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Role of Genomics and RNA-seq in Studies of Fungal Virulence

  • GENOMICS AND PATHOGENESIS (S SHOHAM, SECTION EDITOR)
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Abstract

Since its introduction in the last decade, massive parallel sequencing, or “next-generation sequencing”, has revolutionized our access to genomic information, providing accurate data with increasingly higher yields and lower costs with respect to first-generation technology. Massive parallel sequencing of cDNA, or RNA-seq, is progressively replacing array-based technology as the method of choice for transcriptomics. This review describes some of the most recent applications of next-generation sequencing technology to the study of pathogenic fungi, including Candida, Aspergillus and Cryptococcus species. Several integrated approaches illustrate the power and accuracy of RNA-seq for studying the biology of human fungal pathogens. In addition, the lack of consistency in data analysis is discussed.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320:1344–9.

    Article  PubMed  CAS  Google Scholar 

  2. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:621–8.

    Article  PubMed  CAS  Google Scholar 

  3. Parkhomchuk D, Borodina T, Amstislavskiy V, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009;37:e123.

    Article  PubMed  Google Scholar 

  4. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.

    Article  PubMed  CAS  Google Scholar 

  5. Blankenberg D, Gordon A, Von Kuster G, et al. Manipulation of FASTQ data with Galaxy. Bioinformatics. 2010;26:1783–5.

    Article  PubMed  CAS  Google Scholar 

  6. Wang X, Wu Z, Zhang X. Isoform abundance inference provides a more accurate estimation of gene expression levels in RNA-seq. J Bioinforma Comput Biol. 2010;8 Suppl 1:177–92.

    Article  CAS  Google Scholar 

  7. •• Trapnell C, Roberts A, Goff L, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–78. Description of the Tuxedo pipeline and its application for de novo transcriptome assembly, differential gene expression and visualization of results.

    Article  PubMed  CAS  Google Scholar 

  8. Goffeau A, Barrell BG, Bussey H, et al. Life with 6000 genes. Science. 1996;274:546–67.

    Article  PubMed  CAS  Google Scholar 

  9. Jones T, Federspiel NA, Chibana H, et al. The diploid genome sequence of Candida albicans. Proc Natl Acad Sci U S A. 2004;101:7329–34.

    Article  PubMed  CAS  Google Scholar 

  10. Butler G, Rasmussen MD, Lin MF, et al. Evolution of pathogenicity and sexual reproduction in eight Candida genomes. Nature. 2009;459:657–62.

    Article  PubMed  CAS  Google Scholar 

  11. Nierman WC, Pain A, Anderson MJ, et al. Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus. Nature. 2005;438:1151–6.

    Article  PubMed  CAS  Google Scholar 

  12. Pel HJ, de Winde JH, Archer DB, et al. Genome sequencing and analysis of the versatile cell factory Aspergillus niger. CBS 513.88. Nat Biotechnol. 2007;25:221–31.

    Article  PubMed  Google Scholar 

  13. Loftus BJ, Fung E, Roncaglia P, et al. The genome of the basidiomycetous yeast and human pathogen Cryptococcus neoformans. Science. 2005;307:1321–4.

    Article  PubMed  Google Scholar 

  14. Abadio AK, Kioshima ES, Teixeira MM, et al. Comparative genomics allowed the identification of drug targets against human fungal pathogens. BMC Genomics. 2011;12:75.

    Article  PubMed  CAS  Google Scholar 

  15. Cheng SC, Joosten LA, Kullberg BJ, et al. Interplay between Candida albicans and the mammalian innate host defense. Infect Immun. 2012;80:1304–13.

    Article  PubMed  CAS  Google Scholar 

  16. • Silva S, Negri M, Henriques M, et al. Candida glabrata, Candida parapsilosis and Candida tropicalis: biology, epidemiology, pathogenicity and antifungal resistance. FEMS Microbiol Rev. 2012;36:2880–305. Review of biology of Candida albicans, C. parapsilosis and C. glabrata, including recent information about epidemiology and methods for laboratory identification.

