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Glookbib search for: MolBio multiple alignment LAllison

%A D. R. Powell
%A L. Allison
%A T. I. Dix
%T Fast, optimal alignment of three sequences using linear gap costs
%J J. Theor. Biol.
%V 207
%N 3
%P 325-336
%M DEC
%D 2000
%K jrnl, JTB, MolBio, Biology, multiple, sequence, alignments, similarity,
   affine, linear, cost, gaps, insert, delete, indel, indels, DNA, time, speed,
   fast, string, strings, iterative, phylogenetic, family tree, Ukkonen,
   edit distance, dynamic programming algorithm, DPA, DRPowell, LAllison, TIDix,
   c2000, c200x, c20xx, zz1100, J Theoretical Biology, bioinformatics
%X [...] The obvious dynamic programming algorithm for optimally
   aligning k sequences of length n runs in O(n^k) time. This is
   impractical if k >= 3 and n is of any reasonable length.
   [...] new algorithm [is] guaranteed to find the optimal alignment [...]
   particularly fast when the (three-way) edit distance is small. [...]
   O(n + d^3) on average.
   [paper][11/'00] and code,
   [doi:10.1006/jtbi.2000.2177]['04]
   more on [bioinformatics].

%A L. Allison
%T Towards modelling evolution = mutation modulo selection in sequence
   alignment
%R 95/225
%I Dept. Computer Science, Monash University
%M JUN
%D 1995
%K LAllison, Monash, TR225, TR 225, MolBio, evolution, pressure, selection,
   fit, fitness, family, phylogenetic, evolutionary tree, trees, sequence,
   multiple alignment, zz0795, c1995, c199x, c19xx, bioinformatics
%X [bioinformatics].

%A L. Allison
%A C. S. Wallace
%T An information measure for the string to string correction problem with
   applications
%J 17th Australian Comp. Sci. Conf.
%P 659-668
%M JAN
%D 1994
%W Christchurch, N. Z.
%K LAllison, CSW, CSWallace, Monash, conf, MolBio, inductive inference, II,
   string, sequence, family, evolutionary, phylogenetic, tree, trees,
   variation, variance, uncertainty, estimate, estimation, parameters, DNA,
   multiple alignment, Gibbs sampling, sample, GS, simulated annealing SA,
   minimum message length MML, Bayesian, temperature, cooling, probabilistic,
   NZ, New Zealand, c1994, c199x, c19xx, ACSC 17, 94, ACSC17, ACSC94,
   bioinformatics, Monash
%O Australian Comp. Sci. Comm., Vol 16,  No 1(C), 1994, isbn:047302313X.
%X It has been shown how to calculate a probability for an alignment.
   Alignments are sampled from their posterior probability distribution.
   This is extended to multiple alignments (of several strings).  Averaging
   over many such alignments gives good estimates of how closely the strings
   are related and in what way.  In addition, sampling in an increasingly
   selective way gives a simulated annealing search for an optimal alignment.
   [paper],
   [Bioinformatics].
   See also the related paper J. Mol. Evol. (39, pp418-430, 1994),
   "The posterior probability distribution ...", for more results.

%A L. Allison
%A C. S. Wallace
%T The posterior probability distribution of alignments and its application
   to parameter estimation of evolutionary trees and to optimization of
   multiple alignments
%J J. Mol. Evol.
%V 39
%N 4
%P 418-430
%M OCT
%D 1994
%O An earlier version is TR 93/188, Dept. Comp. Sci., Monash U., July '93
%K jrnl, MolBio, JME, c1994, c199x, c19xx, LAllison, CSWallace, CSW, DNA,
   bioinformatics, optimisation, estimate, infer, parameters, algorithm,
   multiple, alignment, data, string, molecular, sequence, homology, Markov,
   family, phylogenetic, tree, trees, edit distance, Monte Carlo method, mcmc,
   simulated annealing, SA, inductive inference, II, sample, speed, Bayesian,
   dynamic programming algorithm, DPA, stochastic, methods, GS, Gibbs sampling,
   minimum message length encoding, MML, chain, minimum description length, MDL,
   transthyretin, chloramphenicol resistance gene, CAT, CATB, CATD, CATP, CATQ,
   CCOLI, ECOLI, algorithmic, mutual information, theory, significance,
   probabilistic, temperature, limits, TR 93/188, TR188
%X  "It is shown how to sample alignments from their posterior probability
   distribution given two strings.  This is extended to sampling alignments of
   more than two strings.  The result is firstly applied to the estimation of
   the edges of a given evolutionary tree over several strings.  Secondly,
   when used in conjunction with simulated annealing, it gives a stochastic
   search method for an optimal multiple alignment."
   -- [paper] and source code,
      [reprint.ps],
      [doi:/10.1007/BF00160274]['07].
   (The JME paper is a much expanded and changed version of TR 93/188,
    [TR93/188](.ps))

