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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

Random genome compared to sequenced bacterial genomes. Comparisons between 581 sequenced bacterial and archaeal chromosomes and plasmids with a random 5.3 mbp DNA sequence with 50% GC content. The comparisons were performed to test the reliability of different oligonucleotide based statistical measures consisting of di- to hexanucleotide ZOMs, tetranucleotide ROFs and MCMs. The chromosomes and plasmids, represented as points along the horizontal axis, were correlated with the random DNA sequence, with the corresponding correlation scores on the vertical axis, and sorted by increasing AT content from left to right. Higher correlation scores means better match. In (A) all chromosomes and plasmids were compared using di- to hexanucleotide ZOMs, while in (B) they were compared using tetranucleotide ROFs and MCMs, with tetranucleotide ZOMs included as reference. It can be observed that dinucleotide ZOMs achieve surprisingly high correlation scores (A) while hexanucleotide ZOMs show no correlation at all. Tetranucleotide ROFs (B) achieves slightly higher correlation values than both tetranucleotide MCMs and ZOMs.
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Figure 1: Random genome compared to sequenced bacterial genomes. Comparisons between 581 sequenced bacterial and archaeal chromosomes and plasmids with a random 5.3 mbp DNA sequence with 50% GC content. The comparisons were performed to test the reliability of different oligonucleotide based statistical measures consisting of di- to hexanucleotide ZOMs, tetranucleotide ROFs and MCMs. The chromosomes and plasmids, represented as points along the horizontal axis, were correlated with the random DNA sequence, with the corresponding correlation scores on the vertical axis, and sorted by increasing AT content from left to right. Higher correlation scores means better match. In (A) all chromosomes and plasmids were compared using di- to hexanucleotide ZOMs, while in (B) they were compared using tetranucleotide ROFs and MCMs, with tetranucleotide ZOMs included as reference. It can be observed that dinucleotide ZOMs achieve surprisingly high correlation scores (A) while hexanucleotide ZOMs show no correlation at all. Tetranucleotide ROFs (B) achieves slightly higher correlation values than both tetranucleotide MCMs and ZOMs.

Mentions: In the first test, all the measures mentioned above were used to compare 581 bacterial and archaeal chromosomes and plasmids with a completely random DNA sequence with similar size and GC content to an E. coli chromosome, which for this case can be considered as a 5 mbp genome with 50% GC. Lower correlation values between genomes and the random DNA sequences means that the method is more reliable with fewer false positives. For all measures, correlation values above 0.7 indicate some kind of relation. From Figure 1(A) (see additional file 1 for further details) it can be observed that the dinucleotide ZOMs had, by far, the largest spread with respect to correlation scores ranging from -0.66 for Pseudoalteromonas haloplanktis strain TAC125 to Methanospirillum hungatei strain JF-1 with a correlation of 0.81. Stepping up to trinucleotide ZOMs the spread decreased dramatically with P. haloplanktis strain TAC125, again having the lowest correlation value of -0.42, while Corynebacterium efficiens strain YS-314 had the highest value of 0.34. The tetranucleotide ZOMs were still better, with scores ranging from -0.24 for P. haloplanktis strain TAC125 to 0.18 for Leptospira interrogans serovar Copenhageni. Pentanucleotide ZOM correlations ranged from -0.13 to 0.1, while hexanucleotide ZOMs were firmly placed with the lowest correlation scores, never going outside the interval (-0.07, 0.05) for all 581 DNA sequences tested. Turning now to tetranucleotide ROFs and MCMs, as shown in Figure 1(B), the correlation scores were between (-0.31, 0.34), and (-0.25, 0.18), respectively. For the ROF measure mostly plasmids were found at the lower end of the correlation score interval. Xylella fastidiosa strain 9a5c and Geobacter metallireducens strain GS-15 were the first chromosomes from the lower end of the correlation list, obtaining scores of -0.17 and 0.05, respectively. Tetranucleotide MCMs found Sulfolobus solfataricus strain P2 and Acinetobacter sp. ADP1 at each end, respectively, in the correlation score interval (-0.25, 0.18).