    Article  Google Scholar 

  17. Jackson AP, Gamble JA, Yeomans T, et al. Comparative genomics of the fungal pathogens Candida dubliniensis and Candida albicans. Genome Res. 2009;19:2231–44.

    Article  PubMed  CAS  Google Scholar 

  18. • Riccombeni A, Vidanes G, Proux-Wéra E, et al. Sequence and analysis of the genome of the pathogenic yeast Candida orthopsilosis. PLoS One. 2012;7:e35750. Recent example of hybrid de novo genome assembly, combining Sanger, 454 and Illumina sequencing technologies.

    Article  PubMed  CAS  Google Scholar 

  19. Pfaller MA, Diekema DJ. Epidemiology of invasive mycoses in North America. Crit Rev Microbiol. 2010;36:1–53.

    Article  PubMed  Google Scholar 

  20. Tavanti A, Davidson AD, Gow NA, et al. Candida orthopsilosis and Candida metapsilosis spp. nov. to replace Candida parapsilosis groups II and III. J Clin Microbiol. 2005;43:284–92.

    Article  PubMed  CAS  Google Scholar 

  21. Gomez-Lopez A, Alastruey-Izquierdo A, Rodriguez D, et al. Prevalence and susceptibility profile of Candida metapsilosis and Candida orthopsilosis: results from population-based surveillance of candidemia in Spain. Antimicrob Agents Chemother. 2008;52:1506–9.

    Article  PubMed  CAS  Google Scholar 

  22. Tay ST, Na SL, Chong J. Molecular differentiation and antifungal susceptibilities of Candida parapsilosis isolated from patients with bloodstream infections. J Med Microbiol. 2009;58:185–91.

    Article  PubMed  CAS  Google Scholar 

  23. Zordan RE, Galgoczy DJ, Johnson AD. Epigenetic properties of white-opaque switching in Candida albicans are based on a self-sustaining transcriptional feedback loop. Proc Natl Acad Sci U S A. 2006;1032:12807–12.

    Article  Google Scholar 

  24. Zhang A, Petrov KO, Hyun ER, et al. The Tlo proteins are stoichiometric components of Candida albicans mediator anchored via the Med3 subunit. Eukaryot Cell. 2012;11:874–84.

    Article  PubMed  CAS  Google Scholar 

  25. Silva AP, Miranda IM, Guida A, et al. Transcriptional profiling of azole-resistant Candida parapsilosis strains. Antimicrob Agents Chemother. 2011;55:3546–56.

    Article  PubMed  CAS  Google Scholar 

  26. • Achterman RR, White TC. A foot in the door for dermatophyte research. PLoS Pathog. 2012;8:e1002564. Description of ongoing experiments in genome sequencing and transcriptional profiling of dermatophyte species.

    Article  PubMed  Google Scholar 

  27. •• Burmester A, Shelest E, Glöckner G, et al. Comparative and functional genomics provide insights into the pathogenicity of dermatophytic fungi. Genome Biol. 2011;12:R7. Example of integration of RNA-seq, secretome analysis and comparative genomics in the analysis of virulence in dermatophytes.

    Article  PubMed  CAS  Google Scholar 

  28. Roemer T, Jiang B, Davison J, et al. Large-scale essential gene identification in Candida albicans and applications to antifungal drug discovery. Mol Microbiol. 2003;50:167–81.

    Article  PubMed  CAS  Google Scholar 

  29. Hu W, Sillaots S, Lemieux S, et al. Essential gene identification and drug target prioritization in Aspergillus fumigatus. PLoS Pathog. 2007;3:e24.

    Article  PubMed  Google Scholar 

  30. Buurman ET, Westwater C, Hube B, et al. Molecular analysis of CaMnt1p, a mannosyl transferase important for adhesion and virulence of Candida albicans. Proc Natl Acad Sci U S A. 1998;95:7670–5.

