Literature DB >> 8905150

Compression and genetic sequence analysis.

E Rivals1, M Dauchet, J P Delahaye, O Delgrange.   

Abstract

A novel approach to genetic sequence analysis is presented. This approach, based on compression of algorithms, has been launched simultaneously by Grumbach and Tahi, Milosavljevic and Rivals. To reduce the description of an object, a compression algorithm replaces some regularities in the description by special codes. Thus a compression algorithm can be applied to a sequence in order to study the presence of those regularities all over the sequence. This paper explains this ability, gives examples of compression algorithms already developed and mentions their applications. Finally, the theoretical foundations of the approach are presented in an overview of the algorithmic theory of information.

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Year:  1996        PMID: 8905150     DOI: 10.1016/0300-9084(96)84763-8

Source DB:  PubMed          Journal:  Biochimie        ISSN: 0300-9084            Impact factor:   4.079


  7 in total

1.  A genomic distance based on MUM indicates discontinuity between most bacterial species and genera.

Authors:  Marc Deloger; Meriem El Karoui; Marie-Agnès Petit
Journal:  J Bacteriol       Date:  2008-10-31       Impact factor: 3.490

2.  Differential direct coding: a compression algorithm for nucleotide sequence data.

Authors:  Gregory Vey
Journal:  Database (Oxford)       Date:  2009-09-14       Impact factor: 3.451

3.  An Optimal Seed Based Compression Algorithm for DNA Sequences.

Authors:  Pamela Vinitha Eric; Gopakumar Gopalakrishnan; Muralikrishnan Karunakaran
Journal:  Adv Bioinformatics       Date:  2016-07-31

4.  Training-free measures based on algorithmic probability identify high nucleosome occupancy in DNA sequences.

Authors:  Hector Zenil; Peter Minary
Journal:  Nucleic Acids Res       Date:  2019-11-18       Impact factor: 16.971

5.  Cross chromosomal similarity for DNA sequence compression.

Authors:  Choi-Ping Paula Wu; Ngai-Fong Law; Wan-Chi Siu
Journal:  Bioinformation       Date:  2008-07-14

6.  Detecting microsatellites within genomes: significant variation among algorithms.

Authors:  Sébastien Leclercq; Eric Rivals; Philippe Jarne
Journal:  BMC Bioinformatics       Date:  2007-04-18       Impact factor: 3.169

7.  Calculating Kolmogorov complexity from the output frequency distributions of small Turing machines.

Authors:  Fernando Soler-Toscano; Hector Zenil; Jean-Paul Delahaye; Nicolas Gauvrit
Journal:  PLoS One       Date:  2014-05-08       Impact factor: 3.240

  7 in total

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