Explaining microbial phenotypes on a genomic scale: Gwas for microbes

Briefings in Functional Genomics

Volumn 12, Issue 4, 2013, Pages 366-0380

  Dutilh, Bas E ^a   Backus, Lennart ^a   Edwards, Robert A ^a   Wels, Michiel ^a   Bayjanov, Jumamurat R ^a   Van Hijum, Sacha A F T ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

Functional genomics; Genome wide association studies; Genotype phenotype association; Microbial genomics; Random forest

Indexed keywords

DNA FRAGMENT;

ARTICLE; ASPERGILLUS FUMIGATUS; BACILLI; DNA INTEGRATION; DNA REPLICATION; DNA SEQUENCE; EUKARYOTIC CELL; FUNCTIONAL GENOMICS; GAIN OF FUNCTION MUTATION; GENE FUNCTION; GENETIC ASSOCIATION; GENOMICS; GENOTYPE PHENOTYPE CORRELATION; LACTOBACILLALES; LACTOCOCCUS LACTIS; LOSS OF FUNCTION MUTATION; MICROBIAL GENETICS; MULTIGENE FAMILY; NONHUMAN; NUCLEOTIDE SEQUENCE; OPEN READING FRAME; PATHOGENICITY; PHENOTYPE; PHYLOGENETIC TREE; POINT MUTATION; PROPHAGE; RANDOM FOREST; SINGLE NUCLEOTIDE POLYMORPHISM; SINORHIZOBIUM MELILOTI; STOP CODON;

FUNCTIONAL GENOMICS; GENOME-WIDE ASSOCIATION STUDIES; GENOTYPE-PHENOTYPE ASSOCIATION; MICROBIAL GENOMICS; RANDOM FOREST;

GENETIC ASSOCIATION STUDIES; GENOME-WIDE ASSOCIATION STUDY; GENOMICS;

EID: 84882941123     PISSN: 20412649     EISSN: 20412657     Source Type: Journal    
DOI: 10.1093/bfgp/elt008     Document Type: Article

References (119)