    Article  PubMed  CAS  Google Scholar 

  31. Jensen-Pergakes KL, Kennedy MA, Lees ND, et al. Sequencing, disruption, and characterization of the Candida albicans sterol methyltransferase (ERG6) gene: drug susceptibility studies in erg6 mutants. Antimicrob Agents Chemother. 1998;42:1160–7.

    PubMed  CAS  Google Scholar 

  32. Williams CH, Arscott LD, Müller S, et al. Thioredoxin reductase two modes of catalysis have evolved. Eur J Biochem. 2000;267:6110–7.

    Article  PubMed  CAS  Google Scholar 

  33. Peñalva MA, Arst Jr HN. Regulation of gene expression by ambient pH in filamentous fungi and yeasts. Microbiol Mol Biol Rev. 2002;66:426–46.

    Article  PubMed  Google Scholar 

  34. Davis D, Edwards Jr JE, Mitchell AP, Ibrahim AS. Candida albicans RIM101 pH response pathway is required for host-pathogen interactions. Infect Immun. 2000;10:5953–9.

    Article  Google Scholar 

  35. Wagener J, Echtenacher B, Rohde M, et al. The putative alpha-1,2-mannosyltransferase AfMnt1 of the opportunistic fungal pathogen Aspergillus fumigatus is required for cell wall stability and full virulence. Eukaryot Cell. 2008;7:1661–73.

    Article  PubMed  CAS  Google Scholar 

  36. Paul SM, Mytelka DS, Dunwiddie CT, et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat Rev Drug Discov. 2010;9:203–14.

    PubMed  CAS  Google Scholar 

  37. Louis E. Saccharomyces cerevisiae: gene annotation and genome variability, state of the art through comparative genomics. Methods Mol Biol. 2011;759:31–40.

    Article  PubMed  CAS  Google Scholar 

  38. Liti G, Schacherer J. The rise of yeast population genomics. C R Biol. 2011;334:612–9.

    Article  PubMed  CAS  Google Scholar 

  39. • Bruno VM, Wang Z, Marjani SL, et al. Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seq. Genome Res. 2010;20:1451–8. Example of the application of strand-specific RNA-seq.

    Article  PubMed  CAS  Google Scholar 

  40. • Tuch BB, Mitrovich QM, Homann OR, et al. The transcriptomes of two heritable cell types illuminate the circuit governing their differentiation. PLoS Genet. 2010;6:e1001070. Characterization of an epigenetic switch in C. albicans using RNA-seq, and identification of nTARs.

    Article  PubMed  Google Scholar 

  41. •• Nobile CJ, Fox EP, Nett JE, et al. A recently evolved transcriptional network controls biofilm development in Candida albicans. Cell. 2012;148:126–38. Outstanding example of integrated analysis, combining animal models, microarrays, RNA-seq, ChIP/chip and computational molecular evolution to describe the core regulatory network for biofilm formation in C. albicans.

    Article  PubMed  CAS  Google Scholar 

  42. Harriott MM, Noverr MC. Importance of Candida-bacterial polymicrobial biofilms in disease. Trends Microbiol. 2011;19:557–63.

    Article  PubMed  CAS  Google Scholar 

  43. Sudbery PE. Growth of Candida albicans hyphae. Nat Rev Microbiol. 2011;9:737–48.

    Article  PubMed  CAS  Google Scholar 

  44. Martin R, Wächtler B, Schaller M, et al. Host–pathogen interactions and virulence-associated genes during Candida albicans oral infections. Int J Med Microbiol. 2011;301:417–22.

    Article  PubMed  CAS  Google Scholar 

  45. Andes D, Nett J, Oschel P, et al. Development and characterization of an in vivo central venous catheter Candida albicans biofilm model. Infect Immun. 2004;72:6023–31.

