Advertisement

Functional Genomics

  • Hoe-Han GohEmail author
  • Chyan Leong Ng
  • Kok-Keong Loke
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1102)

Abstract

Functional genomics encompasses diverse disciplines in molecular biology and bioinformatics to comprehend the blueprint, regulation, and expression of genetic elements that define the physiology of an organism. The deluge of sequencing data in the postgenomics era has demanded the involvement of computer scientists and mathematicians to create algorithms, analytical software, and databases for the storage, curation, and analysis of biological big data. In this chapter, we discuss on the concept of functional genomics in the context of systems biology and provide examples of its application in human genetic disease studies, molecular crop improvement, and metagenomics for antibiotic discovery. An overview of transcriptomics workflow and experimental considerations is also introduced. Lastly, we present an in-house case study of transcriptomics analysis of an aromatic herbal plant to understand the effect of elicitation on the biosynthesis of volatile organic compounds.

Keywords

Crop genomics Genomic medicine Metagenomics Pharmacogenomics RNA-Seq Sequencing Transcriptomics 

References

  1. 1.
    Winkler H (1920) Verbreitung und Ursache der Parthenogenesis im Pflanzen- und Tierreiche. Verlag Von Gustav Fischer, JenaCrossRefGoogle Scholar
  2. 2.
    Kaul S et al (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815CrossRefGoogle Scholar
  3. 3.
    Adams MD et al (2000) The genome sequence of Drosophila melanogaster. Science 287:2185–2195CrossRefGoogle Scholar
  4. 4.
    Lander ES et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921CrossRefGoogle Scholar
  5. 5.
    Craig Venter J et al (2001) The sequence of the human genome. Science 291:1304–1351CrossRefGoogle Scholar
  6. 6.
    Waterston RH et al (2002) Initial sequencing and comparative analysis of the mouse genome. Nature 420:520–562CrossRefGoogle Scholar
  7. 7.
    Auton A et al (2015) A global reference for human genetic variation. Nature 526:68–74CrossRefGoogle Scholar
  8. 8.
    Harrow J et al (2012) GENCODE: the reference human genome annotation for the ENCODE project. Genome Res 22:1760–1774CrossRefGoogle Scholar
  9. 9.
    Ziller MJ et al (2013) Charting a dynamic DNA methylation landscape of the human genome. Nature 500:477–481CrossRefGoogle Scholar
  10. 10.
    Hawkins RD, Hon GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev Genet 11:476–486CrossRefGoogle Scholar
  11. 11.
    Koepfli KP, Paten B, O'Brien SJ, Genome KC o S (2015) The genome 10K project: a way forward. Annu Rev Anim Biosci 3:57–111CrossRefGoogle Scholar
  12. 12.
    Sandoval J, Esteller M (2012) Cancer epigenomics: beyond genomics. Curr Opin Genet Dev 22:50–55CrossRefGoogle Scholar
  13. 13.
    Ashburner M et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29CrossRefGoogle Scholar
  14. 14.
    Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277–D280CrossRefGoogle Scholar
  15. 15.
    Purcell S et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CrossRefGoogle Scholar
  16. 16.
    Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:14863–14868CrossRefGoogle Scholar
  17. 17.
    Nielsen CB, Cantor M, Dubchak I, Gordon D, Wang T (2010) Visualizing genomes: techniques and challenges. Nat Methods 7:S5–S15CrossRefGoogle Scholar
  18. 18.
    Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192CrossRefGoogle Scholar
  19. 19.
    Krzywinski M et al (2009) Circos: an information aesthetic for comparative genomics. Genome Res 19:1639–1645CrossRefGoogle Scholar
  20. 20.
    Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 294:93–96CrossRefGoogle Scholar
  21. 21.
    Kersten RD et al (2013) Glycogenomics as a mass spectrometry-guided genome-mining method for microbial glycosylated molecules. Proc Natl Acad Sci U S A 110:E4407–E4416CrossRefGoogle Scholar
  22. 22.
    Waters MD, Fostel JM (2004) Toxicogenomics and systems toxicology: aims and prospects. Nat Rev Genet 5:936–948CrossRefGoogle Scholar
  23. 23.
    Kanehisa M et al (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354–D357CrossRefGoogle Scholar
  24. 24.
    Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690CrossRefGoogle Scholar
  25. 25.
    Shapiro E, Biezuner T, Linnarsson S (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat Rev Genet 14:618–630CrossRefGoogle Scholar
  26. 26.
    Handelsman J (2004) Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev 68:669–685CrossRefGoogle Scholar
  27. 27.
    Ellegren H (2008) Comparative genomics and the study of evolution by natural selection. Mol Ecol 17:4586–4596CrossRefGoogle Scholar
  28. 28.
    Delsuc F, Brinkmann H, Philippe H (2005) Phylogenomics and the reconstruction of the tree of life. Nat Rev Genet 6:361–375CrossRefGoogle Scholar
  29. 29.
    Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:95–108CrossRefGoogle Scholar
  30. 30.
    McCarthy JJ, McLeod HL, Ginsburg GS (2013) Genomic medicine: a decade of successes, challenges, and opportunities. Sci Transl Med 5:189sr4CrossRefGoogle Scholar
  31. 31.
    McCarthy MI et al (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:356–369CrossRefGoogle Scholar
  32. 32.
    Conesa A, Mortazavi A (2014) The common ground of genomics and systems biology. BMC Syst Biol 8:S1CrossRefGoogle Scholar
  33. 33.
    Ideker T, Galitski T, Hood L (2001) A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2:343–372CrossRefGoogle Scholar
  34. 34.
    Rhoads A, Au KF (2015) PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13:278–289CrossRefGoogle Scholar
  35. 35.
    Wilson BJ, Nicholls SG (2015) The human genome project, and recent advances in personalized genomics. Risk Manage Healthc Policy 8:9–20CrossRefGoogle Scholar
  36. 36.
    Shastry BS (2009) Single nucleotide polymorphisms. Springer, Berlin, pp 3–22CrossRefGoogle Scholar
  37. 37.
    Orkin S, Antonarakis S, Kazazian H (1984) Base substitution at position-88 in a beta-thalassemic globin gene. Further evidence for the role of distal promoter element ACACCC. J Biol Chem 259:8679–8681PubMedGoogle Scholar
  38. 38.
    Bond GL et al (2004) A single nucleotide polymorphism in the MDM2 promoter attenuates the p53 tumor suppressor pathway and accelerates tumor formation in humans. Cell 119:591–602CrossRefGoogle Scholar
  39. 39.
    Horn S et al (2013) TERT promoter mutations in familial and sporadic melanoma. Science 339:959–961CrossRefGoogle Scholar
  40. 40.
    Madelaine R et al (2018) A screen for deeply conserved non-coding GWAS SNPs uncovers a MIR-9-2 functional mutation associated to retinal vasculature defects in human. Nucleic Acids Res 46:3517–3531CrossRefGoogle Scholar
  41. 41.
    Janssens ACJW, van Duijn CM (2008) Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet 17:R166–R173CrossRefGoogle Scholar
  42. 42.
    Gurdasani D et al (2015) The African genome variation project shapes medical genetics in Africa. Nature 517:327–332CrossRefGoogle Scholar
  43. 43.
    Goff SA et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296:92–100CrossRefGoogle Scholar
  44. 44.
    Yu J et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296:79–92CrossRefGoogle Scholar
  45. 45.
    Schnable PS et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115CrossRefGoogle Scholar
  46. 46.
    Schmutz J et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463:178–183CrossRefGoogle Scholar
  47. 47.
    Xu X et al (2011) Genome sequence and analysis of the tuber crop potato. Nature 475:189–195CrossRefGoogle Scholar
  48. 48.
    Brenchley R et al (2012) Analysis of the bread wheat genome using whole-genome shotgun sequencing. Nature 491:705–710CrossRefGoogle Scholar
  49. 49.
    Singh R et al (2013) Oil palm genome sequence reveals divergence of interfertile species in Old and New worlds. Nature 500:335–339CrossRefGoogle Scholar
  50. 50.
    Rahman AYA et al (2013) Draft genome sequence of the rubber tree Hevea brasiliensis. BMC Genomics 14:75CrossRefGoogle Scholar
  51. 51.
    He J et al (2014) Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 5:484CrossRefGoogle Scholar
  52. 52.
    Ong-Abdullah M et al (2015) Loss of Karma transposon methylation underlies the mantled somaclonal variant of oil palm. Nature 525:533CrossRefGoogle Scholar
  53. 53.
    Rinaldo AR, Ayliffe M (2015) Gene targeting and editing in crop plants: a new era of precision opportunities. Mol Breed 35:1–15CrossRefGoogle Scholar
