanislaus, R., C. Chen, J. Franklin, J. Arthur, and J. S. Almeida. "AGML Central: web based gel proteomic infrastructure." Bioinformatics 21, no. 9 (2005): 1754–57. http://dx.doi.org/10.1093/bioinformatics/bti246.

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18

Zerbino, D. R., B. Aken, L. Clarke, F. Cunningham, A. D. Yates, and P. Flicek. "P1035 Ensembl: A comprehensive bioinformatics infrastructure for vertebrate genetics." Journal of Animal Science 94, suppl_4 (2016): 31–32. http://dx.doi.org/10.2527/jas2016.94supplement431b.

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19

Tusch, Guenter, Paul Leidig, Greg Wolffe, David Elrod, and Carl Strebel. "Technology infrastructure supporting a medical & bioinformatics masters degree." ACM SIGCSE Bulletin 36, no. 3 (2004): 264. http://dx.doi.org/10.1145/1026487.1008097.

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20

Oliva, A., E. Pinnow, R. Levin, and K. Uhl. "Improving Women's Health Through Modernization of Our Bioinformatics Infrastructure." Clinical Pharmacology & Therapeutics 83, no. 1 (2007): 192–95. http://dx.doi.org/10.1038/sj.clpt.6100437.

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21

Vamathevan, Jessica, Rolf Apweiler, and Ewan Birney. "Biomolecular Data Resources: Bioinformatics Infrastructure for Biomedical Data Science." Annual Review of Biomedical Data Science 2, no. 1 (2019): 199–222. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021321.

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Technological advances have continuously driven the generation of bio-molecular data and the development of bioinformatics infrastructure, which enables data reuse for scientific discovery. Several types of data management resources have arisen, such as data deposition databases, added-value databases or knowledgebases, and biology-driven portals. In this review, we provide a unique overview of the gradual evolution of these resources and discuss the goals and features that must be considered in their development. With the increasing application of genomics in the health care context and with 60 to 500 million whole genomes estimated to be sequenced by 2022, biomedical research infrastructure is transforming, too. Systems for federated access, portable tools, provision of reference data, and interpretation tools will enable researchers to derive maximal benefits from these data. Collaboration, coordination, and sustainability of data resources are key to ensure that biomedical knowledge management can scale with technology shifts and growing data volumes.
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22

Giacomoni, F., G. Le Corguille, M. Monsoor, et al. "Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics." Bioinformatics 31, no. 9 (2014): 1493–95. http://dx.doi.org/10.1093/bioinformatics/btu813.

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23

Hendrickx, Diana M., Hugo J. W. L. Aerts, Florian Caiment, et al. "diXa: a data infrastructure for chemical safety assessment." Bioinformatics 31, no. 9 (2014): 1505–7. http://dx.doi.org/10.1093/bioinformatics/btu827.

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24

Cook, Charles E., Rodrigo Lopez, Oana Stroe, et al. "The European Bioinformatics Institute in 2018: tools, infrastructure and training." Nucleic Acids Research 47, no. D1 (2018): D15—D22. http://dx.doi.org/10.1093/nar/gky1124.

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25

Jianlin Cheng, L. Scharenbroich, P. Baldi, and E. Mjolsness. "Sigmoid: A Software Infrastructure for Pathway Bioinformatics and Systems Biology." IEEE Intelligent Systems 20, no. 3 (2005): 68–75. http://dx.doi.org/10.1109/mis.2005.51.

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26

Drysdale, Rachel, Charles E. Cook, Robert Petryszak, et al. "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences." Bioinformatics 36, no. 8 (2020): 2636–42. http://dx.doi.org/10.1093/bioinformatics/btz959.

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27

Pühler, A. "Bioinformatics solutions for big data analysis in life sciences presented by the German network for bioinformatics infrastructure." Journal of Biotechnology 261 (November 2017): 1. http://dx.doi.org/10.1016/j.jbiotec.2017.08.025.

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28

Beck, D., M. Settles, and J. A. Foster. "OTUbase: an R infrastructure package for operational taxonomic unit data." Bioinformatics 27, no. 12 (2011): 1700–1701. http://dx.doi.org/10.1093/bioinformatics/btr196.

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29

Hosburgh, Nathan. "Developing a Bioinformatics Program and Supporting Infrastructure in a Biomedical Library." Journal of eScience Librarianship 7, no. 2 (2018): e1129. http://dx.doi.org/10.7191/jeslib.2018.1129.