1. Bork P, Dandekar T, Diaz-Lazcoz Y, et al. Predicting function: from genes to genomes and back. JMolBiol 1998;283: 707-25.
2. Gene Ontology Consortium. Creating the gene ontology resource: design and implementation. Genome Res 2001;11: 1425-33.
3. Szklarczyk D, Franceschini A, Kuhn M, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. NucleicAcidsRes 2011; 39:D561-8.
4. Korbel JO, Jensen LJ, von Mering C, et al. Analysis of genomic context: prediction of functional associations from conserved bidirectionally transcribed gene pairs. Nat Biotechnol 2004;22:911-7.
5. Kensche PR, Oti M, Dutilh BE, et al. Conservation of divergent transcription in fungi. Trends Genet 2008;24: 207-11.
6. Kensche PR, van Noort V, Dutilh BE, et al. Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution. J R Soc Interface 2008;5: 151-70.
7. Sorci L, Blaby I, De Ingeniis J, et al. Genomics-driven reconstruction of acinetobacter NAD metabolism: insights for antibacterial target selection. JBiolChem 2010;285:39490-9.
8. Stranger BE, Stahl EA, Raj T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 2011;187:367-83.
9. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science 1996;273:1516-7.
10. Sherry ST, Ward M, Sirotkin K. dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res 1999;9:677-9.
11. Ozaki K, Ohnishi Y, Iida A, et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet 2002;32:650-4.
12. Huynen M, Dandekar T, Bork P. Differential genome analysis applied to the species-specific features of Helicobacter pylori. FEBS Lett 1998;426:1-5.
13. Huynen MA, Diaz-Lazcoz Y, Bork P. Differential genome display. Trends Genet 1997;13:389-90.
14. Salama N, Guillemin K, McDaniel TK, et al. A whole-genome microarray reveals genetic diversity among Helicobacter pylori strains. Proc Natl Acad Sci U S A 2000;97: 14668-73.
15. MalloffCA, Fernandez RC, Lam WL. Bacterial comparative genomic hybridization: a method for directly identifying lateral gene transfer. JMol Biol 2001;312:1-5.
16. Willenbrock H, Petersen A, Sekse C, et al. Design of a seven-genome Escherichia coli microarray for comparative genomic profiling. J Bacteriol 2006;188:7713-21.
17. Bayjanov JR, Wels M, Starrenburg M, et al. PanCGH: a genotype-calling algorithm for pangenome CGH data. Bioinformatics 2009;25:309-14.
18. Medini D, Donati C, Tettelin H, et al. The microbial pan-genome. Curr Opin Genet Dev 2005;15:589-94.
19. Dorrell N, Mangan JA, Laing KG, et al. Whole genome comparison of Campylobacter jejuni human isolates using a low-cost microarray reveals extensive genetic diversity. Genome Res 2001;11:1706-15.
20. Gresham D, Dunham MJ, Botstein D. Comparing whole genomes using DNA microarrays. Nat Rev Genet 2008;9: 291-302.
21. Ehrenreich A. DNA microarray technology for the microbiologist: an overview. Appl Microbiol Biotechnol 2006;73: 255-73.
22. Harris SR, Clarke IN, Seth-Smith HM, et al. Whole-genome analysis of diverse Chlamydia trachomatis strains identifies phylogenetic relationships masked by current clinical typing. Nat Genet 2012;44:413-9, S1.
23. Harris SR, Feil EJ, Holden MT, et al. Evolution of MRSA during hospital transmission and intercontinental spread. Science 2010;327:469-74.
24. Mutreja A, Kim DW, Thomson NR, et al. Evidence for several waves of global transmission in the seventh cholera pandemic. Nature 2011;477:462-5.
25. Holt KE, Baker S, Weill FX, et al. Shigella sonnei genome sequencing and phylogenetic analysis indicate recent global dissemination from Europe. Nat Genet 2012;44:1056-9.
26. Bayjanov JR, Molenaar D, Tzeneva V, et al. PhenoLink-a web-tool for linking phenotype to ̃omics data for bacteria: application to gene-trait matching for Lactobacillus plantarum strains. BMCGenomics 2012;13:170.
27. MacLean D, Jones JD, Studholme DJ. Application of 'next-generation' sequencing technologies to microbial genetics. Nat RevMicrobiol 2009;7:287-96.
28. Loman NJ, Constantinidou C, Chan JZ, et al. High-throughput bacterial genome sequencing: an embarrassment of choice, a world of opportunity. Nat Rev Microbiol 2012;10:599-606.
29. Wu D, Hugenholtz P, Mavromatis K, et al. A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea. Nature 2009;462:1056-60.
