Literature DB >> 2379021

Minimum message length encoding and the comparison of macromolecules.

L Allison1, C N Yee.   

Abstract

A comparison of inductive inference known as minimum message length encoding is applied to string comparison in molecular biology. The question of whether or not two strings are related and, if so, of how they are related and the problem of finding a good theory of string mutation are treated as inductive inference problems. The method allows the posterior odds-ratio of two string alignments or of two models of string mutation to be computed. The connection between models of mutation and existing string alignment algorithms is made explicit. A fast minimum message length alignment algorithm is also described.

Mesh:

Year:  1990        PMID: 2379021     DOI: 10.1007/bf02458580

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  12 in total

1.  The multiple origins of human Alu sequences.

Authors:  W Bains
Journal:  J Mol Evol       Date:  1986       Impact factor: 2.395

2.  A new algorithm for best subsequence alignments with application to tRNA-rRNA comparisons.

Authors:  M S Waterman; M Eggert
Journal:  J Mol Biol       Date:  1987-10-20       Impact factor: 5.469

3.  Sequence comparison with concave weighting functions.

Authors:  W Miller; E W Myers
Journal:  Bull Math Biol       Date:  1988       Impact factor: 1.758

4.  Conservation of Shannon's redundancy for proteins.

Authors:  L L Gatlin
Journal:  J Mol Evol       Date:  1974       Impact factor: 2.395

5.  The information content of a multistate distribution.

Authors:  D M Boulton; C S Wallace
Journal:  J Theor Biol       Date:  1969-05       Impact factor: 2.691

6.  Codon preference and its use in identifying protein coding regions in long DNA sequences.

Authors:  R Staden; A D McLachlan
Journal:  Nucleic Acids Res       Date:  1982-01-11       Impact factor: 16.971

7.  Method to determine the reading frame of a protein from the purine/pyrimidine genome sequence and its possible evolutionary justification.

Authors:  J C Shepherd
Journal:  Proc Natl Acad Sci U S A       Date:  1981-03       Impact factor: 11.205

8.  Comparative biosequence metrics.

Authors:  T F Smith; M S Waterman; W M Fitch
Journal:  J Mol Evol       Date:  1981       Impact factor: 2.395

9.  An improved algorithm for matching biological sequences.

Authors:  O Gotoh
Journal:  J Mol Biol       Date:  1982-12-15       Impact factor: 5.469

10.  Efficient sequence alignment algorithms.

Authors:  M S Waterman
Journal:  J Theor Biol       Date:  1984-06-07       Impact factor: 2.691

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  6 in total

1.  Finite-state models in the alignment of macromolecules.

Authors:  L Allison; C S Wallace; C N Yee
Journal:  J Mol Evol       Date:  1992-07       Impact factor: 2.395

Review 2.  Inching toward reality: an improved likelihood model of sequence evolution.

Authors:  J L Thorne; H Kishino; J Felsenstein
Journal:  J Mol Evol       Date:  1992-01       Impact factor: 2.395

3.  An evolutionary model for maximum likelihood alignment of DNA sequences.

Authors:  J L Thorne; H Kishino; J Felsenstein
Journal:  J Mol Evol       Date:  1991-08       Impact factor: 2.395

4.  On the representability of complete genomes by multiple competing finite-context (Markov) models.

Authors:  Armando J Pinho; Paulo J S G Ferreira; António J R Neves; Carlos A C Bastos
Journal:  PLoS One       Date:  2011-06-30       Impact factor: 3.240

5.  A genome alignment algorithm based on compression.

Authors:  Minh Duc Cao; Trevor I Dix; Lloyd Allison
Journal:  BMC Bioinformatics       Date:  2010-12-16       Impact factor: 3.169

6.  Bridging the gaps in statistical models of protein alignment.

Authors:  Dinithi Sumanaweera; Lloyd Allison; Arun S Konagurthu
Journal:  Bioinformatics       Date:  2022-06-24       Impact factor: 6.931

  6 in total

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