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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 |
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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. |
doi_str_mv | 10.1371/journal.pone.0036624 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0036624</identifier><identifier>PMID: 22629319</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>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</subject><ispartof>PloS one, 2012-05, Vol.7 (5), p.e36624-e36624</ispartof><rights>COPYRIGHT 2012 Public Library of Science</rights><rights>2012 Friar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Friar et al. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c719t-1839b783ace52ddae0a68dc2a6206c690f0b2067bd60e9ddc73ddf886fccfea93</citedby><cites>FETCH-LOGICAL-c719t-1839b783ace52ddae0a68dc2a6206c690f0b2067bd60e9ddc73ddf886fccfea93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1324607809/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1324607809?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25732,27903,27904,36991,36992,44569,53769,53771,74872</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22629319$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1627516$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><contributor>Hudson, Matthew E.</contributor><creatorcontrib>Friar, James L</creatorcontrib><creatorcontrib>Goldman, Terrance</creatorcontrib><creatorcontrib>Pérez-Mercader, Juan</creatorcontrib><creatorcontrib>Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)</creatorcontrib><title>Genome sizes and the Benford distribution</title><title>PloS one</title><addtitle>PLoS One</addtitle><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. 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>22629319</pmid><doi>10.1371/journal.pone.0036624</doi><tpages>e36624</tpages><oa>free_for_read</oa></addata></record> |
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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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