[Rd] new book

[A.J. Rossini]

>>>>> "MP" == Martyn Plummer <plummer@iarc.fr> writes:

    MP> This came up a couple of weeks ago on Slashdot (which I should
    MP> really stop reading).  I got the impression from the ensuing
    MP> discussion that it was targetted at biologists,but if you
    MP> found it useful I shall pick up a copy.  In the same
    MP> discussion, the monograph "Biological sequence analysis:
    MP> probabilistic methods of proteins and nucleic acids" by Durbin
    MP> (ed) was highly recommended by some people.  Do you have this?

    MP> Here is a link to the O'Reilly site which contains contents,
    MP> and a sample chapter.

    MP> http://www.oreilly.com/catalog/bioskills/

    MP> I remember buying an O'Reilly book called "The whole internet:
    MP> a users guide and catalogue". I wonder if this one will have
    MP> an even shorter shelf life.

I commented privately, but since this comes up, I'll comment again: if
you know your way around genetic programs, perl and bioperl, and
understand general scripting tools, and have a smattering of
informatics knowledge, the book isn't too useful.  I've got a
protein-folding friend who plans on using it to teach a comp bio
course in microbiology; he considers the skills mentioned in the book
to be the bare-minimum for that, though it's short on theory.

The Durbin book is a nice intro to current state of the art DNA and
protein sequencing methods, but not much more (of course, it lives up
to its title).  That being said, I found it incredibly useful for
seeing what has been done recently.  YMMV.

I think part of the problem, and this is seen by those who recommend
the Durbin book, is that there are a number of different ways of
looking at the "bioinformatics" problem.  Problematically, you've got
something like:

A. Large quantities of data, and handling, storage, backup
B. Annotations and external (data) linkages
C. Computational Biology
D. Statistical issues (experimental design/analysis and exploratory
   approaches; 
E. Combinations of the above two, including such topics (one of many
   examples): stochastic DE modelling using mixed effects models.

and scientifically, you've got one or more of:

1. sequencing (DNA and/or protein)
2. protein structure (secondary/tertiary, based on the above)
3. gene expression arrays of many forms (cDNA, membrane, gold,
   oligios). 
4. SNPs 
5. genetics and evolutionary biology

and then there is the application area (cancer, HIV, other chronic
diseases; comparative studies and animal/plant/non-human organism
models, etc).

The classifications that I use are even up for grabs, depending on who
you are and who you talk with; it's just 2 possible views of this high
buzzword content area, sigh.  I know I've left out a few areas, here.

best,
-tony

-- 
A.J. Rossini				Rsrch. Asst. Prof. of Biostatistics
U. of Washington Biostatistics		rossini@u.washington.edu	
FHCRC/SCHARP/HIV Vaccine Trials Net	rossini@scharp.org
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