gribskov@FCRFV1.NCIFCRF.GOV ("Gribskov, Michael") (04/04/91)
Please excuse my previous empty message which was not intended as a droll comment: clemens suter-crazzolara <suter2@urz.unibas.ch> writes: > ... if i have a protein, and i need to get the sequence, >i tend to leave the sequencing to a specialist, rather then to get >involved in the chemistry myself. this is true for a lot of different >methods related to biology (overexpression, even production, cell- >culture, somitimes even actual cloning). we tend to give a lot >of jobs away. now, if i had to go to courses on all these subjects, >including computing, and had to get involved in each region of >interest, there wouldn`t be much time left for experiments. > >what i want to bring across is the general idea that we shouldn`t >expect everybody to get to deeply involved in computing (as we >also shouldn`t when it comes to protein sequencing). This is one of the core questions: just how much computing does a molecular biologist need to know? The point is well made that knowing how to operate a computer is quited different from knowing something about computation, just as knowing something about protein sequencing is different from knowing how to operate a protein sequenator. My perception is that, currently, broad conclusions about the function, evolution and structure of macromolecules are being made based on sequence similarity and inferred homology. Few graduate programs provide the training to critically evaluate such claims, and many people are left to either take the claim on faith or to reject it as unimportant. Similar points can be made about many other computationally intensive aspects of molecular biology such as predicted structures of RNA, X-ray and NMR structure determinations, molecular dynamics calculations, phylogenetic analysis, etc. The point is that people need to be aware of enough of the background of techniques, such as those listed above, that impinge on their speciality that they can critically evaluate the published results. I think that there are very few subspecialties of molecular biology that are not influenced in a major way by the techniques mentioned above. This doesn't mean that one needs to be able to run the programs, or even the computer, but it does mean that you need to know something about the algorithms and their limitations. My personal feeling is that some general information about computational approaches and the kinds of problems that are tractable, as well as those that are intractable, provides a useful context. It may not however be useful for everyone. Michael Gribskov gribskov@ncifcrf.gov
frank@sass.sari.ac.uk (Frank Wright) (04/05/91)
I agree entirely with Michael Gribskov's comment.....
"My perception is that, currently, broad conclusions about the function,
evolution and structure of macromolecules are being made based on
sequence similarity and inferred homology. Few graduate programs
provide the training to critically evaluate such claims, and many people
are left to either take the claim on faith or to reject it as
unimportant."
I'm writing a short "introduction to sequence analysis" course
for Molecular Biologists at Scottish Agricultural Research
Interests and am keen to stress that sequence *analysis* is
really what they are doing, and that *computing* is simply the
means to this end. I feel this is necessary given the use of
"BioComputing" and/or "Molecular Biology Computing" to describe
the activity of sequence analysis as well as the technology that
makes it all possible.
Sequence analysis often involves formulating a model, and analysing
the data assuming that the model is valid. For example, database
searching programs are implementations of algorithms that make
assumptions about evolutionary processes. Tradeoffs between
biological reality and mathematical tractability or CPU time are
necessary. Users should be aware of these when interpreting output.
Some knowledge of subjects like protein structure and evolution are
required when carrying out and interpreting such complex analyses.
..and possibly also statistics... but I realise it's not a word that
one uses nowadays. I avoid it by using "predict", "sequence analysis",
"structure analysis",etc... It stops people's eyes glazing over.
:-)
Frank Wright
SASS Molecular Biology Support
Edinburgh University, Scotland
e-mail: frank@sass.sari.ac.uk