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30

Tusch, Guenter, Paul Leidig, Gregory Wolffe, David Elrod, and Carl Strebel. "Technology infrastructure in support of a medical & bioinformatics masters degree." ACM SIGCSE Bulletin 41, no. 3 (2009): 388. http://dx.doi.org/10.1145/1595496.1563030.

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31

Larsen, Tessa, Gaylene Medlam, Julia Giovinazzo, Dennis VergeldeDios, and Glenn Jones. "A Systematic Bioinformatics Infrastructure to Support Advances in Professional MRTT Practice." Journal of Medical Imaging and Radiation Sciences 47, no. 1 (2016): S20. http://dx.doi.org/10.1016/j.jmir.2015.12.062.

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32

Gong, Xiujun, Kensuke Nakamura, Hua Yu, Kei Yura, and Nobuhiro Go. "BAAQ: An Infrastructure for Application Integration and Knowledge Discovery in Bioinformatics." IEEE Transactions on Information Technology in Biomedicine 11, no. 4 (2007): 428–34. http://dx.doi.org/10.1109/titb.2006.888700.

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33

Antonio, B. A., N. Namiki, T. Matsumoto, and T. Sasaki. "Rice Bioinformatics: From Sequences to Applications." Asia-Pacific Biotech News 06, no. 24 (2002): 914–20. http://dx.doi.org/10.1142/s0219030302001908.

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Rice genomics data are rapidly expanding in number and complexity. This is further intensified as the international collaboration aimed at sequencing the entire genome accelerates the release of sequence data to the public domain. An overview of available resources for processing, elucidating and propagating genomic information in rice is described here. As the genome sequencing nears its completion, the future challenges in rice bioinformatics lies in developing an informatics infrastructure that would facilitate integration across various data types and sources, and eventually lead to the development of viable strategies for improvement of rice and other major cereal crops.
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34

Wimalaratne, S. M., P. Grenon, R. Hoehndorf, G. V. Gkoutos, and B. de Bono. "An infrastructure for ontology-based information systems in biomedicine: RICORDO case study." Bioinformatics 28, no. 3 (2011): 448–50. http://dx.doi.org/10.1093/bioinformatics/btr662.

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35

Adams, Richard, Allan Clark, Azusa Yamaguchi, et al. "SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology." Bioinformatics 29, no. 5 (2013): 664–65. http://dx.doi.org/10.1093/bioinformatics/btt023.

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36

Cohen, Jeremy, Ioannis Filippis, Mark Woodbridge, et al. "RAPPORT: running scientific high-performance computing applications on the cloud." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1983 (2013): 20120073. http://dx.doi.org/10.1098/rsta.2012.0073.

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Cloud computing infrastructure is now widely used in many domains, but one area where there has been more limited adoption is research computing, in particular for running scientific high-performance computing (HPC) software. The Robust Application Porting for HPC in the Cloud (RAPPORT) project took advantage of existing links between computing researchers and application scientists in the fields of bioinformatics, high-energy physics (HEP) and digital humanities, to investigate running a set of scientific HPC applications from these domains on cloud infrastructure. In this paper, we focus on the bioinformatics and HEP domains, describing the applications and target cloud platforms. We conclude that, while there are many factors that need consideration, there is no fundamental impediment to the use of cloud infrastructure for running many types of HPC applications and, in some cases, there is potential for researchers to benefit significantly from the flexibility offered by cloud platforms.
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37

Salgado, David, Irina M. Armean, Michael Baudis, et al. "The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research." F1000Research 9 (October 13, 2020): 1229. http://dx.doi.org/10.12688/f1000research.24887.1.

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Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While “High-Throughput” sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR’s recently established human CNV Community, with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context.
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38

Kim, Jihoon, Eric Levy, Alex Ferbrache, et al. "MAGI: a Node.js web service for fast microRNA-Seq analysis in a GPU infrastructure." Bioinformatics 30, no. 19 (2014): 2826–27. http://dx.doi.org/10.1093/bioinformatics/btu377.

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39

Ras, Verena, Gerrit Botha, Shaun Aron, et al. "Using a multiple-delivery-mode training approach to develop local capacity and infrastructure for advanced bioinformatics in Africa." PLOS Computational Biology 17, no. 2 (2021): e1008640. http://dx.doi.org/10.1371/journal.pcbi.1008640.

