Advertisement

Single-Cell Genomics and Metagenomics for Microbial Diversity Analysis

  • Rama Kant Dubey
  • Vishal Tripathi
  • Ratna Prabha
  • Rajan Chaurasia
  • Dhananjaya Pratap Singh
  • Ch. Srinivasa Rao
  • Ali El-Keblawy
  • Purushothaman Chirakkuzhyil Abhilash
Chapter
Part of the SpringerBriefs in Environmental Science book series (BRIEFSENVIRONMENTAL)

Abstract

Soil metagenomic analysis was previously limited by technological restrictions and the few reference genomes. The advent of next-generation ‘omics’ technologies has provided high-throughput methods for analysing community structure and reconstructing soil metagenomes. High-throughput sequencing technology and single-cell genomics have revolutionized metagenomic analysis by enabling large-scale sequencing at reduced sequencing costs with less time required. In the present chapter we discuss various technological advances in metagenomics, their processes and the methods of data analysis, and metagenomic success stories under various environments that can be applied for studying the functional and structural diversity of soil microorganisms.

Keywords

Functional annotation Microbial community structure Next-generation sequencing (NGS) technology Single-cell genomics Metagenome 

References

  1. Abulencia CB, Wyborski DL, Garcia JA, Podar M, Chen W, Chang SH, Chang HW, Watson D, Brodie EL, Hazen TC, Keller M (2006) Environmental whole-genome amplification to access microbial populations in contaminated sediments. Appl Environ Microbiol 72:3291–3301CrossRefGoogle Scholar
  2. Aguiar-Pulido V, Huang W, Suarez-Ulloa V, Cickovski T, Mathee K, Narasimhan G (2016) Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis: supplementary issue: bioinformatics methods and applications for big metagenomics data. Evol Bioinform 12:EBO-S36436CrossRefGoogle Scholar
  3. Akinsemolu AA (2018) The role of microorganisms in achieving the sustainable development Goals. J Clean Prod 182:139–155CrossRefGoogle Scholar
  4. Amann R, Fuchs BM (2008) Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat Rev Microbiol 6:339–348Google Scholar
  5. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Meyer F et al. (2008) The RAST Server: rapid annotations using subsystems technology. BMC genomics, 9(1): 75  https://doi.org/10.1186/1471-2164-9-75CrossRefGoogle Scholar
  6. Bai Y, Liang J, Liu R, Hu C, Qu J (2014) Metagenomic analysis reveals microbial diversity and function in the rhizosphere soil of a constructed wetland. Environ Technol 35:2521–2527CrossRefGoogle Scholar
  7. Bashir Y, Singh SP, Konwar BK (2014) Metagenomics: an application based perspective. Chin J Biol.  https://doi.org/10.1155/2014/146030CrossRefGoogle Scholar
  8. Berg G, Raaijmakers JM (2018) Saving seed microbiomes. ISME J 12:1167–1170CrossRefGoogle Scholar
  9. Blainey PC (2012) The future is now: single-cell genomics of bacteria and archaea.  https://doi.org/10.1111/1574-6976.12015CrossRefGoogle Scholar
  10. Brady A, Salzberg SL (2009) Phymm and Phymm BL: metagenomic phylogenetic classification with interpolated Markov models. Nat Methods 6:673–676CrossRefGoogle Scholar
  11. Carr R, Borenstein E (2014) Comparative analysis of functional metagenomic annotation and the mappability of short reads. PLoS One 9:e105776CrossRefGoogle Scholar
  12. Charuvaka A, Rangwala H (2011) Evaluation of short read metagenomic assembly. BMC Genomics 12:S8CrossRefGoogle Scholar
  13. Chen IMA, Markowitz VM, Chu K, Palaniappan K, Szeto E, Pillay M, Ratner A, Huang J, Andersen E, Huntemann M, Varghese N, Hadjithomas M, Tennessen K, Nielsen T, Ivanova NN, Kyrpides NC (2017) IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res 4:D507–D516CrossRefGoogle Scholar
  14. Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, Kulam-Syed-Mohideen AS, McGarrell DM, Marsh T, Garrity GM, Tiedje JM (2009) The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 37:D141–D145CrossRefGoogle Scholar
