[BioEdu] Suggestions for a bioinformatics course

[Michael Sadowski]

Hi everyone, 

I've been teaching on a short (5 day) introductory
course for a couple of years now. The diversity of
backgrounds of the students is quite challenging to
deal with in an educational setting. 

I think the solution to this problem is to be strong
in setting your theme: have a message that people from
any background will be able to understand and teach
it. Then allow the students to pursue the interests
they bring from their own disciplines in their
coursework. This should work well with bioinformatics
since the material is necessarily interdisciplinary. 

If you choose the right topics then there should be a
balance of material that individual students can
choose to follow up on as suits their own interests.
But the ultimate emphasis should be on the biological
problems that the tools and resources are there to
solve (or assist with) and how they do it, since that
is the principal focus of the field itself. The aim
therefore would be to teach that perspective, rather
than necessarily trying to cover all the areas that
bioinformatics touches on (which are manifold!)

I would suggest teaching something about the major
area in which bioinformatics has made progress:
sequence similarity searches. That has a good balance
for different students: the basic problem is
biological and touches on interesting areas of
phylogenetics and molecular evolution. The problems of
database searching and their solution provide
interesting examples of the complexity of organisms
such as domains, low-complexity regions etc. 

The statistics of the search are a deep topic which
should provide information to satisfy the more
mathematically inclined, and there is now a great deal
of literature on the various tricks that have been
used to make searching tractable in reasonable time
and to overcome the problems of estimating parameters
for the statistics. 

This is just one example, but a nice one because it is
one of the most developed areas of the field and sits
nicely between different disciplines. It also has many
possible extensions into related areas - protein
structure prediction and homology modeling,
genome-genome comparisons, advanced profile searches, 
ab initio gene prediction. 

You could then suggest a list of topics relating to
the starting point which students would independently
research: HMMs, statistics of searches, refinements to
the BLAST algorithm etc. 

Michael.

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Date: Wed, 9 Aug 2006 13:53:43 -0400
From: Sudhindra.Gadagkar at notes.udayton.edu
Subject: [BioEdu] Suggestions for a bioinformatics
course
To: bioedu at bioinformatics.org
Message-ID:
	
<OF9188489A.66327853-ON852571C5.00610939-852571C5.00627ECE at notes.udayton.edu>
	
Content-Type: text/plain; charset="us-ascii"

Hello all,

First, thanks to Jeff for this group.  Very timely,
for me at least. I 
am 
a biologist and am teaching an intro bioinformatics
course this fall.  
The 
students will be a mix of biology, premed and Computer
Science 
students. I 
have a number of textbooks with me but am really not
happy with any of 
them, and so am planning on just giving handouts.  I
would like to hear 
of 
experiences of people who have used any particular
book, and also of 
those 
who have not used any one in particular.  Also (this
concept is still 
evolving), to deal with heterogeneity in the
backgrounds of my 
students, I 
am thinking of separate projects that will exploit
their respective 
training and keep them interested.  Any suggestions
for project topics 
and/or alternative ways of dealing with the
heterogeneity?  These would 
be 
upper-class students (typically seniors).  It is a
dual-listed course, 
and 
so I have grad students as well!

Actually, I hope this email will start a thread of
fruitful discussion. 
Views of students are also most welcome!

Sudhindra Gadagkar

----------------------------------------------------------------------------
Sudhindra R. Gadagkar, Ph.D.
Department of Biology
University of Dayton
300 College Park
Dayton, OH 45469-2320

Ph: (937) 229-2410
Fax: (937) 229-2021
Email: gadagkar at notes.udayton.edu
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