30. Mavromatis K, Land ML, Brettin TS, et al. The fast changing landscape of sequencing technologies and their impact on microbial genome assemblies and annotation. PLoS One 2012;7:e48837.
31. Flicek P, Birney E. Sense from sequence reads: methods for alignment and assembly. NatMethods 2009;6:S6-12.
32. Klassen JL, Currie CR. Gene fragmentation in bacterial draft genomes: extent, consequences and mitigation. BMC Genomics 2012;13:14.
33. Carneiro AR, Ramos RT, Barbosa HP, et al. Quality of prokaryote genome assembly: indispensable issues of factors affecting prokaryote genome assembly quality. Gene 2012; 505:365-7.
34. Siezen RJ, van Hijum SA. Genome (re-)annotation and open-source annotation pipelines. Microb Biotechnol 2010;3: 362-9.
35. Aziz RK, Bartels D, Best AA, et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics 2008;9:75.
36. Delcher AL, Bratke KA, Powers EC, et al. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics 2007;23:673-9.
37. Besemer J, Borodovsky M. GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res 2005;33:W451-4.
38. Besemer J, Lomsadze A, Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res 2001;29:2607-18.
39. Hyatt D, Chen GL, Locascio PF, et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010;11:119.
40. Pruitt KD, Tatusova T, Brown GR, et al. NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res 2012;40: D130-5.
41. Benson DA, Karsch-Mizrachi I, Lipman DJ, et al. GenBank. Nucleic Acids Res 2011;39:D32-7.
42. Bairoch A, Apweiler R, Wu CH, et al. The Universal Protein Resource (UniProt). Nucleic Acids Res 2005;33: D154-9.
43. Altschul SF, Gish W, Miller W, et al. Basic local alignment search tool. JMol Biol 1990;215:403-10.
44. Punta M, Coggill PC, Eberhardt RY, et al. The Pfam protein families database. Nucleic Acids Res 2012;40: D290-301.
45. Johnson DA, Tetu SG, Phillippy K, et al. High-throughput phenotypic characterization of Pseudomonas aeruginosa membrane transport genes. PLoS Genet 2008;4:e1000211.
46. Hsiao TL, Revelles O, Chen L, et al. Automatic policing of biochemical annotations using genomic correlations. Nat Chem Biol 2010;6:34-40.
47. Schnoes AM, Brown SD, Dodevski I, et al. Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoSComput Biol 2009;5:e1000605.
48. Richardson EJ, Watson M. The automatic annotation of bacterial genomes. Brief Bioinform 2013;14:1-12.
49. Fitch WM. Distinguishing homologous from analogous proteins. Syst Zool 1970;19:99-113.
50. Remm M, Storm CE, Sonnhammer EL. Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. JMol Biol 2001;314:1041-52.
51. Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science 1997;278:631-7.
52. Dutilh BE, van Noort V, Van Der Heijden RT, et al. Assessment of phylogenomic and orthology approaches for phylogenetic inference. Bioinformatics 2007;23:815-24.
53. Dutilh BE, Huynen MA, Strous M. Increasing the coverage of a metapopulation consensus genome by iterative read mapping and assembly. Bioinformatics 2009;25:2878-81.
54. Alexeyenko A, Tamas I, Liu G, et al. Automatic clustering of orthologs and inparalogs shared by multiple proteomes. Bioinformatics 2006;22:e9-15.
55. Li L, Stoeckert CJ, Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 2003;13:2178-89.
56. Dutilh BE, Huynen MA, Snel B. A global definition of expression context is conserved between orthologs, but does not correlate with sequence conservation. BMC Genomics 2006;7:10.
57. Tatusov RL, Galperin MY, Natale DA, et al. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res 2000;28:33-6.
58. Dutilh BE, He Y, Hekkelman ML, et al. Signature, a web server for taxonomic characterization of sequence samples using signature genes. Nucleic Acids Res 2008;36: W470-4.
59. Jensen LJ, Kuhn M, Stark M, et al. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 2009;37:D412-6.
60. Powell S, Szklarczyk D, Trachana K, et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res 2012;40:D284-9.
61. Nogueira T, Rankin DJ, Touchon M, et al. Horizontal gene transfer of the secretome drives the evolution of bacterial cooperation and virulence. Curr Biol 2009;19: 1683-91.
