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Reliability and applications of statistical methods based on oligonucleotide frequencies in bacterial and archaeal genomes.

Bohlin J, Skjerve E, Ussery DW - BMC Genomics (2008)

Bottom Line: The increasing number of sequenced prokaryotic genomes contains a wealth of genomic data that needs to be effectively analysed.The tetranucleotide ZOM measure was a good measure to detect horizontally transferred regions, and when used to compare the phylogenetic relationships between plasmids and hosts, significant correlation (R2 = 0.4) was found with genomic GC content and intra-chromosomal homogeneity.However, none of the measures examined were superior in all tests, and therefore the choice of the statistical method should depend on the task at hand.

View Article: PubMed Central - HTML - PubMed

Affiliation: Norwegian School of Veterinary Science, P.O. Box 8146 Dep., N-0033 Oslo, Norway. n.bohlin@veths.no

ABSTRACT

Background: The increasing number of sequenced prokaryotic genomes contains a wealth of genomic data that needs to be effectively analysed. A set of statistical tools exists for such analysis, but their strengths and weaknesses have not been fully explored. The statistical methods we are concerned with here are mainly used to examine similarities between archaeal and bacterial DNA from different genomes. These methods compare observed genomic frequencies of fixed-sized oligonucleotides with expected values, which can be determined by genomic nucleotide content, smaller oligonucleotide frequencies, or be based on specific statistical distributions. Advantages with these statistical methods include measurements of phylogenetic relationship with relatively small pieces of DNA sampled from almost anywhere within genomes, detection of foreign/conserved DNA, and homology searches. Our aim was to explore the reliability and best suited applications for some popular methods, which include relative oligonucleotide frequencies (ROF), di- to hexanucleotide zero'th order Markov methods (ZOM) and 2.order Markov chain Method (MCM). Tests were performed on distant homology searches with large DNA sequences, detection of foreign/conserved DNA, and plasmid-host similarity comparisons. Additionally, the reliability of the methods was tested by comparing both real and random genomic DNA.

Results: Our findings show that the optimal method is context dependent. ROFs were best suited for distant homology searches, whilst the hexanucleotide ZOM and MCM measures were more reliable measures in terms of phylogeny. The dinucleotide ZOM method produced high correlation values when used to compare real genomes to an artificially constructed random genome with similar %GC, and should therefore be used with care. The tetranucleotide ZOM measure was a good measure to detect horizontally transferred regions, and when used to compare the phylogenetic relationships between plasmids and hosts, significant correlation (R2 = 0.4) was found with genomic GC content and intra-chromosomal homogeneity.

Conclusion: The statistical methods examined are fast, easy to implement, and powerful for a number of different applications involving genomic sequence comparisons. However, none of the measures examined were superior in all tests, and therefore the choice of the statistical method should depend on the task at hand.