  15. Cong J, Yang Y, Liu X, Lu H, Liu X, Zhou J, Li D, Yin H, Ding J, Zhang Y (2015) Analyses of soil microbial community compositions and functional genes reveal potential consequences of natural forest succession. Sci Rep-UK 5:10007CrossRefGoogle Scholar
  16. Creer S, Deiner K, Frey S, Porazinska D, Taberlet P, Thomas WK, Potter C, Bik HM (2016) The ecologist’s field guide to sequence-based identification of biodiversity. Methods Ecol Evol 7:1008–1018CrossRefGoogle Scholar
  17. Darling AE, Jospin G, Lowe E, Matsen FA, Bik HM, Eisen JA (2014) Phylogenetic analysis of genomes and metagenomes. PeerJ 2:e243CrossRefGoogle Scholar
  18. De Bourcy, CF, De Vlaminck I, Kanbar JN, Wang J, Gawad C, Quake SR (2014) A quantitative comparison of singlecell whole genome amplification methods. PloS one, 9(8), e105585.  https://doi.org/10.1371/journal.pone.0105585.CrossRefGoogle Scholar
  19. Deleye L, Tilleman L, Vander Plaetsen AS, Cornelis S, Deforce D, Van Nieuwerburgh F (2017) Performance of four modern whole genome amplification methods for copy number variant detection in single cells. Sci. Rep. 7(1): 3422Google Scholar
  20. Dhillon V, Li X (2015) Single-cell genome sequencing for viral-host interactions. J Comput Sci Syst Biol 8:160–165Google Scholar
  21. Dröge J, McHardy AC (2012) Taxonomic binning of metagenome samples generated by next-generation sequencing technologies. Brief Bioinform 13:646–655CrossRefGoogle Scholar
  22. Ekblom R, Wolf JBW (2014) A field guide to whole-genome sequencing, assembly and annotation. Evol Appl 7:1026–1042CrossRefGoogle Scholar
  23. Emmert-Buck MR et al (1996) Laser capture microdissection. Science 274:998–1001CrossRefGoogle Scholar
  24. Filippo CD, Ramazzotti M, Fontana P, Cavalieri D (2012) Bioinformatic approaches for functional annotation and pathway inference in metagenomics data. Brief Bioinform 13:696–710CrossRefGoogle Scholar
  25. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, Heger A, Hetherington K, Holm L, Mistry J, Sonnhammer EL, Tate J, Punta M (2014) the protein families database. Nucleic Acids Res 42:D222–D230CrossRefGoogle Scholar
  26. Gawad C, Koh W, Quake SR (2016) Single-cell genome sequencing: current state of the science. Nat Rev Genet 3:175–188CrossRefGoogle Scholar
  27. Gilbert JA, Meyer F, Bailey MJ (2011) The future of microbial metagenomics (or is ignorance bliss?). ISME J 5:777–779CrossRefGoogle Scholar
  28. Gilbert JA, Jansson JK, Knight R (2014) The Earth Microbiome project: successes and aspirations. BMC Biology, 12(1), 69.  https://doi.org/10.1186/s12915-014-0069-1
  29. Gole J et al (2013) Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells. Nat Biotechnol 31:1126–1132CrossRefGoogle Scholar
  30. Guo J, Cole JR, Zhang Q, Brown CT, Tiedje JM (2016) Microbial community analysis with ribosomal gene fragments from shotgun metagenomes. Appl Environ Microbiol 82:157–166CrossRefGoogle Scholar
  31. Haft DH, Selengut JD, White O (2003) The TIGRFAMs database of protein families. Nucleic Acids Res 1:371–373CrossRefGoogle Scholar
  32. Ham RG (1965) Clonal growth of mammalian cells in a chemically defined, synthetic medium. Proc Natl Acad Sci U S A 53:288–293CrossRefGoogle Scholar
  33. Handelsman J (2004) The ecologist’s field guide to sequence-based identification of biodiversity. Microbiol Mol Biol Rev 68:669–685CrossRefGoogle Scholar
  34. Hanning I, Diaz-Sanchez S (2015) The functionality of the gastrointestinal microbiome in non-human animals. Microbiome 3:51CrossRefGoogle Scholar
  35. Henson J, Tischler G, Ning Z (2012) Next-generation sequencing and large genome assemblies. Pharmacogenomics 13:901–915CrossRefGoogle Scholar
