Literature DB >> 16447996

Shannon information in complete genomes.

Chang-Heng Chang1, Li-Ching Hsieh, Ta-Yuan Chen, Hong-Da Chen, Liaofu Luo, Hoong-Chien Lee.   

Abstract

Shannon information in the genomes of all completely sequenced prokaryotes and eukaryotes are measured in word lengths of two to ten letters. It is found that in a scale-dependent way, the Shannon information in complete genomes are much greater than that in matching random sequences - thousands of times greater in the case of short words. Furthermore, with the exception of the 14 chromosomes of Plasmodium falciparum, the Shannon information in all available complete genomes belong to a universality class given by an extremely simple formula. The data are consistent with a model for genome growth composed of two main ingredients: random segmental duplications that increase the Shannon information in a scale-independent way, and random point mutations that preferentially reduces the larger-scale Shannon information. The inference drawn from the present study is that the large-scale and coarse-grained growth of genomes was selectively neutral and this suggests an independent corroboration of Kimura's neutral theory of evolution.

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Year:  2004        PMID: 16447996     DOI: 10.1109/csb.2004.1332413

Source DB:  PubMed          Journal:  Proc IEEE Comput Syst Bioinform Conf        ISSN: 1551-7497


  4 in total

1.  Applying Shannon's information theory to bacterial and phage genomes and metagenomes.

Authors:  Sajia Akhter; Barbara A Bailey; Peter Salamon; Ramy K Aziz; Robert A Edwards
Journal:  Sci Rep       Date:  2013-01-08       Impact factor: 4.379

2.  SeeDNA: a visualization tool for K-string content of long DNA sequences and their randomized counterparts.

Authors:  Junjie Shen; Shuyu Zhang; Hoong-Chien Lee; Bailin Hao
Journal:  Genomics Proteomics Bioinformatics       Date:  2004-08       Impact factor: 7.691

3.  Kullback Leibler divergence in complete bacterial and phage genomes.

Authors:  Sajia Akhter; Ramy K Aziz; Mona T Kashef; Eslam S Ibrahim; Barbara Bailey; Robert A Edwards
Journal:  PeerJ       Date:  2017-11-30       Impact factor: 2.984

Review 4.  Information theory applications for biological sequence analysis.

Authors:  Susana Vinga
Journal:  Brief Bioinform       Date:  2013-09-20       Impact factor: 11.622

  4 in total

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