Molecular Life Sciences

Living Edition
| Editors: Robert D. Wells, Judith S. Bond, Judith Klinman, Bettie Sue Siler Masters, Ellis Bell

Genomic Signature Analysis to Predict Plasmid Host Range

  • Haruo Suzuki
  • Celeste J. Brown
  • Eva M. Top
Living reference work entry

Later version available View entry history



Plasmids differ in their ability to maintain themselves in different hosts, and this determines the extent to which they can spread the genes they carry to bacteria of different species in their local environment. The rate at which new sequence information is accumulating means that it is impracticable to test the host range of all new plasmids empirically so ways of predicting host range from their sequence will provide important ways of classifying plasmids and in some cases assessing risk and directing resources. Different bacteria have different genomic sequence signatures of nucleotide composition, and it appears that plasmids that are associated permanently with bacteria of a single signature type tend to acquire that signature while plasmids that move around more between bacterial types are less adapted to any host type. This short entry describes progress in developing this simple concept as an approach to analyzing plasmid genomes and using this information to predict...


Host Range Genomic Signature Related Bacterium Evolutionary Host Plasmid Fragment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.


  1. Abe T, Kanaya S, Kinouchi M, Ichiba Y, Kozuki T, Ikemura T (2003) Informatics for unveiling hidden genome signatures. Genome Res 13:693–702PubMedCrossRefPubMedCentralGoogle Scholar
  2. Abe T, Sugawara H, Kinouchi M, Kanaya S, Ikemura T (2005) Novel phylogenetic studies of genomic sequence fragments derived from uncultured microbe mixtures in environmental and clinical samples. DNA Res 12:281–290PubMedCrossRefGoogle Scholar
  3. Arakawa K, Suzuki H, Tomita M (2009) Quantitative analysis of replication-related mutation and selection pressures in bacterial chromosomes and plasmids using generalised GC skew index. BMC Genomics 10:640PubMedCrossRefPubMedCentralGoogle Scholar
  4. Bohlin J (2011) Genomic signatures in microbes – properties and applications. Scientific World J 11:715–725CrossRefGoogle Scholar
  5. Campbell A, Mrazek J, Karlin S (1999) Genome signature comparisons among prokaryote, plasmid, and mitochondrial DNA. Proc Natl Acad Sci U S A 96:9184–9189PubMedCrossRefPubMedCentralGoogle Scholar
  6. Dalevi D, Dubhashi D, Hermansson M (2006) Bayesian classifiers for detecting HGT using fixed and variable order Markov models of genomic signatures. Bioinformatics 22:517–522PubMedCrossRefGoogle Scholar
  7. Dalevi D, Dubhashi D, Hermansson M (2006) A new order estimator for fixed and variable length Markov models with applications to DNA sequence similarity. Stat Appl Genet Mol Biol 5: Article 8Google Scholar
  8. Karlin S, Burge C (1995) Dinucleotide relative abundance extremes: a genomic signature. Trends Genet 11:283–290PubMedCrossRefGoogle Scholar
  9. Karlin S, Mrazek J, Campbell AM (1997) Compositional biases of bacterial genomes and evolutionary implications. J Bacteriol 179:3899–3913PubMedPubMedCentralGoogle Scholar
  10. Mellmann A, Harmsen D, Cummings CA et al (2011) Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation sequencing technology. PLoS One 6:e22751PubMedCrossRefPubMedCentralGoogle Scholar
  11. Mrazek J (2009) Phylogenetic signals in DNA composition: limitations and prospects. Mol Biol Evol 26:1163–1169PubMedCrossRefGoogle Scholar
  12. Norberg P, Bergstrom M, Jethava V, Dubhashi D, Hermansson M (2011) The IncP-1 plasmid backbone adapts to different host bacterial species and evolves through homologous recombination. Nat Commun 2:268PubMedCrossRefPubMedCentralGoogle Scholar
  13. Proctor LM (2011) The human microbiome project in 2011 and beyond. Cell Host Microbe 10:287–291PubMedCrossRefGoogle Scholar
  14. Rocha EP, Danchin A (2002) Base composition bias might result from competition for metabolic resources. Trends Genet 18:291–294PubMedCrossRefGoogle Scholar
  15. Simmons MP (2008) Potential use of host-derived genome signatures to root virus phylogenies. Mol Phylogenet Evol 49:969–978PubMedCrossRefGoogle Scholar
  16. Suzuki H, Sota M, Brown CJ, Top EM (2008) Using Mahalanobis distance to compare genomic signatures between bacterial plasmids and chromosomes. Nucleic Acids Res 36:e147PubMedCrossRefPubMedCentralGoogle Scholar
  17. Suzuki H, Yano H, Brown CJ, Top EM (2010) Predicting plasmid promiscuity based on genomic signature. J Bacteriol 192:6045–6055PubMedCrossRefPubMedCentralGoogle Scholar
  18. Thorsted PB, Macartney DP, Akhtar P et al (1998) Complete sequence of the IncPbeta plasmid R751: implications for evolution and organisation of the IncP backbone. J Mol Biol 282:969–990PubMedCrossRefGoogle Scholar
  19. van Passel MW, Bart A, Luyf AC, van Kampen AH, van der Ende A (2006) Compositional discordance between prokaryotic plasmids and host chromosomes. BMC Genomics 7:26PubMedCrossRefPubMedCentralGoogle Scholar
  20. van Passel MW, Kuramae EE, Luyf AC, Bart A, Boekhout T (2006) The reach of the genome signature in prokaryotes. BMC Evol Biol 6:84PubMedCrossRefPubMedCentralGoogle Scholar
  21. Wilkins BM, Chilley PM, Thomas AT, Pocklington MJ (1996) Distribution of restriction enzyme recognition sequences on broad host range plasmid RP4: molecular and evolutionary implications. J Mol Biol 258:447–456PubMedCrossRefGoogle Scholar
  22. Willner D, Thurber RV, Rohwer F (2009) Metagenomic signatures of 86 microbial and viral metagenomes. Environ Microbiol 11:1752–1766PubMedCrossRefGoogle Scholar
  23. Wong K, Finan TM, Golding GB (2002) Dinucleotide compositional analysis of Sinorhizobium meliloti using the genome signature: distinguishing chromosomes and plasmids. Funct Integr Genomics 2:274–281PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Population Medicine and Diagnostic Sciences, College of Veterinary MedicineCornell UniversityIthacaUSA
  2. 2.Department of Biological Sciences, Institute for Bioinformatics and Evolutionary Studies (IBEST)University of IdahoMoscowUSA