Investigations of Oligonucleotide Usage Variance Within and Between Prokaryotes
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{"title"=>"Investigations of oligonucleotide usage variance within and between prokaryotes", "type"=>"journal", "authors"=>[{"first_name"=>"Jon", "last_name"=>"Bohlin", "scopus_author_id"=>"35239694300"}, {"first_name"=>"Eystein", "last_name"=>"Skjerve", "scopus_author_id"=>"7003469374"}, {"first_name"=>"David W.", "last_name"=>"Ussery", "scopus_author_id"=>"7003947937"}], "year"=>2008, "source"=>"PLoS Computational Biology", "identifiers"=>{"isbn"=>"1553-7358 (Electronic)", "issn"=>"1553734X", "pui"=>"351656714", "sgr"=>"43249115309", "pmid"=>"18421372", "scopus"=>"2-s2.0-43249115309", "doi"=>"10.1371/journal.pcbi.1000057"}, "id"=>"758317f2-d58e-3421-ace9-76a90592f03b", "abstract"=>"Oligonucleotide usage in archaeal and bacterial genomes can be linked to a number of properties, including codon usage (trinucleotides), DNA base-stacking energy (dinucleotides), and DNA structural conformation (di- to tetranucleotides). We wanted to assess the statistical information potential of different DNA 'word-sizes' and explore how oligonucleotide frequencies differ in coding and non-coding regions. In addition, we used oligonucleotide frequencies to investigate DNA composition and how DNA sequence patterns change within and between prokaryotic organisms. Among the results found was that prokaryotic chromosomes can be described by hexanucleotide frequencies, suggesting that prokaryotic DNA is predominantly short range correlated, i.e., information in prokaryotic genomes is encoded in short oligonucleotides. Oligonucleotide usage varied more within AT-rich and host-associated genomes than in GC-rich and free-living genomes, and this variation was mainly located in non-coding regions. Bias (selectional pressure) in tetranucleotide usage correlated with GC content, and coding regions were more biased than non-coding regions. Non-coding regions were also found to be approximately 5.5% more AT-rich than coding regions, on average, in the 402 chromosomes examined. Pronounced DNA compositional differences were found both within and between AT-rich and GC-rich genomes. GC-rich genomes were more similar and biased in terms of tetranucleotide usage in non-coding regions than AT-rich genomes. The differences found between AT-rich and GC-rich genomes may possibly be attributed to lifestyle, since tetranucleotide usage within host-associated bacteria was, on average, more dissimilar and less biased than free-living archaea and bacteria.", "link"=>"http://www.mendeley.com/research/investigations-oligonucleotide-usage-variance-within-between-prokaryotes", "reader_count"=>58, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Researcher"=>12, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>23, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>7}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Researcher"=>12, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>23, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>1, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>1, "Agricultural and Biological Sciences"=>44, "Medicine and Dentistry"=>1, "Social Sciences"=>1, "Computer Science"=>3, "Earth and Planetary Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>44}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Austria"=>1, "United States"=>4, "Norway"=>1, "Egypt"=>1, "United Kingdom"=>2, "Mexico"=>1, "Israel"=>1, "Chile"=>1, "Germany"=>1, "Spain"=>2}, "group_count"=>2}

