Global shifts in genome and proteome composition are very tightly coupled

Genome Biology and Evolution

Volumn 7, Issue 6, 2015, Pages 1519-1532

  Brbić, Maria ^a,b   Warnecke, Tobias ^c   Kriško, Anita ^b   Supek, Fran ^a,d  

^a RUDER BO KOVI INSTITUTE   (Croatia)

^b Mediterranean Institute for Life Sciences   (Croatia)

^c IMPERIAL COLLEGE LONDON   (United Kingdom)

^d CENTRE FOR GENOMIC REGULATION CRG   (Spain)

Author keywords

Amino acid composition; Ecological preferences; Fungal genome; Intergenic DNA; Oligonucleotide composition; Prokaryotic genome; Support vector regression

Indexed keywords

AMINO ACID; NUCLEOTIDE; OLIGONUCLEOTIDE; PROTEIN; PROTEOME; SPACER DNA;

CHEMISTRY; DNA BASE COMPOSITION; FUNGAL GENOME; GENETICS; GENOMICS; METABOLISM; MICROBIAL GENOME; PHYLOGENY; READING FRAME;

AMINO ACIDS; BASE COMPOSITION; DNA, INTERGENIC; GENOME, FUNGAL; GENOME, MICROBIAL; GENOMICS; NUCLEOTIDES; OLIGONUCLEOTIDES; PHYLOGENY; PROTEINS; PROTEOME; READING FRAMES;

EID: 84979855787     PISSN: None     EISSN: 17596653     Source Type: Journal    
DOI: 10.1093/gbe/evv088     Document Type: Article

References (45)

1. Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G. 2008. Support vector machines and kernels for computational biology. PLoS Comput Biol. 4:e1000173.
2. Berka RM et al. 2011. Comparative genomic analysis of the thermophilic biomass-degrading fungi Myceliophthora thermophila and Thielavia terrestris. Nat Biotech. 29:922-927.
3. Blockeel H, Raedt LD, Ramon J. 1998. Top-down induction of clustering trees. In: Shavlik JW, editor. Proceedings of the Fifteenth International Conference on Machine Learning. ICML '98; Madison, Wisconsin. San Francisco (CA): Morgan Kaufmann Publishers Inc. p. 55-63. Available from: http://dl.acm.org/citation.cfm?id=645527.657456.
4. Bohlin J, Brynildsrud O, Vesth T, Skjerve E, Ussery DW. 2013. Amino acid usage is asymmetrically biased in AT-and GC-rich microbial genomes. PLoS One 8:e69878.
5. Breiman L. 2001. Random forests. Mach Learn. 45:5-32.
6. Chang C-C, Lin C-J. 2011. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2:27.
7. DeLong ER, DeLong DM, Clarke-Pearson DL. 1988. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837-845.
8. Deschavanne PJ, Giron A, Vilain J, Fagot G, Fertil B. 1999. Genomic signature: characterization and classification of species assessed by chaos game representation of sequences. Mol Biol Evol. 16:1391-1399.
9. Dethlefsen L, Huse S, SoginML, Relman DA. 2008. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 6:e280.
10. Freeland JC, Gale EF. 1947. The amino-acid composition of certain bacteria and yeasts. Biochem J. 41:135-138.
11. Garcia-Vallve S, Guzman E, Montero MA, Romeu A. 2003. HGT-DB: a database of putative horizontally transferred genes in prokaryotic complete genomes. Nucleic Acids Res. 31:187-189.
12. Graziano G, Merlino A. 2014. Molecular bases of protein halotolerance. Biochim Biophys Acta. 1844:850-858.
13. Greaves RB, Warwicker J. 2007. Mechanisms for stabilisation and the maintenance of solubility in proteins from thermophiles. BMC Struct Biol. 7:18-40.
14. Gu X, Hewett-Emmett D, Li WH. 1998. Directional mutational pressure affects the amino acid composition and hydrophobicity of proteins in bacteria. Genetica 102-103:383-391.
15. Hershberg R, Petrov DA. 2010. Evidence that mutation is universally biased towards AT in bacteria. PLoS Genet. 6:e1001115.
16. Hildebrand F, Meyer A, Eyre-Walker A. 2010. Evidence of selection upon genomic GC-content in bacteria. PLoS Genet. 6:e1001107.
17. Hopf TA, et al. 2012. Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149:1607-1621.
18. Hsu CW, Chang CC, Lin CJ. 2010. A practical guide to support vector classification. Available from: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
19. Hurst LD, Merchant AR. 2001. High guanine-cytosine content is not an adaptation to high temperature: a comparative analysis amongst prokaryotes. Proc Biol Sci. 268:493-497.
20. Ikemura T. 1985. Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol. 2:13-34.
21. Karlin S. 1998. Global dinucleotide signatures and analysis of genomic heterogeneity. Curr Opin Microbiol. 1:598-610.
22. Krisko A, Copic T, Gabaldón T, Lehner B, Supek F. 2014. Inferring gene function from evolutionary change in signatures of translation efficiency. Genome Biol. 15:R44.
23. Lambros RJ, Mortimer JR, Forsdyke DR. 2003. Optimum growth temperature and the base composition of open reading frames in prokaryotes. Extremophiles 7:443-450.
24. Lightfield J, Fram NR, Ely B. 2011. Across bacterial phyla, distantly-related genomes with similar genomic GC content have similar patterns of amino acid usage. PLoS One 6:e17677.
25. Marks DS, et al. 2011. Protein 3D structure computed from evolutionary sequence variation. PLoS One 6:e28766.
26. Molina N, van Nimwegen E. 2008. Universal patterns of purifying selection at noncoding positions in bacteria. Genome Res. 18:148-160.
27. Moura A, Savageau MA, Alves R. 2013. Relative amino acid composition signatures of organisms and environments. PLoS One 8:e77319.
28. Muto A, Osawa S. 1987. The guanine and cytosine content of genomic DNA and bacterial evolution. Proc Natl Acad Sci U S A. 84:166-169.
29. Nekrutenko A, Li W-H. 2000. Assessment of compositional heterogeneity within and between eukaryotic genomes. Genome Res. 10:1986-1995.
30. Paul S, Bag SK, Das S, Harvill ET, Dutta C. 2008. Molecular signature of hypersaline adaptation: insights from genome and proteome composition of halophilic prokaryotes. Genome Biol. 9:R70.
31. Pohl M, Theiben G, Schuster S. 2012. GC content dependency of open reading frame prediction via stop codon frequencies. Gene 511:441-446.
32. Pride DT, Meinersmann RJ, Wassenaar TM, Blaser MJ. 2003. Evolutionary implications of microbial genome tetranucleotide frequency biases. Genome Res. 13:145-158.
33. Rocha EPC, Feil EJ. 2010. Mutational patterns cannot explain genome composition: are there any neutral sites in the genomes of bacteria? PLoS Genet. 6:e1001104.
34. Schietgat L, et al. 2010. Predicting gene function using hierarchical multilabel decision tree ensembles. BMC Bioinformatics 11:2-15.
35. Singer GAC, Hickey DA. 2000. Nucleotide bias causes a genomewide bias in the amino acid composition of proteins. Mol Biol Evol. 17:1581-1588.
36. Škunca N, et al. 2013. Phyletic profiling with cliques of orthologs is enhanced by signatures of paralogy relationships. PLoS Comput Biol. 9:e1002852.
37. Slavkov I, Gjorgjioski V, Struyf J, Džeroski S. 2010. Finding explained groups of time-course gene expression profiles with predictive clustering trees. Mol Biosyst. 6:729-740.
38. Smole Z, et al. 2011. Proteome sequence features carry signatures of the environmental niche of prokaryotes. BMC Evol Biol. 11:26-35.
39. Stokes JL, Gunness M. 1946. The amino acid composition of microorganisms. J Bacteriol. 52:195-207.
40. Sueoka N. 1988. Directionalmutation pressure and neutralmolecular evolution. Proc Natl Acad Sci U S A. 85:2653-2657.
41. Supek F, Skunca N, Repar J, Vlahovicek K, Smuc T. 2010. Translational selection is ubiquitous in prokaryotes. PLoS Genet. 6:e1001004.
42. Tekaia F, Yeramian E. 2006. Evolution of proteomes: fundamental signatures and global trends in amino acid compositions. BMC Genomics 7:307.
43. Vidovic A, Supek F, Nikolic A, Krisko A. 2014. Signatures of conformational stability and oxidation resistance in proteomes of pathogenic bacteria. Cell Rep. 7:1393-1400.
44. Willner D, Thurber RV, Rohwer F. 2009. Metagenomic signatures of 86 microbial and viral metagenomes. Environ Microbiol. 11:1752-1766.
45. Zeldovich KB, Berezovsky IN, Shakhnovich EI. 2007. Protein and DNA sequence determinants of thermophilic adaptation. PLoS Comput Biol. 3:e5.