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With more microbiome studies being conducted by African-based research groups, there is an increasing demand for knowledge and skills in the design and analysis of microbiome studies and data. However, high-quality bioinformatics courses are often impeded by differences in computational environments, complicated software stacks, numerous dependencies, and versions of bioinformatics tools along with a lack of local computational infrastructure and expertise. To address this, H3ABioNet developed a 16S rRNA Microbiome Intermediate Bioinformatics Training course, extending its remote classroom model. The course was developed alongside experienced microbiome researchers, bioinformaticians, and systems administrators, who identified key topics to address. Development of containerised workflows has previously been undertaken by H3ABioNet, and Singularity containers were used here to enable the deployment of a standard replicable software stack across different hosting sites. The pilot ran successfully in 2019 across 23 sites registered in 11 African countries, with more than 200 participants formally enrolled and 106 volunteer staff for onsite support. The pulling, running, and testing of the containers, software, and analyses on various clusters were performed prior to the start of the course by hosting classrooms. The containers allowed the replication of analyses and results across all participating classrooms running a cluster and remained available posttraining ensuring analyses could be repeated on real data. Participants thus received the opportunity to analyse their own data, while local staff were trained and supported by experienced experts, increasing local capacity for ongoing research support. This provides a model for delivering topic-specific bioinformatics courses across Africa and other remote/low-resourced regions which overcomes barriers such as inadequate infrastructures, geographical distance, and access to expertise and educational materials.
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40

Jackman, Shaun D., Tatyana Mozgacheva, Susie Chen, et al. "ORCA: a comprehensive bioinformatics container environment for education and research." Bioinformatics 35, no. 21 (2019): 4448–50. http://dx.doi.org/10.1093/bioinformatics/btz278.

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Abstract Summary The ORCA bioinformatics environment is a Docker image that contains hundreds of bioinformatics tools and their dependencies. The ORCA image and accompanying server infrastructure provide a comprehensive bioinformatics environment for education and research. The ORCA environment on a server is implemented using Docker containers, but without requiring users to interact directly with Docker, suitable for novices who may not yet have familiarity with managing containers. ORCA has been used successfully to provide a private bioinformatics environment to external collaborators at a large genome institute, for teaching an undergraduate class on bioinformatics targeted at biologists, and to provide a ready-to-go bioinformatics suite for a hackathon. Using ORCA eliminates time that would be spent debugging software installation issues, so that time may be better spent on education and research. Availability and implementation The ORCA Docker image is available at https://hub.docker.com/r/bcgsc/orca/. The source code of ORCA is available at https://github.com/bcgsc/orca under the MIT license.
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41

Wibberg, Daniel, Bérénice Batut, Peter Belmann, et al. "The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR." F1000Research 8 (September 11, 2020): 1877. http://dx.doi.org/10.12688/f1000research.20244.2.

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The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.
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42

Cárdenas-García, Maura, Pedro P. González-Pérez, and Sara Montagna. "Simulation of caspases apoptotic signalling pathway in a tuple space-based bioinformatics infrastructure." EMBnet.journal 18, B (2012): 94. http://dx.doi.org/10.14806/ej.18.b.563.

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43

Irina, Potapova. "State Strategy of Russian Universities and Technological Business Companies for the Transfer of Bioinformatics Knowledge." International Journal of Applied Research in Bioinformatics 9, no. 2 (2019): 50–56. http://dx.doi.org/10.4018/ijarb.2019070105.

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This article presents examples of optimization of IT-infrastructure. Issues of information security at the time of digital economy are discussed. The article lists modern trends of optimization at IT-infrastructure use of bioinformatics knowledge. Examples of cloud services and the use of hybrid clouds are given. The information system of the future is defined. There is an integration of the existing system into a single digital platform and a description is given of ensuring the work of the social bloc of the country. The author defines the software visualizer, which opens unlimited possibilities for manipulating disk resources.
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44

Chen, Chuming, Peter B. McGarvey, Hongzhan Huang, and Cathy H. Wu. "Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput “omics” Data." Advances in Bioinformatics 2010 (March 29, 2010): 1–19. http://dx.doi.org/10.1155/2010/423589.

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High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge.
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45

Ahmed, Azza E., Ayah A. Awadallah, Mawada Tagelsir, et al. "Delivering blended bioinformatics training in resource-limited settings: a case study on the University of Khartoum H3ABioNet node." Briefings in Bioinformatics 21, no. 2 (2019): 719–28. http://dx.doi.org/10.1093/bib/bbz004.