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  • {"month"=>"12", "year"=>"2019", "pdf_views"=>"6", "xml_views"=>"0", "html_views"=>"1"}
  • {"month"=>"1", "year"=>"2020", "pdf_views"=>"3", "xml_views"=>"0", "html_views"=>"1"}
  • {"month"=>"2", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"1", "html_views"=>"1"}
  • {"month"=>"3", "year"=>"2020", "pdf_views"=>"7", "xml_views"=>"0", "html_views"=>"7"}
  • {"month"=>"4", "year"=>"2020", "pdf_views"=>"8", "xml_views"=>"0", "html_views"=>"7"}
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  • {"month"=>"10", "year"=>"2020", "pdf_views"=>"7", "xml_views"=>"1", "html_views"=>"3"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/459902"], "description"=>"<div><p>Oligonucleotide usage in archaeal and bacterial genomes can be linked to a number of properties, including codon usage (trinucleotides), DNA base-stacking energy (dinucleotides), and DNA structural conformation (di- to tetranucleotides). We wanted to assess the statistical information potential of different DNA ‘word-sizes’ and explore how oligonucleotide frequencies differ in coding and non-coding regions. In addition, we used oligonucleotide frequencies to investigate DNA composition and how DNA sequence patterns change within and between prokaryotic organisms. Among the results found was that prokaryotic chromosomes can be described by hexanucleotide frequencies, suggesting that prokaryotic DNA is predominantly short range correlated, i.e., information in prokaryotic genomes is encoded in short oligonucleotides. Oligonucleotide usage varied more within AT-rich and host-associated genomes than in GC-rich and free-living genomes, and this variation was mainly located in non-coding regions. Bias (selectional pressure) in tetranucleotide usage correlated with GC content, and coding regions were more biased than non-coding regions. Non-coding regions were also found to be approximately 5.5% more AT-rich than coding regions, on average, in the 402 chromosomes examined. Pronounced DNA compositional differences were found both within and between AT-rich and GC-rich genomes. GC-rich genomes were more similar and biased in terms of tetranucleotide usage in non-coding regions than AT-rich genomes. The differences found between AT-rich and GC-rich genomes may possibly be attributed to lifestyle, since tetranucleotide usage within host-associated bacteria was, on average, more dissimilar and less biased than free-living archaea and bacteria.</p></div>", "links"=>[], "tags"=>["investigations", "oligonucleotide", "usage", "variance", "prokaryotes"], "article_id"=>150607, "categories"=>["Cancer"], "users"=>["Jon Bohlin", "Eystein Skjerve", "David W. Ussery"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000057"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Investigations_of_Oligonucleotide_Usage_Variance_Within_and_Between_Prokaryotes/150607", "title"=>"Investigations of Oligonucleotide Usage Variance Within and Between Prokaryotes", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2008-04-18 00:10:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/933390"], "description"=>"<p>Cumulative information potential is measured in di- to octanucleotide frequencies in prokaryotic genomes with GC content between 47% and 53%. These genomes were selected because of the increased sensitivity of the Pearson correlation measure for chromosomes with similar AT/GC content. The archaeal and bacterial chromosomes are represented along the horizontal axis, sorted by increasing GC content from left to right, with corresponding correlation scores between observed <i>n</i>-mer words and approximated <i>n</i>-mer words on the vertical axis. The <i>n</i>-mer words were approximated by observed (<i>n</i>–<i>1</i>)-mer words and genomic nucleotide frequencies. High correlation scores indicate increased similarity between observed and approximated oligonucleotide usage.</p>", "links"=>[], "tags"=>["sized"], "article_id"=>603840, "categories"=>["Infectious Diseases"], "users"=>["Jon Bohlin", "Eystein Skjerve", "David W. Ussery"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000057.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_information_potential_in_differently_sized_oligonucleotides_/603840", "title"=>"Statistical information potential in differently sized oligonucleotides.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-18 01:04:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/933520"], "description"=>"<p>Prokaryotic chromosomes are sorted by increasing GC content from left to right on the horizontal axis. The vertical axis represents chromosome size in mbp.</p>", "links"=>[], "tags"=>["genome", "gc"], "article_id"=>603973, "categories"=>["Infectious Diseases"], "users"=>["Jon Bohlin", "Eystein Skjerve", "David W. Ussery"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000057.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prokaryotic_genome_size_versus_GC_content_/603973", "title"=>"Prokaryotic genome size versus GC content.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-18 01:06:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/933618"], "description"=>"<p>Prokaryotic chromosomes are sorted by increasing GC content from left to right (vertical axis), with red and blue regression lines representing OUV values (horizontal axis) for chromosomes and coding regions, respectively. Larger values imply more bias, or stronger selectional pressure, in genomic tetranucleotide usage. The surrounding dotted lines indicate 99% prediction intervals.</p>", "links"=>[], "tags"=>["usage", "variance", "measures", "402", "archaeal", "bacterial"], "article_id"=>604065, "categories"=>["Infectious Diseases"], "users"=>["Jon Bohlin", "Eystein Skjerve", "David W. Ussery"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000057.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tetranucleotide_usage_variance_measures_of_402_archaeal_and_bacterial_chromosomes_/604065", "title"=>"Tetranucleotide usage variance measures of 402 archaeal and bacterial chromosomes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-18 01:07:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/933796"], "description"=>"<p>Each archaeal and bacterial chromosome is sorted on the horizontal axis with increasing GC content from left to right, with corresponding OUD values on the vertical axis. The red and blue lines represent OUD scores for whole chromosomes and coding regions, respectively. Smaller OUD values mean more homogeneous chromosomes in terms of tetranucleotide usage. The surrounding dotted lines indicate 99% prediction intervals.</p>", "links"=>[], "tags"=>["oligonucleotide", "usage", "deviance", "prokaryotic", "chromosomes"], "article_id"=>604240, "categories"=>["Infectious Diseases"], "users"=>["Jon Bohlin", "Eystein Skjerve", "David W. Ussery"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000057.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_oligonucleotide_usage_deviance_within_prokaryotic_chromosomes_OUD_/604240", "title"=>"Average oligonucleotide usage deviance within prokaryotic chromosomes (OUD).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-18 01:10:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/933941"], "description"=>"<p>The vertical axis shows the ratio of observed divided by expected OUD values for each chromosome sorted with respect to genomic GC content from left to right on the horizontal axis. The ratio test measures how observed oligonucleotide usage varies within chromosomes (red line) and coding regions (blue line) compared with expected based on GC content. Rising ratio values above 1 (vertical axis) means increased observed variance compared with expected. The dotted lines represent 99% prediction intervals.</p>", "links"=>[], "tags"=>["observed", "divided", "oud"], "article_id"=>604388, "categories"=>["Infectious Diseases"], "users"=>["Jon Bohlin", "Eystein Skjerve", "David W. Ussery"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000057.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ratio_of_observed_divided_by_expected_OUD_values_/604388", "title"=>"Ratio of observed divided by expected OUD values.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-18 01:13:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/934067"], "description"=>"<p>The vertical axis shows the variance of nucleotide frequencies within chromosomes (red line) and coding regions (blue line) compared with corresponding mean genomic GC content on the horizontal axis. Lower average nucleotide variance scores (vertical axis) means more similar distributions of GC content within chromosomes (and vice versa).</p>", "links"=>[], "tags"=>["gc"], "article_id"=>604519, "categories"=>["Infectious Diseases"], "users"=>["Jon Bohlin", "Eystein Skjerve", "David W. Ussery"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000057.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variation_of_GC_content_within_genomes_/604519", "title"=>"Variation of GC content within genomes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2008-04-18 01:15:19"}