    Article  PubMed  CAS  Google Scholar 

  46. Nett J, Andes D. Candida albicans biofilm development, modeling a host–pathogen interaction. Curr Opin Microbiol. 2006;9:340–5.

    Article  PubMed  CAS  Google Scholar 

  47. •• Tierney L, Linde J, Müller S, et al. An interspecies regulatory network inferred from simultaneous RNA-seq of Candida albicans invading innate immune cells. Front Microbiol. 2012;3:85. Example of using RNA-seq to profile both host and pathogen response to infection.

    PubMed  Google Scholar 

  48. Diniz SN, Nomizo R, Cisalpino PS, et al. PTX3 function as an opsonin for the dectin-1-dependent internalization of zymosan by macrophages. J Leukoc Biol. 2004;75:649–56.

    Article  PubMed  CAS  Google Scholar 

  49. Feng Q, Zhang Y. The NuRD complex: linking histone modification to nucleosome remodeling. Curr Top Microbiol Immunol. 2003;274:269–90.

    Article  PubMed  CAS  Google Scholar 

  50. Lu X, Kovalev GI, Chang H, et al. Inactivation of NuRD component Mta2 causes abnormal T cell activation and lupus-like autoimmune disease in mice. J Biol Chem. 2008;283:13825–33.

    Article  PubMed  CAS  Google Scholar 

  51. Altwasser R, Linde J, Buyko E, et al. Genome-wide scale-free network inference for Candida albicans. Front Microbiol. 2012;3:51.

    PubMed  Google Scholar 

  52. • Guida A, Lindstädt C, Maguire SL, et al. Using RNA-seq to determine the transcriptional landscape and the hypoxic response of the pathogenic yeast Candida parapsilosis. BMC Genomics. 2011;12:628. First report of RNA-seq analysis in C. parapsilosis, including characterization of the hypoxic response.

    Article  PubMed  CAS  Google Scholar 

  53. Ernst JF, Tielker D. Responses to hypoxia in fungal pathogens. Cell Microbiol. 2009;11:183–90.

    Article  PubMed  CAS  Google Scholar 

  54. Rossignol T, Ding C, Guida A, et al. Correlation between biofilm formation and the hypoxic response in Candida parapsilosis. Eukaryot Cell. 2009;8:550–9.

    Article  PubMed  CAS  Google Scholar 

  55. Synnott JM, Guida A, Mulhern-Haughey S, et al. Regulation of the hypoxic response in Candida albicans. Eukaryot Cell. 2010;9:1734–46.

    Article  PubMed  CAS  Google Scholar 

  56. Vik A, Rine J. Upc2p and Ecm22p, dual regulators of sterol biosynthesis in Saccharomyces cerevisiae. Mol Cell Biol. 2001;21:6395–405.

    Article  PubMed  CAS  Google Scholar 

  57. Marr KA, Carter RA, Crippa F, et al. Epidemiology and outcome of mould infections in hematopoietic stem cell transplant recipients. Clin Infect Dis. 2002;34:909–17.

    Article  PubMed  Google Scholar 

  58. Morgan J, Wannemuehler KA, Marr KA, et al. Incidence of invasive aspergillosis following hematopoietic stem cell and solid organ transplantation: interim results of a prospective multicenter surveillance program. Med Mycol. 2005;43 Suppl 1:S49–58.

    Article  PubMed  Google Scholar 

  59. Bennett JW, Klich M. Mycotoxins. Clin Microbiol Rev. 2003;16:497–516.

    Article  PubMed  CAS  Google Scholar 

  60. Paterson RR, Lima N. Toxicology of mycotoxins. EXS. 2010;100:31–63.

    PubMed  CAS  Google Scholar 

  61. Yu J, Chang PK, Ehrlich KC, et al. Clustered pathway genes in aflatoxin biosynthesis. Appl Environ Microbiol. 2004;70:1253–62.

