4273π: bioinformatics education on low cost ARM hardware

Daniel Barker; David Ellard Keith Ferrier; Peter W.H. Holland; John B. O. Mitchell; Heleen Plaisier; Michael Gordon Ritchie; Steven D. Smart

doi:10.1186/1471-2105-14-243

4273π: bioinformatics education on low cost ARM hardware

Daniel Barker, David Ellard Keith Ferrier, Peter W.H. Holland, John B. O. Mitchell, Heleen Plaisier, Michael Gordon Ritchie, Steven D. Smart

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

6 Downloads (Pure)

Abstract

Background: Teaching bioinformatics at universities is complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. For a future
career in biology or bioinformatics, the installation of software is a useful skill. We propose that this may be taught by running the course on GNU/Linux running on inexpensive Raspberry Pi computer hardware, for which students
may be granted full administrator access. Results: We release 4273π, an operating system image for Raspberry Pi based on Raspbian Linux. This includes minor customisations for classroom use and includes our Open Access bioinformatics course, 4273π Bioinformatics for Biologists. This is based on the final-year undergraduate module BL4273, run on Raspberry Pi computers at the University of St Andrews, Semester 1, academic year 2012–2013. Conclusions: 4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.

Original language	English
Article number	243
Number of pages	6
Journal	BMC Bioinformatics
Volume	14
Early online date	12 Aug 2013
DOIs	https://doi.org/10.1186/1471-2105-14-243
Publication status	Published - 12 Aug 2013

Keywords

Bioinformatics education
Teaching material
Raspberry Pi
Linux

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10.1186/1471-2105-14-243

barker243
© 2013 Barker et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Final published version, 1.42 MB

Machine Learning Approaches to Predict: Machine Learning Approaches to Predict Enzyme Function
Mitchell, J. B. O. (PI) & De Ferrari, L. (Researcher)
BBSRC
1/09/11 → 31/12/14
Project: Standard

Cite this

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title = "4273π: bioinformatics education on low cost ARM hardware",

abstract = "Background: Teaching bioinformatics at universities is complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. For a future career in biology or bioinformatics, the installation of software is a useful skill. We propose that this may be taught by running the course on GNU/Linux running on inexpensive Raspberry Pi computer hardware, for which students may be granted full administrator access. Results: We release 4273π, an operating system image for Raspberry Pi based on Raspbian Linux. This includes minor customisations for classroom use and includes our Open Access bioinformatics course, 4273π Bioinformatics for Biologists. This is based on the final-year undergraduate module BL4273, run on Raspberry Pi computers at the University of St Andrews, Semester 1, academic year 2012–2013. Conclusions: 4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.",

keywords = "Bioinformatics education, Teaching material, Raspberry Pi, Linux",

author = "Daniel Barker and Ferrier, {David Ellard Keith} and Holland, {Peter W.H.} and Mitchell, {John B. O.} and Heleen Plaisier and Ritchie, {Michael Gordon} and Smart, {Steven D.}",

year = "2013",

month = aug,

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doi = "10.1186/1471-2105-14-243",

language = "English",

volume = "14",

journal = "BMC Bioinformatics",

issn = "1471-2105",

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AU - Barker, Daniel

AU - Ferrier, David Ellard Keith

AU - Holland, Peter W.H.

AU - Mitchell, John B. O.

AU - Plaisier, Heleen

AU - Ritchie, Michael Gordon

AU - Smart, Steven D.

PY - 2013/8/12

Y1 - 2013/8/12

N2 - Background: Teaching bioinformatics at universities is complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. For a future career in biology or bioinformatics, the installation of software is a useful skill. We propose that this may be taught by running the course on GNU/Linux running on inexpensive Raspberry Pi computer hardware, for which students may be granted full administrator access. Results: We release 4273π, an operating system image for Raspberry Pi based on Raspbian Linux. This includes minor customisations for classroom use and includes our Open Access bioinformatics course, 4273π Bioinformatics for Biologists. This is based on the final-year undergraduate module BL4273, run on Raspberry Pi computers at the University of St Andrews, Semester 1, academic year 2012–2013. Conclusions: 4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.

AB - Background: Teaching bioinformatics at universities is complicated by typical computer classroom settings. As well as running software locally and online, students should gain experience of systems administration. For a future career in biology or bioinformatics, the installation of software is a useful skill. We propose that this may be taught by running the course on GNU/Linux running on inexpensive Raspberry Pi computer hardware, for which students may be granted full administrator access. Results: We release 4273π, an operating system image for Raspberry Pi based on Raspbian Linux. This includes minor customisations for classroom use and includes our Open Access bioinformatics course, 4273π Bioinformatics for Biologists. This is based on the final-year undergraduate module BL4273, run on Raspberry Pi computers at the University of St Andrews, Semester 1, academic year 2012–2013. Conclusions: 4273π is a means to teach bioinformatics, including systems administration tasks, to undergraduates at low cost.

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4273π: bioinformatics education on low cost ARM hardware

Abstract

Keywords

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Machine Learning Approaches to Predict: Machine Learning Approaches to Predict Enzyme Function

Cite this