  36. Higashi S, Barreto AMS, Cantão ME, Vasconcelos ATR (2012) Analysis of composition-based metagenomic classification. BMC Genomics 13:S1CrossRefGoogle Scholar
  37. Hooper SD, Dalevi D, Pati A, Mavromatis K, Ivanova NN, Kyrpides NC (2010) Estimating DNA coverage and abundance in metagenomes using a gamma approximation. Bioinformatics 26:295–301CrossRefGoogle Scholar
  38. Hou Y, Song L, Zhu P, Zhang B, Tao Y, Xu X,Wu H (2012) Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell, 148(5): 873–885CrossRefGoogle Scholar
  39. Hunter S, Corbett M, Denise H, Fraser M, Gonzalez-Beltran A, Hunter C, Jones P, Leinonen R, McAnulla C, Maguire E, Maslen J, Mitchell A, Nuka G, Oisel A, Pesseat S, Radhakrishnan R, Rocca-Serra P, Scheremetjew M, Sterk P, Vaughan D, Cochrane G, Field D, Sansone SA (2014a) metagenomics-a new resource for the analysis and archiving of metagenomic data. Nucleic Acids Res 42:D600–D606CrossRefGoogle Scholar
  40. Hunter S, Corbett M, Denise H, Fraser M, Gonzalez-Beltran A, Hunter C, Jones P, Leinonen R, McAnulla C, Maguire E, Maslen J, Mitchell A, Nuka G, Oisel A, Pesseat S, Radhakrishnan R, Rocca-Serra P, Scheremetjew M, Sterk P, Vaughan D, Cochrane G, Field D, Sansone SA (2014b) EBI metagenomics-a new resource for the analysis and archiving of metagenomic data. Nucleic Acids Res 42:D600–D606CrossRefGoogle Scholar
  41. Huson DH, Auch AF, Qi J, Schuster SC (2007) MEGAN analysis of metagenomic data. Genome Res 17:377–386CrossRefGoogle Scholar
  42. Iwasaki Y, Abe T, Wada K, Wada Y, Ikemura T (2013) A novel bioinformatics strategy to analyze microbial big sequence data for efficient knowledge discovery: batch-learning self-organizing map (BLSOM). Microorganisms 1:137–157CrossRefGoogle Scholar
  43. Jansson J (2011) Soil microbes: metagenomic approaches. https://eesa.lbl.gov/soil-microbes-metagenomic-approaches/2011
  44. Kalisky T, Quake SR (2011) Single-cell genomics. Nat Methods 8:311–314CrossRefGoogle Scholar
  45. Kamke J, Bayer K, Woyke T, Hentschel U (2012) Exploring symbioses by single-cell genomics. Biol Bull 223:30–43CrossRefGoogle Scholar
  46. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30CrossRefGoogle Scholar
  47. Keegan KP, Glass EM, Meyer F (2016) MG-RAST, a metagenomics service for analysis of microbial community structure and function. Methods Mol Biol 1399:207–233CrossRefGoogle Scholar
  48. Kembel SW, Eisen JA, Pollard KS, Green JL (2011) The phylogenetic diversity of metagenomes. PLoS One 6:e23214CrossRefGoogle Scholar
  49. Kind J, Pagie L, Ortabozkoyun H, Boyle S, de Vries SS et al (2013) Single-cell dynamics of genome-nuclear lamina interactions. Cell 153:178–192CrossRefGoogle Scholar
  50. Kunin V, Copeland A, Lapidus A, Mavromatis K, Hugenholtz P (2008) A bioinformatician’s guide to metagenomics. Microbiol Mol Biol Rev 72:557–578CrossRefGoogle Scholar
  51. Landry ZC, Giovanonni SJ, Quake SR, Blainey PC (2013) Optofluidic cell selection from complex microbial communities for single-genome analysis. Methods Enzymol 531:61–90CrossRefGoogle Scholar
  52. Lasken RS (2012) Genomic sequencing of uncultured microorganisms from single cells. Nat Rev Microbiol 10:631–640.  https://doi.org/10.1038/nrmicro2857CrossRefGoogle Scholar
  53. Lecault V, White AK, Singhal A, Hansen CL (2012) Microfluidic single cell analysis: from promise to practice. Curr Opin Chem Biol 16:381–390CrossRefGoogle Scholar
  54. Leung K et al (2012) A programmable droplet-based microfluidic device applied to multiparameter analysis of single microbes and microbial communities. Proc Natl Acad Sci U S A 109:7665–7670CrossRefGoogle Scholar
  55. Leung ML, Wang Y, Waters J, Navin NES (2015) single nucleus exome sequencing. Genome Biol 16:55CrossRefGoogle Scholar
  56. Li H-Y et al (2012) Endophytes and their role in phytoremediation. Fungal Divers 54:11–18CrossRefGoogle Scholar