62. Brussow H. Bacteria between protists and phages: from antipredation strategies to the evolution of pathogenicity. MolMicrobiol 2007;65:583-9.
63. Wang X, Kim Y, Ma Q, et al. Cryptic prophages help bacteria cope with adverse environments. Nat Commun 2010;1: 147.
64. Akhter S, Aziz RK, Edwards RA. PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies. Nucleic Acids Res 2012;40:e126.
65. Bonhoeffer S, Sniegowski P. Virus evolution: the importance of being erroneous. Nature 2002;420:367, 369.
66. Mokili JL, Rohwer F, Dutilh BE. Metagenomics and future perspectives in virus discovery. Curr Opin Virol 2012;2: 63-77.
67. Weissman SJ, Moseley SL, Dykhuizen DE, et al. Enterobacterial adhesins and the case for studying SNPs in bacteria. TrendsMicrobiol 2003;11:115-7.
68. Sokurenko EV, Courtney HS, Ohman DE, et al. FimH family of type 1 fimbrial adhesins: functional heterogeneity due to minor sequence variations among fimH genes. J Bacteriol 1994;176:748-55.
69. Camps SMT, Dutilh BE, Arendrup MC, et al. Discovery of a hapE mutation that causes azole resistance in Aspergillus fumigatus through whole genome sequencing and sexual crossing. PLoS One 2012;7(11):e50034.
70. Bochner BR. Global phenotypic characterization of bacteria. FEMSMicrobiol Rev 2009;33:191-205.
71. Bochner BR, Gadzinski P, Panomitros E. Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res 2001;11:1246-55.
72. Biondi EG, Tatti E, Comparini D, et al. Metabolic capacity of Sinorhizobium (Ensifer) meliloti strains as determined by phenotype MicroArray analysis. Appl Environ Microbiol 2009;75:5396-404.
73. Akulian JA, Metersky ML. Antibiotic resistance patterns in medical and surgical patients in a combined medical-surgical intensive care unit. J Crit Care 2012; doi:10.1016/j.jcrc.2012.02.006 (Advance Access publication 27 March).
74. Siezen RJ, Tzeneva VA, Castioni A, et al. Phenotypic and genomic diversity of Lactobacillus plantarum strains isolated from various environmental niches. EnvironMicrobiol 2010; 12:758-73.
75. Burall LS, Laksanalamai P, Datta AR. Listeria monocytogenes mutants with altered growth phenotypes at refrigeration temperature and high salt concentrations. ApplEnviron Microbiol 2012;78:1265-72.
76. Marti S, Nait Chabane Y, Alexandre S, et al. Growth of Acinetobacter baumannii in pellicle enhanced the expression of potential virulence factors. PLoS One 2011;6: e26030.
77. Nannapaneni P, Hertwig F, Depke M, et al. Defining the structure of the general stress regulon of Bacillus subtilis using targeted microarray analysis and random forest classification. Microbiology 2012;158:696-707.
78. Siezen RJ, Bayjanov JR, Felis GE, et al. Genome-scale diversity and niche adaptation analysis of Lactococcus lactis by comparative genome hybridization using multi-strain arrays. Microb Biotechnol 2011;4:383-402.
79. Filocamo A, Nueno-Palop C, Bisignano C, et al. Effect of garlic powder on the growth of commensal bacteria from the gastrointestinal tract. Phytomedicine 2012;19:707-11.
80. Habib F, Johnson AD, Bundschuh R, et al. Large scale genotype-phenotype correlation analysis based on phylogenetic trees. Bioinformatics 2007;23:785-8.
81. Fuste E, Galisteo GJ, Jover L, et al. Comparison of antibiotic susceptibility of old and current Serratia. Future Microbiol 2012;7:781-6.
82. Siezen RJ, Starrenburg MJ, Boekhorst J, et al. Genome-scale genotype-phenotype matching of two Lactococcus lactis isolates from plants identifies mechanisms of adaptation to the plant niche. Appl Environ Microbiol 2008;74:424-36.
83. Liu Y, Li J, Sam L, et al. An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits. PLoSComput Biol 2006;2:e159.
84. Fiehn O. Metabolomics-the link between genotypes and phenotypes. PlantMol Biol 2002;48:155-71.
85. Field D, Garrity G, Gray T, et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol 2008;26:541-7.
86. Zhang M, Zhang D, Wells MT. Variable selection for large p small n regression models with incomplete data: mapping QTL with epistases. BMC Bioinformatics 2008;9:251.
87. Fitzner K, Heckinger E. Sample size calculation and power analysis: a quick review. Diabetes Educ 2010;36:701-7.