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Plasmid-hosts comparisons based on the tetranucleotide ZOM measure. Plasmids sized 10 kbp and larger were compared with their corresponding archaeal and bacterial hosts. Plasmid-host correlation values (black dots) were then compared with host average autocorrelation values (expected plasmid-host correlation score, red line) based on 40 kbp sliding windows and tetranucleotide ZOMs. The green line represents lower autocorrelation values, i.e. average autocorrelation values subtracted by standard deviation, while the blue and cyan lines show host and plasmid GC content respectively. The vertical axis represents host bacteria average autocorrelation values (red line), host GC content (blue line), plasmid GC content (cyan), and plasmid-host correlations (black dots). All bacteria and archaea with corresponding plasmids are distributed as points along the horizontal axis and sorted by increasing plasmid GC content from left to right. From the graph it can be observed that GC rich bacteria were more similar to their plasmids in terms of tetranucleotide ZOMs than AT rich bacteria. It can also be noticed that average autocorrelation scores (expected plasmid-host correlation scores) seems to increase and become less volatile for GC rich bacteria than their AT rich counterparts.
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Figure 6: Plasmid-hosts comparisons based on the tetranucleotide ZOM measure. Plasmids sized 10 kbp and larger were compared with their corresponding archaeal and bacterial hosts. Plasmid-host correlation values (black dots) were then compared with host average autocorrelation values (expected plasmid-host correlation score, red line) based on 40 kbp sliding windows and tetranucleotide ZOMs. The green line represents lower autocorrelation values, i.e. average autocorrelation values subtracted by standard deviation, while the blue and cyan lines show host and plasmid GC content respectively. The vertical axis represents host bacteria average autocorrelation values (red line), host GC content (blue line), plasmid GC content (cyan), and plasmid-host correlations (black dots). All bacteria and archaea with corresponding plasmids are distributed as points along the horizontal axis and sorted by increasing plasmid GC content from left to right. From the graph it can be observed that GC rich bacteria were more similar to their plasmids in terms of tetranucleotide ZOMs than AT rich bacteria. It can also be noticed that average autocorrelation scores (expected plasmid-host correlation scores) seems to increase and become less volatile for GC rich bacteria than their AT rich counterparts.

Mentions: Tetranucleotide ZOMs were used to compare plasmids sized 10 kbp and larger with corresponding host genomes, totalling 83 different bacteria and archaea with 108 chromosomes and 179 different plasmids. A minimum plasmid size was used to make the comparisons as accurate as possible between plasmids and hosts using the tetranucleotide ZOM method. Our findings support the claim in [26] that plasmids are, especially AT rich genomes, more distantly related to their host (average correlation of 0.82) than what would be expected from their hosts average autocorrelation values based on correspondingly sized sliding windows (average correlation of 0.94), see Figure 6 (additional file 7 contains a labeled graph) and materials and methods section for details. In addition, we found that plasmid similarity to host, YPH, correlated well with host average autocorrelation values, XAAC, and GC content, XGC,


Reliability and applications of statistical methods based on oligonucleotide frequencies in bacterial and archaeal genomes.

Bohlin J, Skjerve E, Ussery DW - BMC Genomics (2008)

Plasmid-hosts comparisons based on the tetranucleotide ZOM measure. Plasmids sized 10 kbp and larger were compared with their corresponding archaeal and bacterial hosts. Plasmid-host correlation values (black dots) were then compared with host average autocorrelation values (expected plasmid-host correlation score, red line) based on 40 kbp sliding windows and tetranucleotide ZOMs. The green line represents lower autocorrelation values, i.e. average autocorrelation values subtracted by standard deviation, while the blue and cyan lines show host and plasmid GC content respectively. The vertical axis represents host bacteria average autocorrelation values (red line), host GC content (blue line), plasmid GC content (cyan), and plasmid-host correlations (black dots). All bacteria and archaea with corresponding plasmids are distributed as points along the horizontal axis and sorted by increasing plasmid GC content from left to right. From the graph it can be observed that GC rich bacteria were more similar to their plasmids in terms of tetranucleotide ZOMs than AT rich bacteria. It can also be noticed that average autocorrelation scores (expected plasmid-host correlation scores) seems to increase and become less volatile for GC rich bacteria than their AT rich counterparts.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2289816&req=5

