An educational guide for Nanopore sequencing in the classroom
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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Luzia De Nobrega, Franklin
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Salazar, A.N.
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Anyansi, C.
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Aparicio-Maldonado, Cristian
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Costa, Ana Rita
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Haagsma, Anna
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Hiraral, Anwar
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Mahfouz, A
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McKenzie, Rebecca
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van Rossum, Teunke
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Brouns, Stan J.J.
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Abeel, Thomas
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23 January 2020
Luzia De Nobrega, Franklin
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Salazar, A.N.
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Anyansi, C.
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Aparicio-Maldonado, Cristian
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Costa, Ana Rita
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Haagsma, Anna
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Hiraral, Anwar
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Mahfouz, A
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McKenzie, Rebecca
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van Rossum, Teunke
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Brouns, Stan J.J.
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Abeel, Thomas
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Luzia De Nobrega, Franklin, Salazar, A.N., Anyansi, C., Aparicio-Maldonado, Cristian, Costa, Ana Rita, Haagsma, Anna, Hiraral, Anwar, Mahfouz, A, McKenzie, Rebecca, van Rossum, Teunke, Brouns, Stan J.J. and Abeel, Thomas
(2020)
An educational guide for Nanopore sequencing in the classroom.
PLoS Computational Biology, 16 (1), , [e1007314].
(doi:10.1371/journal.pcbi.1007314).
Abstract
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.
Text
journal.pcbi.1007314
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More information
Accepted/In Press date: 8 October 2019
Published date: 23 January 2020
Additional Information:
Funding Information:
ANS is supported by a grant from the BE-Basic Foundation related to FES funds from the Dutch Ministry of Economic Affairs. FLN is supported by the Netherlands Organization for Scientific Research (NWO) Veni grant 016. Veni.181.092. REM is supported by an NWO Frontiers of Nanoscience (NanoFront) grant. SJJB is supported by European Research Council (ERC) Stg grant 639707 and NWO Vici grant. Oxford Nanopore Technologies provided some consumables for the course. Funders had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2020 Salazar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Local EPrints ID: 443064
URI: http://eprints.soton.ac.uk/id/eprint/443064
ISSN: 1553-734X
PURE UUID: 71c286a5-0f3c-4eb1-89cd-a0a945bd32a0
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Date deposited: 07 Aug 2020 16:36
Last modified: 17 Mar 2024 04:02
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Contributors
Author:
A.N. Salazar
Author:
C. Anyansi
Author:
Cristian Aparicio-Maldonado
Author:
Ana Rita Costa
Author:
Anna Haagsma
Author:
Anwar Hiraral
Author:
A Mahfouz
Author:
Rebecca McKenzie
Author:
Teunke van Rossum
Author:
Stan J.J. Brouns
Author:
Thomas Abeel
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