%A L. Allison
%T A fast algorithm for the optimal alignment of three strings
%J J. Theor. Biol.
%V 164
%N 2
%P 261-269
%M SEP
%D 1993
%O TR 92/168  Dept. Computer Science, Monash University, Oct '92.
%K LAllison, Monash, jrnl, II, JTB, MolBio, bioinformatics, multiple alignment,
   edit distance, Ukkonen, three, string, strings, sequence, sequences,
   dynamic programming algorithm, DPA, TR 92 168 TR92-168 TR168,
   c1993, c199x, c19xx, J Theoretical Biology
%X  Given 3 strings, length ~ n, 3-way edit-distance d,
   O(n.d^2) time algorithm worst case, O(n+d^3) typically.
   Tree costs 0/1/2.   ie.   xxx :0;    xxy, xx-, x-- :1;    xyz, xy- :2
   NB. Each internal node of an unrooted binary tree has 3 neighbours.
   [more],
   [reprint.ps],
   [paper] inc' pdf paper and code,
   [doi:10.1006/jtbi.1993.1153]['04],
   and more on [bioinformatics].

%A L. Allison
%T Some algorithmic attacks on multiple alignment (abstract)
%J Boden Conf.
%W Thredbo, Australia
%M FEB
%D 1993
%K LAllison, Monash, RSBS, ANU, conf, MolBio, MML, II, string,
   sequence, approximate match, matching, three, DPA, bioinformatics
%X also see [Bioinformatics].

%A L. Allison
%T Estimating parameters and evolutionary distances in the inference of
   evolutionary trees (abstract)
%J Robertson Symposium.
%W Australian National University
%M JAN
%D 1993
%K LAllison, Monash, RSBS, ANU, conf, MolBio, MML, inductive inference,
   II, string, sequence, multiple alignment, approximate match,
   matching, phylogenetic, tree, trees, c1993, c199x, c19xx, bioinformatics
%X also see [Bioinformatics].

%A L. Allison
%A C. S. Wallace
%A C. N. Yee
%T Minimum message length encoding, evolutionary trees and multiple alignment
%J 25th Hawaii Int. Conf. on Sys. Sci.
%K LAllison, CSW, Monash, conf, MolBio, minimum message length encoding, MML,
   ML, evolutionary, family, phylogenetic, tree, trees, CSWallace, CSW, human,
   Bayesian, finite state, model, machine, FSM, hidden Markov model, primate,
   HMM, DNA, multiple alignment, inductive inference, II, bioinformatics, chimp,
   HICSS, HICSS25, HICCS92, TR 91 155, TR91-155, TR155, c1992, c199x, c19xx
%V 1
%P 663-674
%M JAN
%D 1992
%O TR 91/155, Dept. Computer Science, Monash University '91
%X "A method of Bayesian inference known as MML encoding is applied to inference
   of an evolutionary tree and to multiple alignment for K >= 2 strings.
   It allows the posterior odds-ratio of two competing hypotheses, for
   example two trees, to be calculated. A tree that is a good hypothesis forms
   the basis  of a short message describing the strings.  The mutation
   process is modelled by finite-state machine.  It is seen that tree
   inference and multiple alignment are intimately connected."
   -- [paper],
   there is an example on the primate globin pseudo-genes.
   (Also see [Bioinformatics].)

%A L. Allison
%A Du Xiaofeng
%T Relating three strings by minimum message length encoding (abstract)
%P 13
%J International Conference on Genes, Proteins and Computers
%W Chester
%I SERC Daresbury Laboratory
%M APR
%D 1990
%K LAllison, Monash, conf, MolBio, multiple, three, triple, alignment,
   LCS, LCSS, MML, family, evolutionary phylogenetic tree, bioinformatics,
   inductive inference, II, DNA, c1990, c199x, c19xx
%X also see [Bioinformatics].


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