  54. 54.
    Wang Y et al (2014) Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat Biotechnol 32:947–951CrossRefGoogle Scholar
  55. 55.
    Jiang W et al (2013) Demonstration of CRISPR/Cas9/sgRNA-mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Res 41:e188CrossRefGoogle Scholar
  56. 56.
    Lawrenson T et al (2015) Induction of targeted, heritable mutations in barley and Brassica oleracea using RNA-guided Cas9 nuclease. Genome Biol 16:258CrossRefGoogle Scholar
  57. 57.
    Svitashev S et al (2015) Targeted mutagenesis, precise gene editing and site-specific gene insertion in maize using Cas9 and guide RNA. Plant Physiol:00793.02015, 169(2):931–945CrossRefGoogle Scholar
  58. 58.
    Li Z et al (2015) Cas9-guide RNA directed genome editing in soybean. Plant Physiol:00783.02015, 169(2):960–970CrossRefGoogle Scholar
  59. 59.
    Gao C (2018) The future of CRISPR technologies in agriculture. Nat Rev Mol Cell Biol 39:1–2Google Scholar
  60. 60.
    Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM (1998) Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chem Biol 5:R245–R249CrossRefGoogle Scholar
  61. 61.
    Davies J (1999) Millennium bugs. Trends Genet 15:M2–M5CrossRefGoogle Scholar
  62. 62.
    Rappé MS, Giovannoni SJ (2003) The uncultured microbial majority. Annu Rev Microbiol 57:369–394CrossRefGoogle Scholar
  63. 63.
    Achtman M, Wagner M (2008) Microbial diversity and the genetic nature of microbial species. Nat Rev Microbiol 6:431–440CrossRefGoogle Scholar
  64. 64.
    Ling LL et al (2015) A new antibiotic kills pathogens without detectable resistance. Nature 517:455CrossRefGoogle Scholar
  65. 65.
    MacNeil I et al (2001) Expression and isolation of antimicrobial small molecules from soil DNA libraries. J Mol Microbiol Biotechnol 3:301–308PubMedGoogle Scholar
  66. 66.
    Gillespie DE et al (2002) Isolation of antibiotics turbomycin A and B from a metagenomic library of soil microbial DNA. Appl Environ Microbiol 68:4301–4306CrossRefGoogle Scholar
  67. 67.
    Brady SF, Clardy J (2004) Palmitoylputrescine, an antibiotic isolated from the heterologous expression of DNA extracted from bromeliad tank water. J Nat Prod 67:1283–1286CrossRefGoogle Scholar
  68. 68.
    Oyama LB et al (2017) Buwchitin: a ruminal peptide with antimicrobial potential against Enterococcus faecalis. Front Chem 5:51CrossRefGoogle Scholar
  69. 69.
    Nasrin S et al (2018) Chloramphenicol derivatives with antibacterial activity identified by functional metagenomics. J Nat Prod 81:1321CrossRefGoogle Scholar
  70. 70.
    Hover BM et al (2018) Culture-independent discovery of the malacidins as calcium-dependent antibiotics with activity against multidrug-resistant Gram-positive pathogens. Nat Microbiol 3:415CrossRefGoogle Scholar
  71. 71.
    Li B et al (2015) Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME J 9:2490–2502CrossRefGoogle Scholar
  72. 72.
    Forsberg KJ et al (2012) The shared antibiotic resistome of soil bacteria and human pathogens. Science 337:1107–1111CrossRefGoogle Scholar
  73. 73.
    Wilson MC et al (2014) An environmental bacterial taxon with a large and distinct metabolic repertoire. Nature 506:58–62CrossRefGoogle Scholar
  74. 74.
    Hrdlickova R, Toloue M, Tian B (2017) RNA-seq methods for transcriptome analysis. Wiley Interdiscip Rev RNA 8. https://doi.org/10.1002/wrna.1364 CrossRefGoogle Scholar
  75. 75.
    Ma X, Tang Z, Qin J, Meng Y (2015) The use of high-throughput sequencing methods for plant microRNA research. RNA Biol 12:709–719CrossRefGoogle Scholar
  76. 76.
    Aviner R, Geiger T, Elroy-Stein O (2013) PUNCH-P for global translatome profiling: methodology, insights and comparison to other techniques. Translation 1:e27516CrossRefGoogle Scholar
  77. 77.
    Li W et al (2015) Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis. BMC Genomics 16:1069CrossRefGoogle Scholar
  78. 78.
    Saliba A-E, Westermann AJ, Gorski SA, Vogel J (2014) Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res 42:8845–8860CrossRefGoogle Scholar
  79. 79.
    Dominissini D (2014) Roadmap to the epitranscriptome. Science 346:1192CrossRefGoogle Scholar
  80. 80.
