Literature DB >> 12364605

Congruent evolution of different classes of non-coding DNA in prokaryotic genomes.

Igor B Rogozin1, Kira S Makarova, Darren A Natale, Alexey N Spiridonov, Roman L Tatusov, Yuri I Wolf, Jodie Yin, Eugene V Koonin.   

Abstract

Prokaryotic genomes are considered to be 'wall-to-wall' genomes, which consist largely of genes for proteins and structural RNAs, with only a small fraction of the genomic DNA allotted to intergenic regions, which are thought to typically contain regulatory signals. The majority of bacterial and archaeal genomes contain 6-14% non-coding DNA. Significant positive correlations were detected between the fraction of non-coding DNA and inter- and intra-operonic distances, suggesting that different classes of non-coding DNA evolve congruently. In contrast, no correlation was found between any of these characteristics of non-coding sequences and the number of genes or genome size. Thus, the non-coding regions and the gene sets in prokaryotes seem to evolve in different regimes. The evolution of non-coding regions appears to be determined primarily by the selective pressure to minimize the amount of non-functional DNA, while maintaining essential regulatory signals, because of which the content of non-coding DNA in different genomes is relatively uniform and intra- and inter-operonic non-coding regions evolve congruently. In contrast, the gene set is optimized for the particular environmental niche of the given microbe, which results in the lack of correlation between the gene number and the characteristics of non-coding regions.

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Year:  2002        PMID: 12364605      PMCID: PMC140549          DOI: 10.1093/nar/gkf549

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  40 in total

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Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

2.  Genome alignment, evolution of prokaryotic genome organization, and prediction of gene function using genomic context.

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3.  Prediction of operons in microbial genomes.

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4.  Using the COG database to improve gene recognition in complete genomes.

Authors:  D A Natale; M Y Galperin; R L Tatusov; E V Koonin
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5.  Intrinsic errors in genome annotation.

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6.  On the total number of genes and their length distribution in complete microbial genomes.

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Authors:  R L Tatusov; D A Natale; I V Garkavtsev; T A Tatusova; U T Shankavaram; B S Rao; B Kiryutin; M Y Galperin; N D Fedorova; E V Koonin
Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

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  40 in total

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Journal:  Genetics       Date:  2004-12       Impact factor: 4.562

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5.  Computational methods for Gene Orthology inference.

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Review 6.  Systems biology perspectives on minimal and simpler cells.

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7.  Different clustering of genomes across life using the A-T-C-G and degenerate R-Y alphabets: early and late signaling on genome evolution?

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10.  PairWise Neighbours database: overlaps and spacers among prokaryote genomes.

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