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Fungal gene sequences make excellent models for teaching data mining

Published online by Cambridge University Press:  23 September 2004

PAUL HOOLEY ,
ALAN BURNS  and
MICHAEL WHITEHEAD
PAUL HOOLEY
Affiliation:
School of Applied Sciences, University of Wolverhampton, Wulfruna St., Wolverhampton, West Midlands. WV1 1SB. UK. Email P.Hooley@wlv.ac.uk, Fax +44 01902 322714
ALAN BURNS
Affiliation:
School of Applied Sciences, University of Wolverhampton, Wulfruna St., Wolverhampton, West Midlands. WV1 1SB. UK. Email P.Hooley@wlv.ac.uk, Fax +44 01902 322714
MICHAEL WHITEHEAD
Affiliation:
School of Applied Sciences, University of Wolverhampton, Wulfruna St., Wolverhampton, West Midlands. WV1 1SB. UK. Email P.Hooley@wlv.ac.uk, Fax +44 01902 322714
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Abstract

A brief introductory exercise in the use of on-line databases to examine fungal genes and their products is described. Fungal genes make particularly good teaching models owing to their relatively simple eukaryotic structure and wide range of homologues in higher organisms including humans. An evaluation of students' reactions to the exercise is included.

Keywords

Data miningBLASTFungal genes
Type
Original Article
Information
Mycologist , Volume 18 , Issue 3 , August 2004 , pp. 118 - 124
Copyright
© 2004 Cambridge University Press

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