  57. Lichter P, Ledbetter SA, Ledbetter DH, Ward DC (1990) Fluorescence in situ hybridization with Alu and L1 polymerase chain reaction probes for rapid characterization of human chromosomes in hybrid cell lines. Proc Natl Acad Sci U S A 87:6634–6638CrossRefGoogle Scholar
  58. Lin HH, Liao YC (2016) Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep 6:24175CrossRefGoogle Scholar
  59. Lovett M (2013) The applications of single-cell genomics. Hum Mol Genet 22:R22–R26CrossRefGoogle Scholar
  60. Macaulay IC, Voet T (2014) Single cell genomics: advances and future perspectives. PLoS Genet 10:e1004126CrossRefGoogle Scholar
  61. Macosko EZ et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–1214CrossRefGoogle Scholar
  62. Maldonado-Mendoza IE, Galindo-flores H, Lopez-meyer M (2009) An introduction to metagenomics. In: Chauhan AK, Varma A (eds) A textbook of molecular biotechnology. I. K. International Publishing House Pvt Ltd, New DelhiGoogle Scholar
  63. Marcy Y, Ishoey T, Lasken RS, Stockwell TB, Walenz BP, Halpern AL, Beeson KY, Goldberg SMD, Quake SR (2007) Nanoliter reactors improve multiple displacement amplification of genomes from single cells. PLoS Genet 3:1702–1708CrossRefGoogle Scholar
  64. Martínez-García M, Santos F, Moreno-Paz M, Parro V, Antón J (2014) Unveiling viral-host interactions within the ‘microbial dark matter’. Nat Commun 5:4542CrossRefGoogle Scholar
  65. Mathé C, Sagot MF, Schiex T, Rouzé P (2002) Survey and summary: current methods of gene prediction, their strengths and weaknesses. Nucleic Acids Res 30:4103–4117CrossRefGoogle Scholar
  66. McHardy AC, Martin HG, Tsirigos A, Hugenholtz P, Rigoutsos I (2007) Accurate phylogenetic classification of variable-length DNA fragments. Nat Methods 4(63–72):10Google Scholar
  67. Melcher U, Verma R, Schneider WL (2014) Metagenomic search strategies for interactions among plants and multiple microbes. Front Plant Sci 5:268CrossRefGoogle Scholar
  68. Mendoza MLZ, Sicheritz-Pontén T, Gilbert MTP (2015) Environmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses. Brief Bioinform 16:745–758CrossRefGoogle Scholar
  69. Miller JR, Koren S, Sutton G (2010) Assembly algorithms for next-generation sequencing data. Genomics 95:315–327CrossRefGoogle Scholar
  70. Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Verezemska O, Isbandi M, Thomas AD, Ali R, Sharma K, Kyrpides NC, Reddy TB (2017) Data updates and feature enhancements. Nucleic Acids Res 45:D446–D456CrossRefGoogle Scholar
  71. Muller J, Szklarczyk D, Julien P, Letunic I, Roth A, Kuhn M, Powell S, von Mering C, Doerks T, Jensen LJ, Bork P (2010) extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations. Nucleic Acids Res 38:D190–D195CrossRefGoogle Scholar
  72. Navin N et al (2011) Tumour evolution inferred by single-cell sequencing. Nature 472:90–94CrossRefGoogle Scholar
  73. Overbeek MV, Kusuma WA, Buono A (2013). Clustering metagenome fragments using growing self organizing map. In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 285–289). IEEE.Google Scholar
  74. Perkel JM (2012) Single-cell genomics: defining microbiology’s dark matter. BioTechniques 52:301–303CrossRefGoogle Scholar
  75. Ponomarova O, Patil KR (2015) Metabolic interactions in microbial communities: untangling the Gordian knot. Curr Opin Microbiol 27:37–44CrossRefGoogle Scholar
  76. Prakash O, Sharma R, Rahi P, Karthikeyan N (2014) Role of microorganisms in plant nutrition and health. In: Nutrient use efficiency: from basics to advances, pp 125–161Google Scholar
  77. Pride DT, Schoenfeld T (2008) Genome signature analysis of thermal virus metagenomes reveals Archaea and thermophilic signatures. BMC Genomics 9:420CrossRefGoogle Scholar
  78. Rajendhran J, Gunasekaran P (2011) Microbial phylogeny and diversity: small subunit ribosomal RNA sequence analysis and beyond. Microbiol Res 166:99–110CrossRefGoogle Scholar
