Literature DB >> 1342931

A statistical method for detecting regions with different evolutionary dynamics in multialigned sequences.

G Pesole1, M Attimonelli, G Preparata, C Saccone.   

Abstract

We describe a stochastic method for tracing the evolutionary pattern of multialigned sequences. This method allows us to detect gene regions with distinct evolutionary dynamics, e.g., regions that significantly deviate from the expected behavior. Accurate detection of hypervariable or hyperconstrained regions may provide useful information on the structure/function relationship of biosequences. This information can help localize functional constraints. In addition, the selection of distinct evolutionary dynamics may assist in the correct use of biosequences as reliable molecular clocks.

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Year:  1992        PMID： 1342931     DOI： 10.1016/1055-7903(92)90023-a

Source DB:  PubMed          Journal:  Mol Phylogenet Evol        ISSN： 1055-7903            Impact factor:   4.286


  6 in total

1.  Molecular classification of living organisms.

Authors:  C Saccone; C Gissi; C Lanave; G Pesole
Journal:  J Mol Evol       Date:  1995-03       Impact factor: 2.395

2.  Evolutionary analysis of cytochrome b sequences in some Perciformes: evidence for a slower rate of evolution than in mammals.

Authors:  P Cantatore; M Roberti; G Pesole; A Ludovico; F Milella; M N Gadaleta; C Saccone
Journal:  J Mol Evol       Date:  1994-12       Impact factor: 2.395

3.  Time and biosequences.

Authors:  C Saccone; C Lanave; G Pesole
Journal:  J Mol Evol       Date:  1993-08       Impact factor: 2.395

4.  Monte Carlo simulation in phylogenies: an application to test the constancy of evolutionary rates.

Authors:  J C Adell; J Dopazo
Journal:  J Mol Evol       Date:  1994-03       Impact factor: 2.395

5.  Evolutionary history of the human multigene families reveals widespread gene duplications throughout the history of animals.

Authors:  Nashaiman Pervaiz; Nazia Shakeel; Ayesha Qasim; Rabail Zehra; Saneela Anwar; Neenish Rana; Yongbiao Xue; Zhang Zhang; Yiming Bao; Amir Ali Abbasi
Journal:  BMC Evol Biol       Date:  2019-06-20       Impact factor: 3.260

6.  Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences.

Authors:  Zhang Zhang; Jeffrey P Townsend
Journal:  PLoS Comput Biol       Date:  2009-06-26       Impact factor: 4.475
  6 in total

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