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

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

Random genome compared to sequenced bacterial genomes. Comparisons between 581 sequenced bacterial and archaeal chromosomes and plasmids with a random 5.3 mbp DNA sequence with 50% GC content. The comparisons were performed to test the reliability of different oligonucleotide based statistical measures consisting of di- to hexanucleotide ZOMs, tetranucleotide ROFs and MCMs. The chromosomes and plasmids, represented as points along the horizontal axis, were correlated with the random DNA sequence, with the corresponding correlation scores on the vertical axis, and sorted by increasing AT content from left to right. Higher correlation scores means better match. In (A) all chromosomes and plasmids were compared using di- to hexanucleotide ZOMs, while in (B) they were compared using tetranucleotide ROFs and MCMs, with tetranucleotide ZOMs included as reference. It can be observed that dinucleotide ZOMs achieve surprisingly high correlation scores (A) while hexanucleotide ZOMs show no correlation at all. Tetranucleotide ROFs (B) achieves slightly higher correlation values than both tetranucleotide MCMs and ZOMs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Random genome compared to sequenced bacterial genomes. Comparisons between 581 sequenced bacterial and archaeal chromosomes and plasmids with a random 5.3 mbp DNA sequence with 50% GC content. The comparisons were performed to test the reliability of different oligonucleotide based statistical measures consisting of di- to hexanucleotide ZOMs, tetranucleotide ROFs and MCMs. The chromosomes and plasmids, represented as points along the horizontal axis, were correlated with the random DNA sequence, with the corresponding correlation scores on the vertical axis, and sorted by increasing AT content from left to right. Higher correlation scores means better match. In (A) all chromosomes and plasmids were compared using di- to hexanucleotide ZOMs, while in (B) they were compared using tetranucleotide ROFs and MCMs, with tetranucleotide ZOMs included as reference. It can be observed that dinucleotide ZOMs achieve surprisingly high correlation scores (A) while hexanucleotide ZOMs show no correlation at all. Tetranucleotide ROFs (B) achieves slightly higher correlation values than both tetranucleotide MCMs and ZOMs.
Mentions: In the first test, all the measures mentioned above were used to compare 581 bacterial and archaeal chromosomes and plasmids with a completely random DNA sequence with similar size and GC content to an E. coli chromosome, which for this case can be considered as a 5 mbp genome with 50% GC. Lower correlation values between genomes and the random DNA sequences means that the method is more reliable with fewer false positives. For all measures, correlation values above 0.7 indicate some kind of relation. From Figure 1(A) (see additional file 1 for further details) it can be observed that the dinucleotide ZOMs had, by far, the largest spread with respect to correlation scores ranging from -0.66 for Pseudoalteromonas haloplanktis strain TAC125 to Methanospirillum hungatei strain JF-1 with a correlation of 0.81. Stepping up to trinucleotide ZOMs the spread decreased dramatically with P. haloplanktis strain TAC125, again having the lowest correlation value of -0.42, while Corynebacterium efficiens strain YS-314 had the highest value of 0.34. The tetranucleotide ZOMs were still better, with scores ranging from -0.24 for P. haloplanktis strain TAC125 to 0.18 for Leptospira interrogans serovar Copenhageni. Pentanucleotide ZOM correlations ranged from -0.13 to 0.1, while hexanucleotide ZOMs were firmly placed with the lowest correlation scores, never going outside the interval (-0.07, 0.05) for all 581 DNA sequences tested. Turning now to tetranucleotide ROFs and MCMs, as shown in Figure 1(B), the correlation scores were between (-0.31, 0.34), and (-0.25, 0.18), respectively. For the ROF measure mostly plasmids were found at the lower end of the correlation score interval. Xylella fastidiosa strain 9a5c and Geobacter metallireducens strain GS-15 were the first chromosomes from the lower end of the correlation list, obtaining scores of -0.17 and 0.05, respectively. Tetranucleotide MCMs found Sulfolobus solfataricus strain P2 and Acinetobacter sp. ADP1 at each end, respectively, in the correlation score interval (-0.25, 0.18).

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