An educational guide for nanopore sequencing in the classroom.

The last decade has witnessed a remarkable increase in our ability to measure genetic information. Advancements of sequencing technologies are challenging the existing methods of data storage and analysis. While methods to cope with the data deluge are progressing, many biologists have lagged behind due to the fast pace of computational advancements and tools available to address their scientific questions. Future generations of biologists must be more computationally aware and capable. This means they should be trained to give them the computational skills to keep pace with technological developments. Here, we propose a model that bridges experimental and bioinformatics concepts using the Oxford Nanopore Technologies (ONT) sequencing platform. We provide both a guide to begin to empower the new generation of educators, scientists, and students in performing long-read assembly of bacterial and bacteriophage genomes and a standalone virtual machine containing all the required software and learning materials for the course.

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PID https://www.doi.org/10.1371/journal.pcbi.1007314
PID pmc:PMC6977714
PID pmid:31971941
PID urn:urn:NBN:nl:ui:24-uuid:bf190e26-4afd-4f77-9e96-a922207ecdd5
URL https://doaj.org/toc/1553-734X
URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6977714
URL https://dblp.uni-trier.de/db/journals/ploscb/ploscb16.html#SalazarNAACHHMM20
URL https://doaj.org/toc/1553-7358
URL http://resolver.tudelft.nl/uuid:bf190e26-4afd-4f77-9e96-a922207ecdd5
URL https://doi.org/10.1371/journal.pcbi.1007314
URL https://academic.microsoft.com/#/detail/3001938412
URL https://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1007314
URL http://europepmc.org/articles/PMC6977714
URL https://eprints.soton.ac.uk/443064/
URL https://dx.plos.org/10.1371/journal.pcbi.1007314
URL https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007314&type=printable
URL http://dx.doi.org/10.1371/journal.pcbi.1007314
URL https://www.narcis.nl/publication/RecordID/oai%3Atudelft.nl%3Auuid%3Abf190e26-4afd-4f77-9e96-a922207ecdd5
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Access Right Open Access
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Author Alex N. Salazar, 0000-0002-8453-8769
Author Franklin L. Nobrega, 0000-0002-8238-1083
Author Christine Anyansi, 0000-0003-4208-0697
Author Cristian Aparicio-Maldonado
Author Ana Rita Costa
Author Anna C. Haagsma
Author Anwar Hiralal, 0000-0003-3797-6616
Author Ahmed Mahfouz, 0000-0001-8601-2149
Author Rebecca E. McKenzie
Author Teunke van Rossum, 0000-0002-5219-1799
Author Stan J. J. Brouns
Author Thomas Abeel, 0000-0002-7205-7431
Contributor Ouellette, Francis
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Collected From PubMed Central; ORCID; Datacite; e-Prints Soton; UnpayWall; DOAJ-Articles; NARCIS; Crossref; Microsoft Academic Graph
Hosted By Europe PubMed Central; e-Prints Soton; TU Delft Repository; NARCIS; PLoS Computational Biology
Journal PLoS Computational Biology, ,
Publication Date 2020-01-23
Publisher Public Library of Science (PLoS)
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Country Netherlands; United Kingdom
Description Author summary Genomes contain all the information required for an organism to function. Understanding the genome sequence is often the key to answer important biological questions. For example, the sequences of human genomes are used for diagnosis of genetic disorders or for the development of personalized treatments, while the sequences of microbes may inform about their mechanisms of infection and guide the development of novel drugs. Today, our capacity to generate genome sequencing data is tremendous. However, our capacity to process this information is insufficient. This is partially due to limitations of current methods for data analysis but is mostly caused by lack of training for most biologists to leverage high-throughput sequencing data and use their full potential. It is urgent that we train the new generations of biologists to become computationally aware and able to keep pace with technological developments in the field. In this manuscript, we illustrate our efforts in adopting an integrated teaching model that bridges experimental and bioinformatics works. Our course integrates data generation in the lab with bioinformatics work to illustrate the interlinking of lab practices and downstream effects. In our demonstration course, we used nanopore sequencing to train nanobiology students, but the model is easily customizable to suit students of different educational backgrounds or alternative technologies. The tools we provide help not only science educators but also biologists to address many relevant questions in biology.
Format application/pdf; text
Language English
Resource Type Other literature type; Article
keyword keywords.Ecology, Evolution, Behavior and Systematics
system:type publication
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::fb13509114460e69dab00123265c69e0
Author jsonws_user
Last Updated 25 December 2020, 17:02 (CET)
Created 25 December 2020, 17:02 (CET)