    Article  PubMed  CAS  Google Scholar 

  62. Yu J, Fedorova ND, Montalbano BG, et al. Tight control of mycotoxin biosynthesis gene expression in Aspergillus flavus by temperature as revealed by RNA-Seq. FEMS Microbiol Lett. 2011;322:145–9.

    Article  PubMed  CAS  Google Scholar 

  63. O'Brian GR, Georgianna DR, Wilkinson JR, et al. The effect of elevated temperature on gene transcription and aflatoxin biosynthesis. Mycologia. 2007;99:232–9.

    Article  PubMed  Google Scholar 

  64. • Gibbons JG, Beauvais A, Beau R, et al. Global transcriptome changes underlying colony growth in the opportunistic human pathogen Aspergillus fumigatus. Eukaryot Cell. 2012;11:68–78. Using RNA-seq to determine the transcriptional profile of Aspergillus biofilms.

    Article  PubMed  CAS  Google Scholar 

  65. Loussert C, Schmitt C, Prevost MC, et al. In vivo biofilm composition of Aspergillus fumigatus. Cell Microbiol. 2010;12:405–10.

    Article  PubMed  CAS  Google Scholar 

  66. Machida M, Asai K, Sano M, et al. Genome sequencing and analysis of Aspergillus oryzae. Nature. 2005;438:1157–61.

    Article  PubMed  Google Scholar 

  67. Wang B, Guo G, Wang C, et al. Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing. Nucleic Acids Res. 2010;38:5075–87.

    Article  PubMed  CAS  Google Scholar 

  68. Garcia-Hermoso D, Janbon G, Dromer F. Epidemiological evidence for dormant Cryptococcus neoformans infection. J Clin Microbiol. 1999;37:3204–9.

    PubMed  CAS  Google Scholar 

  69. Brizendine KD, Baddley JW, Pappas PG. Pulmonary cryptococcosis. Semin Respir Crit Care Med. 2011;32:727–34.

    Article  PubMed  Google Scholar 

  70. Martinez LR, Casadevall A. Cryptococcus neoformans biofilm formation depends on surface support and carbon source and reduces fungal cell susceptibility to heat, cold, and UV light. Appl Environ Microbiol. 2007;73:4592–601.

    Article  PubMed  CAS  Google Scholar 

  71. Chang YC, Miller GF, Kwon-Chung KJ. Importance of a developmentally regulated pheromone receptor of Cryptococcus neoformans for virulence. Infect Immun. 2003;71:4953–60.

    Article  PubMed  CAS  Google Scholar 

  72. Doering TL. How sweet it is! Cell wall biogenesis and polysaccharide capsule formation in Cryptococcus neoformans. Annu Rev Microbiol. 2009;63:223–47.

    Article  PubMed  CAS  Google Scholar 

  73. Rivera J, Feldmesser M, Cammer M, Casadevall A. Organ-dependent variation of capsule thickness in Cryptococcus neoformans during experimental murine infection. Infect Immun. 1998;66:5027–30.

    PubMed  CAS  Google Scholar 

  74. •• Haynes BC, Skowyra ML, Spencer SJ, et al. Toward an integrated model of capsule regulation in Cryptococcus neoformans. PLoS Pathog. 2011;7:e1002411. An example of integrating RNA-seq with ChIP-seq to identify genes directly involved in capsule regulation.

    Article  PubMed  CAS  Google Scholar 

  75. Koutelou E, Hirsch CL, Dent SY. Multiple faces of the SAGA complex. Curr Opin Cell Biol. 2010;22:374–82.

    Article  PubMed  CAS  Google Scholar 

  76. Jung WH, Sham A, White R, et al. Iron regulation of the major virulence factors in the AIDS-associated pathogen Cryptococcus neoformans. PLoS Biol. 2006;4:e410.

    Article  PubMed  Google Scholar 

  77. Carriconde F, Gilgado F, Arthur I, et al. Clonality and α-a recombination in the Australian Cryptococcus gattii VGII population—an emerging outbreak in Australia. PLoS One. 2011;6:e16936.

