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Genome sizes and the Benford distribution

Data on the number of Open Reading Frames (ORFs) coded by genomes from the 3 domains of Life show the presence of some notable general features. These include essential differences between the Prokaryotes and Eukaryotes, with the number of ORFs growing linearly with total genome size for the former,...

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Published in:PloS one 2012-05, Vol.7 (5), p.e36624-e36624
Main Authors: Friar, James L, Goldman, Terrance, Pérez-Mercader, Juan
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description Data on the number of Open Reading Frames (ORFs) coded by genomes from the 3 domains of Life show the presence of some notable general features. These include essential differences between the Prokaryotes and Eukaryotes, with the number of ORFs growing linearly with total genome size for the former, but only logarithmically for the latter. Simply by assuming that the (protein) coding and non-coding fractions of the genome must have different dynamics and that the non-coding fraction must be particularly versatile and therefore be controlled by a variety of (unspecified) probability distribution functions (pdf's), we are able to predict that the number of ORFs for Eukaryotes follows a Benford distribution and must therefore have a specific logarithmic form. Using the data for the 1000+ genomes available to us in early 2010, we find that the Benford distribution provides excellent fits to the data over several orders of magnitude. In its linear regime the Benford distribution produces excellent fits to the Prokaryote data, while the full non-linear form of the distribution similarly provides an excellent fit to the Eukaryote data. Furthermore, in their region of overlap the salient features are statistically congruent. This allows us to interpret the difference between Prokaryotes and Eukaryotes as the manifestation of the increased demand in the biological functions required for the larger Eukaryotes, to estimate some minimal genome sizes, and to predict a maximal Prokaryote genome size on the order of 8-12 megabasepairs. These results naturally allow a mathematical interpretation in terms of maximal entropy and, therefore, most efficient information transmission.
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subjects Animals
Archaea - genetics
Bacteria - genetics
Biology
Communication
Distribution functions
Entropy
ENVIRONMENTAL SCIENCES
Eukaryota - genetics
Eukaryotes
Evolution
Evolution, Molecular
Functions (mathematics)
Genes
Genome
Genome Size
Genomes
Genomics
Information processing
Information theory
Laboratories
Mathematics
Mycoplasma genitalium
Open Reading Frames
Physics
Probability distribution
Probability distribution functions
Probability distributions
Prokaryotes
Proteins
Science & Technology - Other Topics
title Genome sizes and the Benford distribution
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