PMC Usage Stats | Further Information

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  • {"month"=>"7", "scanned-page-browse"=>"0", "cited-by"=>"0", "abstract"=>"0", "full-text"=>"4", "year"=>"2010", "pdf"=>"3", "unique-ip"=>"5", "figure"=>"0", "scanned-summary"=>"0", "supp-data"=>"0"}
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  • {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"3", "full-text"=>"11", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"3", "full-text"=>"1", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}

Relative Metric

{"start_date"=>"2008-01-01T00:00:00Z", "end_date"=>"2008-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[324, 674, 863, 1015, 1146, 1259, 1360, 1452, 1540, 1621, 1704, 1776, 1843, 1905, 1968, 2029, 2097, 2162, 2233, 2289, 2352, 2412, 2476, 2538, 2601, 2655, 2714, 2777, 2841, 2894, 2946, 2998, 3047, 3103, 3151, 3201, 3258, 3317, 3369, 3424, 3471, 3521, 3583, 3625, 3687, 3743, 3788, 3838, 3896, 3953, 4015, 4075, 4148, 4201, 4275, 4347, 4420, 4486, 4535, 4592, 4660, 4731, 4792, 4845, 4901, 4971, 5027, 5089, 5148, 5200, 5261, 5318, 5374]}, {"subject_area"=>"/Biology and life sciences/Cell biology", "average_usage"=>[303, 628, 827, 976, 1105, 1222, 1321, 1406, 1482, 1564, 1639, 1713, 1784, 1851, 1915, 1966, 2020, 2080, 2138, 2203, 2258, 2315, 2379, 2437, 2492, 2556, 2613, 2670, 2726, 2785, 2834, 2886, 2948, 2988, 3037, 3089, 3140, 3179, 3236, 3283, 3340, 3390, 3438, 3487, 3541, 3586, 3642, 3707, 3754, 3805, 3853, 3897, 3956, 4016, 4081, 4152, 4218, 4258, 4319, 4378, 4442, 4487, 4536, 4589, 4635, 4689, 4754, 4807, 4863, 4920, 4982, 5042, 5094]}, {"subject_area"=>"/Biology and life sciences/Microbiology", "average_usage"=>[316, 663, 828, 992, 1103, 1209, 1306, 1400, 1491, 1563, 1640, 1715, 1783, 1851, 1908, 1991, 2061, 2121, 2177, 2237, 2296, 2365, 2422, 2521, 2581, 2639, 2696, 2757, 2822, 2874, 2925, 2977, 3030, 3097, 3156, 3247, 3311, 3355, 3414, 3468, 3531, 3579, 3626, 3677, 3715, 3778, 3846, 3903, 3941, 4005, 4076, 4129, 4189, 4266, 4332, 4385, 4452, 4495, 4583, 4647, 4710, 4771, 4849, 4905, 4971, 5026, 5098, 5160, 5213, 5269, 5329, 5393, 5439]}, {"subject_area"=>"/Physical sciences/Materials science", "average_usage"=>[249, 545, 693, 866, 980, 1076, 1186, 1280, 1351, 1458, 1519, 1568, 1622, 1663, 1716, 1803, 1862, 1917, 1952, 2016, 2083, 2150, 2213, 2257, 2295, 2354, 2393, 2436, 2460, 2521, 2554, 2604, 2662, 2704, 2739, 2777, 2824, 2879, 2928, 2970, 3009, 3046, 3084, 3125, 3179, 3233, 3280, 3336, 3395, 3431, 3500, 3535, 3579, 3625, 3735, 3789, 3845, 3901, 3970, 4038, 4083, 4140, 4187, 4224, 4267, 4313, 4368, 4408, 4453, 4487, 4524, 4557, 4595]}]}
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