    Lamarre S et al (2018) Optimization of an RNA-seq differential gene expression analysis depending on biological replicate number and library size. Front. Plant Sci. 9:108Google Scholar
  81. 81.
    Ching T, Huang S, Garmire LX (2014) Power analysis and sample size estimation for RNA-seq differential expression. RNA 20:1684–1696CrossRefGoogle Scholar
  82. 82.
    de Klerk E, den Dunnen JT, ‘t Hoen PAC (2014) RNA sequencing: from tag-based profiling to resolving complete transcript structure. Cell Mol Life Sci 71:3537–3551Google Scholar
  83. 83.
    Jamaluddin ND, Mohd Noor N, Goh H-H (2017) Genome-wide transcriptome profiling of Carica papaya L. embryogenic callus. Physiol Mol Biol Plants 23:357–368CrossRefGoogle Scholar
  84. 84.
    Conesa A et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13CrossRefGoogle Scholar
  85. 85.
    Griffith M, Walker JR, Spies NC, Ainscough BJ, Griffith OL (2015) Informatics for RNA sequencing: a web resource for analysis on the cloud. PLOS Comput Biol 11:e1004393CrossRefGoogle Scholar
  86. 86.
    Nagasaki H et al (2013) DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. DNA Res 20:383–390CrossRefGoogle Scholar
  87. 87.
    Afgan E et al (2018) The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46:W537–W544CrossRefGoogle Scholar
  88. 88.
    Bair E (2013) Identification of significant features in DNA microarray data. Wiley Interdiscip Rev Comput Stat 5. https://doi.org/10.1002/wics.1260 CrossRefGoogle Scholar
  89. 89.
    An D, Cao HX, Li C, Humbeck K, Wang W (2018) Isoform sequencing and state-of-art applications for unravelling complexity of plant transcriptomes. Genes 9:43CrossRefGoogle Scholar
  90. 90.
    Moll P, Ante M, Seitz A, Reda T (2014) QuantSeq 3′ mRNA sequencing for RNA quantification. Nat Methods 11:972CrossRefGoogle Scholar
  91. 91.
    Martin JA, Wang Z (2011) Next-generation transcriptome assembly. Nat Rev Genet 12:671CrossRefGoogle Scholar
  92. 92.
    Christapher P, Parasuraman S, Christina J, Asmawi MZ, Vikneswaran M (2015) Review on Polygonum minus. Huds, a commonly used food additive in Southeast Asia. Pharm Res 7:1–6Google Scholar
  93. 93.
    Gor MC et al (2011) Identification of cDNAs for jasmonic acid-responsive genes in Polygonum minus roots by suppression subtractive hybridization. Acta Physiol Plant 33:283–294CrossRefGoogle Scholar
  94. 94.
    Roslan ND et al (2012) Flavonoid biosynthesis genes putatively identified in the aromatic plant Polygonum minus via expressed sequences tag (EST) analysis. Int J Mol Sci 13:2692–2706CrossRefGoogle Scholar
  95. 95.
    Ee SF et al (2013) Transcriptome profiling of genes induced by salicylic acid and methyl jasmonate in Polygonum minus. Mol Biol Rep 40:2231–2241CrossRefGoogle Scholar
  96. 96.
    Loke K-K et al (2016) RNA-seq analysis for secondary metabolite pathway gene discovery in Polygonum minus. Genomics Data 7:12–13CrossRefGoogle Scholar
  97. 97.
    Loke KK et al (2017) Transcriptome analysis of Polygonum minus reveals candidate genes involved in important secondary metabolic pathways of phenylpropanoids and flavonoids. Peer J 2017. PeerJ 5:e2938CrossRefGoogle Scholar
  98. 98.
    Rahnamaie-Tajadod R, Loke KK, Goh HH, Noor NM (2017) Differential gene expression analysis in Polygonum minus leaf upon 24h of methyl jasmonate elicitation. Front Plant Sci 8:109CrossRefGoogle Scholar
  99. 99.
    Nazaruddin N et al (2017) Small RNA-seq analysis in response to methyl jasmonate and abscisic acid treatment in Persicaria minor. Genomics Data 12:157–158CrossRefGoogle Scholar
  100. 100.
    Song AAL et al (2012) Overexpressing 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) in the lactococcal mevalonate pathway for heterologous plant sesquiterpene production. PLOS ONE 7:e52444CrossRefGoogle Scholar
  101. 101.
    Ee SF et al (2014) Functional characterization of sesquiterpene synthase from Polygonum minus. Sci World J 2014:840592CrossRefGoogle Scholar
  102. 102.
    Ker DS et al (2017) Purification and biochemical characterization of recombinant Persicaria minor β-sesquiphellandrene synthase. PeerJ 5:e2961Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Systems BiologyUniversiti Kebangsaan Malaysia (UKM)BangiMalaysia

Personalised recommendations