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Abstract Motivation Delivering high-quality distance-based courses in resource-limited settings is a challenging task. Besides the needed infrastructure and expertise, effective delivery of a bioinformatics course could benefit from hands-on sessions, interactivity and problem-based learning approaches. Results In this article, we discuss the challenges and best practices in delivering bioinformatics training in resource-limited settings taking the example of hosting and running a multiple-delivery online course, Introduction to Bioinformatics, that was developed by the H3ABioNet Education and Training working group and delivered in 27 remote classrooms across Africa in 2017. We take the case of the University of Khartoum classrooms. Believing that our local setting is similar to others in less-developed countries, we also reflect upon aspects like classroom environment and recruitment of students to maximize outcomes.
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46

Mabvakure, Batsirai M., Raymond Rott, Leslie Dobrowsky, et al. "Advancing HIV Vaccine Research With Low-Cost High-Performance Computing Infrastructure: An Alternative Approach for Resource-Limited Settings." Bioinformatics and Biology Insights 13 (January 2019): 117793221988234. http://dx.doi.org/10.1177/1177932219882347.

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Next-generation sequencing (NGS) technologies have revolutionized biological research by generating genomic data that were once unaffordable by traditional first-generation sequencing technologies. These sequencing methodologies provide an opportunity for in-depth analyses of host and pathogen genomes as they are able to sequence millions of templates at a time. However, these large datasets can only be efficiently explored using bioinformatics analyses requiring huge data storage and computational resources adapted for high-performance processing. High-performance computing allows for efficient handling of large data and tasks that may require multi-threading and prolonged computational times, which is not feasible with ordinary computers. However, high-performance computing resources are costly and therefore not always readily available in low-income settings. We describe the establishment of an affordable high-performance computing bioinformatics cluster consisting of 3 nodes, constructed using ordinary desktop computers and open-source software including Linux Fedora, SLURM Workload Manager, and the Conda package manager. For the analysis of large antibody sequence datasets and for complex viral phylodynamic analyses, the cluster out-performed desktop computers. This has demonstrated that it is possible to construct high-performance computing capacity capable of analyzing large NGS data from relatively low-cost hardware and entirely free (open-source) software, even in resource-limited settings. Such a cluster design has broad utility beyond bioinformatics to other studies that require high-performance computing.
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47

Wibberg, Daniel, Bérénice Batut, Peter Belmann, et al. "The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR." F1000Research 8 (November 7, 2019): 1877. http://dx.doi.org/10.12688/f1000research.20244.1.

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The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR network. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 250 training courses were carried out with more than 5,200 participants and these courses received recommendation rates of almost 90% (status as of October 2019). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.
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48

Harris, Nomi L., Peter J. A. Cock, Christopher J. Fields, et al. "BOSC 2019, the 20th annual Bioinformatics Open Source Conference." F1000Research 8 (December 20, 2019): 2132. http://dx.doi.org/10.12688/f1000research.21568.1.

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The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on “Building infrastructure for responsible open science in Africa”. Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases.
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49

Rohde, Palle Duun, Izel Fourie Sørensen, and Peter Sørensen. "qgg: an R package for large-scale quantitative genetic analyses." Bioinformatics 36, no. 8 (2019): 2614–15. http://dx.doi.org/10.1093/bioinformatics/btz955.

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Abstract Summary Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, and performing single and multiple marker association analyses and genomic-based predictions of phenotypes. Availability and implementation The qgg package is freely available. For the latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg. The current release is available from CRAN (https://CRAN.R-project.org/package=qgg) for all major operating systems. Supplementary information Supplementary data are available at Bioinformatics online.
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50

Dumont, Emmanuel L. P., Benjamin Tycko, and Catherine Do. "CloudASM: an ultra-efficient cloud-based pipeline for mapping allele-specific DNA methylation." Bioinformatics 36, no. 11 (2020): 3558–60. http://dx.doi.org/10.1093/bioinformatics/btaa149.

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Abstract Summary Methods for quantifying the imbalance in CpG methylation between alleles genome-wide have been described but their algorithmic time complexity is quadratic and their practical use requires painstaking attention to infrastructure choice, implementation and execution. To solve this problem, we developed CloudASM, a scalable, ultra-efficient, turn-key, portable pipeline on Google Cloud Platform (GCP) that uses a novel pipeline manager and GCP’s serverless enterprise data warehouse. Availability and implementation CloudASM is freely available in the GitHub repository https://github.com/TyckoLab/CloudASM and a sample dataset and its results are also freely available at https://console.cloud.google.com/storage/browser/cloudasm. Contact emmanuel.dumont@hmh-cdi.org
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