    Article  PubMed  CAS  Google Scholar 

  78. Galanis E, Hoang L, Kibsey P, et al. Clinical presentation, diagnosis and management of Cryptococcus gattii cases: lessons learned from British Columbia. Can J Infect Dis Med Microbiol. 2009;20:23–8.

    PubMed  Google Scholar 

  79. Bovers M, Hagen F, Boekhout T. Diversity of the Cryptococcus neoformans-Cryptococcus gattii species complex. Rev Iberoam Micol. 2008;25:S4–S12.

    Article  PubMed  Google Scholar 

  80. Chaturvedi V, Nierman WC. Cryptococcus gattii comparative genomics and transcriptomics: a NIH/NIAID white paper. Mycopathologia. 2012;173:367–73.

    Article  PubMed  CAS  Google Scholar 

  81. •• Thompson JF, Milos PM. The properties and applications of single-molecule DNA sequencing. Genome Biol. 2011;12:217. Extensive review of next-generation sequencing, describing benefits, drawbacks and applications of the main technologies.

    PubMed  CAS  Google Scholar 

  82. Rizzo JM, Buck MJ. Key principles and clinical applications of "next-generation" DNA sequencing. Cancer Prev Res (Phila). 2012;5:887–900.

    Article  CAS  Google Scholar 

  83. Levin JZ, Yassour M, Adiconis X, et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods. 2010;7:709–15.

    Article  PubMed  CAS  Google Scholar 

  84. Hansen KD, Irizarry RA, Wu Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics. 2012;13:204–16.

    Article  PubMed  Google Scholar 

  85. Hardcastle TJ, Kelly KA. baySeq: empirical bayesian methods for identifying differential expression in sequence count data. BMC Bioinforma. 2010;11:422.

    Article  Google Scholar 

  86. Wang L, Feng Z, Wang X, et al. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136–8.

    Article  PubMed  Google Scholar 

  87. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    Article  PubMed  CAS  Google Scholar 

  88. Brown JA, Sherlock G, Myers CL, et al. Global analysis of gene function in yeast by quantitative phenotypic profiling. Mol Syst Biol. 2006;2:2006.0001.

    Article  PubMed  Google Scholar 

  89. Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008;18:1851–8.

    Article  PubMed  CAS  Google Scholar 

  90. Jiang H, Wong WH. SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics. 2008;15:2395–6.

    Article  Google Scholar 

  91. Jiang H, Wong WH. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics. 2009;25:1026–32.

    Article  PubMed  CAS  Google Scholar 

  92. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3

    Google Scholar 

  93. Lo K, Gottardo R. Flexible empirical bayes models for differential gene expression. Bioinformatics. 2007;23:328–35.

    Article  PubMed  CAS  Google Scholar 

  94. Li R, Li Y, Kristiansen K, Wang J. SOAP: short oligonucleotide alignment program. Bioinformatics. 2008;24:713–4.

    Article  PubMed  CAS  Google Scholar 

  95. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100:9440–5.

    Google Scholar 

  96. Kent WJ. BLAT—the BLAST-like alignment tool. Genome Res. 2002;12:656–64.

    PubMed  CAS  Google Scholar 

  97. Zhang M, Gish W. Improved spliced alignment from an information theoretic approach. Bioinformatics. 2006;22:13–20.

    Article  PubMed  Google Scholar 

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Disclosure

G. Butler: received a grant from the Science Foundation of Ireland; A. Riccombeni: none.

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  1. School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland

    Alessandro Riccombeni & Geraldine Butler

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  1. Alessandro Riccombeni
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Riccombeni, A., Butler, G. Role of Genomics and RNA-seq in Studies of Fungal Virulence. Curr Fungal Infect Rep 6, 267–274 (2012). https://doi.org/10.1007/s12281-012-0104-z

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