Figure 6: Plasmid-hosts comparisons based on the tetranucleotide ZOM measure. Plasmids sized 10 kbp and larger were compared with their corresponding archaeal and bacterial hosts. Plasmid-host correlation values (black dots) were then compared with host average autocorrelation values (expected plasmid-host correlation score, red line) based on 40 kbp sliding windows and tetranucleotide ZOMs. The green line represents lower autocorrelation values, i.e. average autocorrelation values subtracted by standard deviation, while the blue and cyan lines show host and plasmid GC content respectively. The vertical axis represents host bacteria average autocorrelation values (red line), host GC content (blue line), plasmid GC content (cyan), and plasmid-host correlations (black dots). All bacteria and archaea with corresponding plasmids are distributed as points along the horizontal axis and sorted by increasing plasmid GC content from left to right. From the graph it can be observed that GC rich bacteria were more similar to their plasmids in terms of tetranucleotide ZOMs than AT rich bacteria. It can also be noticed that average autocorrelation scores (expected plasmid-host correlation scores) seems to increase and become less volatile for GC rich bacteria than their AT rich counterparts.
Mentions: Tetranucleotide ZOMs were used to compare plasmids sized 10 kbp and larger with corresponding host genomes, totalling 83 different bacteria and archaea with 108 chromosomes and 179 different plasmids. A minimum plasmid size was used to make the comparisons as accurate as possible between plasmids and hosts using the tetranucleotide ZOM method. Our findings support the claim in [26] that plasmids are, especially AT rich genomes, more distantly related to their host (average correlation of 0.82) than what would be expected from their hosts average autocorrelation values based on correspondingly sized sliding windows (average correlation of 0.94), see Figure 6 (additional file 7 contains a labeled graph) and materials and methods section for details. In addition, we found that plasmid similarity to host, YPH, correlated well with host average autocorrelation values, XAAC, and GC content, XGC,

Bottom Line: The increasing number of sequenced prokaryotic genomes contains a wealth of genomic data that needs to be effectively analysed.The tetranucleotide ZOM measure was a good measure to detect horizontally transferred regions, and when used to compare the phylogenetic relationships between plasmids and hosts, significant correlation (R2 = 0.4) was found with genomic GC content and intra-chromosomal homogeneity.However, none of the measures examined were superior in all tests, and therefore the choice of the statistical method should depend on the task at hand.

View Article: PubMed Central - HTML - PubMed

Affiliation: Norwegian School of Veterinary Science, P.O. Box 8146 Dep., N-0033 Oslo, Norway. n.bohlin@veths.no

ABSTRACT

Background: The increasing number of sequenced prokaryotic genomes contains a wealth of genomic data that needs to be effectively analysed. A set of statistical tools exists for such analysis, but their strengths and weaknesses have not been fully explored. The statistical methods we are concerned with here are mainly used to examine similarities between archaeal and bacterial DNA from different genomes. These methods compare observed genomic frequencies of fixed-sized oligonucleotides with expected values, which can be determined by genomic nucleotide content, smaller oligonucleotide frequencies, or be based on specific statistical distributions. Advantages with these statistical methods include measurements of phylogenetic relationship with relatively small pieces of DNA sampled from almost anywhere within genomes, detection of foreign/conserved DNA, and homology searches. Our aim was to explore the reliability and best suited applications for some popular methods, which include relative oligonucleotide frequencies (ROF), di- to hexanucleotide zero'th order Markov methods (ZOM) and 2.order Markov chain Method (MCM). Tests were performed on distant homology searches with large DNA sequences, detection of foreign/conserved DNA, and plasmid-host similarity comparisons. Additionally, the reliability of the methods was tested by comparing both real and random genomic DNA.

Results: Our findings show that the optimal method is context dependent. ROFs were best suited for distant homology searches, whilst the hexanucleotide ZOM and MCM measures were more reliable measures in terms of phylogeny. The dinucleotide ZOM method produced high correlation values when used to compare real genomes to an artificially constructed random genome with similar %GC, and should therefore be used with care. The tetranucleotide ZOM measure was a good measure to detect horizontally transferred regions, and when used to compare the phylogenetic relationships between plasmids and hosts, significant correlation (R2 = 0.4) was found with genomic GC content and intra-chromosomal homogeneity.

Conclusion: The statistical methods examined are fast, easy to implement, and powerful for a number of different applications involving genomic sequence comparisons. However, none of the measures examined were superior in all tests, and therefore the choice of the statistical method should depend on the task at hand.

Show MeSH
Related in: MedlinePlus