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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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Related in: MedlinePlus

T. maritima tetranucleotide MCM, ROF, and ZOM autocorrelation profiles. Di-, tetra- and hexanucleotide ZOMs (top), respectively red, green and blue lines, together with tetranucleotide MCM and ROF (bottom), respectively green and red lines, based autocorrelation profiles of T. maritima. Autocorrelation scores (vertical axis) were obtained with 5 kbp sliding windows, overlapping every 2.5 kbp, correlated with mean genomic values. The horizontal axis represents chromosomal position, with each point spanning 5 kbp. All large dips, except the one found at position 190 kbp, which was found to be 16S, 23S and 5S rRNA genes, are presumed to be horizontally transferred. The marked dips in the tetranucleotide ZOM profiles are part of a presumed horizontally acquired ABC transport system. It can be observed from the Figure that the profile based on tetranucleotide ROFs resembles the ZOM profiles, but that some dips are less visible. The low average autocorrelation value in the tetranucleotide MCM profile is assumed to be caused by lower departure values between observed and expected tetranucleotide frequencies due to small sliding window size. Although many of the large dips found in the other measures were absent in the MCM profile, irregularities (marked dots) were observed in the MCM profile that were not easily detectable with the other measures. Looking at the di-, tetra- and hexanucleotide ZOM profiles, progressively more fluctuations can be observed for increasing oligonucleotide size while average autocorrelation scores drop.
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Figure 3: T. maritima tetranucleotide MCM, ROF, and ZOM autocorrelation profiles. Di-, tetra- and hexanucleotide ZOMs (top), respectively red, green and blue lines, together with tetranucleotide MCM and ROF (bottom), respectively green and red lines, based autocorrelation profiles of T. maritima. Autocorrelation scores (vertical axis) were obtained with 5 kbp sliding windows, overlapping every 2.5 kbp, correlated with mean genomic values. The horizontal axis represents chromosomal position, with each point spanning 5 kbp. All large dips, except the one found at position 190 kbp, which was found to be 16S, 23S and 5S rRNA genes, are presumed to be horizontally transferred. The marked dips in the tetranucleotide ZOM profiles are part of a presumed horizontally acquired ABC transport system. It can be observed from the Figure that the profile based on tetranucleotide ROFs resembles the ZOM profiles, but that some dips are less visible. The low average autocorrelation value in the tetranucleotide MCM profile is assumed to be caused by lower departure values between observed and expected tetranucleotide frequencies due to small sliding window size. Although many of the large dips found in the other measures were absent in the MCM profile, irregularities (marked dots) were observed in the MCM profile that were not easily detectable with the other measures. Looking at the di-, tetra- and hexanucleotide ZOM profiles, progressively more fluctuations can be observed for increasing oligonucleotide size while average autocorrelation scores drop.

Mentions: Figures 2 and 3 illustrate some of the above mentioned measures in autocorrelation profiles of B. subtilis and T. maritima.


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

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

T. maritima tetranucleotide MCM, ROF, and ZOM autocorrelation profiles. Di-, tetra- and hexanucleotide ZOMs (top), respectively red, green and blue lines, together with tetranucleotide MCM and ROF (bottom), respectively green and red lines, based autocorrelation profiles of T. maritima. Autocorrelation scores (vertical axis) were obtained with 5 kbp sliding windows, overlapping every 2.5 kbp, correlated with mean genomic values. The horizontal axis represents chromosomal position, with each point spanning 5 kbp. All large dips, except the one found at position 190 kbp, which was found to be 16S, 23S and 5S rRNA genes, are presumed to be horizontally transferred. The marked dips in the tetranucleotide ZOM profiles are part of a presumed horizontally acquired ABC transport system. It can be observed from the Figure that the profile based on tetranucleotide ROFs resembles the ZOM profiles, but that some dips are less visible. The low average autocorrelation value in the tetranucleotide MCM profile is assumed to be caused by lower departure values between observed and expected tetranucleotide frequencies due to small sliding window size. Although many of the large dips found in the other measures were absent in the MCM profile, irregularities (marked dots) were observed in the MCM profile that were not easily detectable with the other measures. Looking at the di-, tetra- and hexanucleotide ZOM profiles, progressively more fluctuations can be observed for increasing oligonucleotide size while average autocorrelation scores drop.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: T. maritima tetranucleotide MCM, ROF, and ZOM autocorrelation profiles. Di-, tetra- and hexanucleotide ZOMs (top), respectively red, green and blue lines, together with tetranucleotide MCM and ROF (bottom), respectively green and red lines, based autocorrelation profiles of T. maritima. Autocorrelation scores (vertical axis) were obtained with 5 kbp sliding windows, overlapping every 2.5 kbp, correlated with mean genomic values. The horizontal axis represents chromosomal position, with each point spanning 5 kbp. All large dips, except the one found at position 190 kbp, which was found to be 16S, 23S and 5S rRNA genes, are presumed to be horizontally transferred. The marked dips in the tetranucleotide ZOM profiles are part of a presumed horizontally acquired ABC transport system. It can be observed from the Figure that the profile based on tetranucleotide ROFs resembles the ZOM profiles, but that some dips are less visible. The low average autocorrelation value in the tetranucleotide MCM profile is assumed to be caused by lower departure values between observed and expected tetranucleotide frequencies due to small sliding window size. Although many of the large dips found in the other measures were absent in the MCM profile, irregularities (marked dots) were observed in the MCM profile that were not easily detectable with the other measures. Looking at the di-, tetra- and hexanucleotide ZOM profiles, progressively more fluctuations can be observed for increasing oligonucleotide size while average autocorrelation scores drop.
Mentions: Figures 2 and 3 illustrate some of the above mentioned measures in autocorrelation profiles of B. subtilis and T. maritima.

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