Bioinformatics, Vol 15, 38-52, Copyright © 1999 by Oxford University Press
JS Liu and CE Lawrence
MOTIVATION: Most existing bioinformatics methods are limited to making
point estimates of one variable, e.g. the optimal alignment, with fixed
input values for all other variables, e.g. gap penalties and scoring
matrices. While the requirement to specify parameters remains one of the
more vexing issues in bioinformatics, it is a reflection of a larger issue:
the need to broaden the view on statistical inference in bioinformatics.
RESULTS: The assignment of probabilities for all possible values of all
unknown variables in a problem in the form of a posterior distribution is
the goal of Bayesian inference. Here we show how this goal can be achieved
for most bioinformatics methods that use dynamic programming. Specifically,
a tutorial style description of a Bayesian inference procedure for
segmentation of a sequence based on the heterogeneity in its composition is
given. In addition, full Bayesian inference algorithms for sequence
alignment are described. AVAILABILITY: Software and a set of transparencies
for a tutorial describing these ideas are available at
http://www.wadsworth.org/res&res/bioinfo/
ARTICLES
Bayesian inference on biopolymer models
Department of Statistics, Stanford University, Stanford, CA, USA. jliu@stat.stabford.edu
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