88. Quince C, Lanzen A, Curtis TP, et al. Accurate determination of microbial diversity from 454 pyrosequencing data. NatMethods 2009;6:639-41.
89. Strobl C, Boulesteix AL, Zeileis A, et al. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 2007;8:25.
90. Nicodemus KK, Malley JD. Predictor correlation impacts machine learning algorithms: implications for genomic studies. Bioinformatics 2009;25:1884-90.
91. Blagus R, Lusa L. Class prediction for high-dimensional class-imbalanced data. BMC Bioinformatics 2010;11:523.
92. Chen C, Liaw A, Breiman L. Using Random Forest to Learn Imbalanced Data, in StatisticsTechnical Reports. Berkeley, CA: Department of Statistics, UC Berkeley, 2004.
93. Fay MP, Proschan MA. Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv 2010;4:1-39.
94. Gelman A. Analysis of variance-Why it is more important than ever. Ann Stat 2005;33:1-31.
95. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. JAmStatAssoc 1952;47:583-621.
96. Plackett RL. Pearson, Karl and the chi-squared test. IntStat Rev 1983;51:59-72.
97. Kendall MG. A new measure of rank correlation. Biometrika 1938;30:81-93.
98. Spearman C. The proof and measurement of association between two things. IntJ Epidemiol 2010;39:1137-50.
99. Gibbons JD. Nonparametric methods for quantitative analysis. Columbus, Ohio: American Sciences Press, 1997.
100. Breiman L. Random forests. Mach Learn 2001;45:5-32.
101. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273-97.
102. Touw WG, Bayjanov JR, Overmars L, et al. Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? Brief Bioinform 2012; doi: 10.1093/bib/bbs034 (Advance Access publication 10 July).
103. Roshan U, Chikkagoudar S, Wei Z, et al. Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest. Nucleic Acids Res 2011;39:e62.
104. De Lobel L, Geurts P, Baele G, et al. A screening methodology based on Random Forests to improve the detection of gene-gene interactions. Eur J Hum Genet 2010;18: 1127-32.
105. Lunetta KL, Hayward LB, Segal J, et al. Screening large-scale association study data: exploiting interactions using random forests. BMCGenet 2004;5:32.
106. Jiang P, Wu H, Wang W, et al. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007;35:W339-44.
107. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13:2498-504.
108. Smoot ME, Ono K, Ruscheinski J, et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011;27:431-2.
109. Yamada T, Letunic I, Okuda S, et al. iPath2.0: interactive pathway explorer. Nucleic Acids Res 2011;39:W412-5.
110. Bron PA, Wels M, Bongers RS, et al. Transcriptomes reveal genetic signatures underlying physiological variations imposed by different fermentation conditions in Lactobacillus plantarum. PLoS One 2012;7:e38720.
111. Pretzer G, Snel J, Molenaar D, et al. Biodiversity-based identification and functional characterization of the mannose-specific adhesin of Lactobacillus plantarum. J Bacteriol 2005;187:6128-36.
112. van Hemert S, Meijerink M, Molenaar D, et al. Identification of Lactobacillus plantarum genes modulating the cytokine response of human peripheral blood mononuclear cells. BMCMicrobiol 2010;10:293.
113. Meijerink M, van Hemert S, Taverne N, et al. Identification of genetic loci in Lactobacillus plantarum that modulate the immune response of dendritic cells using comparative genome hybridization. PLoS One 2010;5: e10632.
114. Marchesi JR, Dutilh BE, Hall N, et al. Towards the human colorectal cancer microbiome. PLoS One 2011;6:e20447.
115. Gianoulis TA, Raes J, Patel PV, et al. Quantifying environmental adaptation of metabolic pathways in metagenomics. ProcNatl Acad SciUSA 2009;106:1374-9.
116. Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol 2011;12: R60.
117. Baran Y, Halperin E. Joint analysis of multiple metagenomic samples. PLoSComput Biol 2012;8:e1002373.
118. van Bokhorst-van de Veen H, Lee IC, Marco ML, et al. Modulation of Lactobacillus plantarum gastrointestinal robustness by fermentation conditions enables identification of bacterial robustness markers. PLoS One 2012;7: e39053.
119. Van Der Heijden RT, Snel B, van Noort V, et al. Orthology prediction at scalable resolution by phylogenetic tree analysis. BMC Bioinformatics 2007;8:83.