  79. Randle-Boggis RJ, Helgason T, Sapp M, Ashton PD (2016) Evaluating techniques for metagenome annotation using simulated sequence data. FEMS Microbiol Ecol 92:95CrossRefGoogle Scholar
  80. Rastogi G, Sani RK (2011) Molecular techniques to assess microbial community structure, function, and dynamics in the environment, pp 29–57Google Scholar
  81. Raynaud X, Nunan N (2014) Spatial ecology of bacteria at the microscale in soil. PLoS One 9:e87217CrossRefGoogle Scholar
  82. Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ et al (2013) Insights into the phylogeny and coding potential of microbial dark matter. Nature 499:431–437CrossRefGoogle Scholar
  83. Rinke C et al (2014) Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat Protoc 9:1038–1048CrossRefGoogle Scholar
  84. Rodriguez-R LM, Konstantinidis KT (2014) Bypassing cultivation to identify bacterial species. Microbe, 9(3): 111–8Google Scholar
  85. Rusch DB, Halpern AL, Sutton G et al (2007) The sorcerer II global ocean sampling expedition: northwest Atlantic through eastern tropical pacific. PLoS Biol 5:e77CrossRefGoogle Scholar
  86. Salzberg SL, Pertea M, Delcher AL, Gardner MJ, Tettelin H (1999) Interpolated Markov models for eukaryotic gene finding. Genomics 59:24–31CrossRefGoogle Scholar
  87. Sebastian R, Kim JY, Kim TH, Lee KT (2013) Metagenomics: a promising approach to assess enzymes biocatalyst for biofuel production. Asian J Biotechnol 5:33–50CrossRefGoogle Scholar
  88. Sedlar K, Kupkova K, Provaznik I (2017) Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Comput Struct Biotechnol J 15:48–55CrossRefGoogle Scholar
  89. Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C (2013) Computational meta’omics for microbial community studies. Mol Syst Biol 9:666.  https://doi.org/10.1038/msb.2013.22CrossRefGoogle Scholar
  90. Seshadri R, Kravitz SA, Smarr L, Gilna P, Frazier M (2007) CAMERA: a community resource for metagenomics. PLoS Biol 5:e75CrossRefGoogle Scholar
  91. Shah N, Tang H, Doak TG, Ye Y (2011) Comparing bacterial communities inferred from 16S rRNA gene sequencing and shotgun metagenomics. Symp Biocomput Pac:165–176.  https://doi.org/10.1142/97898143350580018
  92. Shapiro, B. J. (2017). The population genetics of pangenomes. Nature Microbiology, 2(12), 1574–1574.  https://doi.org/10.1038/s41564-017-0066-6CrossRefGoogle Scholar
  93. Sharon I (2010) Computational methods for metagenomic analysis. Ph.D Thesis, The Technion – Israel Institute of TechnologyGoogle Scholar
  94. Sharpton TJ (2014) An introduction to the analysis of shotgun metagenomic data. Front Plant Sci 5:209CrossRefGoogle Scholar
  95. Shokrall SI, Spall JL, Gibson JF, Hajibabaei M (2012) Mol Ecol 21:1794–1805CrossRefGoogle Scholar
  96. Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP (2014) Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet 2:121–132CrossRefGoogle Scholar
  97. Stanley CE, van der Heijden MG (2017) Microbiome-on-a-Chip: new frontiers in plant-microbiota research. Trends Microbiol 25:610–613CrossRefGoogle Scholar
  98. Stepanauskas R (2012) Single cell genomics: an individual look at microbes. Curr Opin Microbiol 15:613–620CrossRefGoogle Scholar
  99. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Smirnov S, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA (2003) The COG database: an updated version includes eukaryotes. BMC Bioinform 11:41CrossRefGoogle Scholar
  100. Teeling H, Glöckner FO (2012) Current opportunities and challenges in microbial metagenome analysis – a bioinformatic perspective. Brief Bioinform 3:728–742CrossRefGoogle Scholar
  101. Teeling H, Waldmann J, Lombardot T, Bauer M, Glöckner FO (2004) TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinform 5:163CrossRefGoogle Scholar
  102. Telenius H et al (1992) Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics 13:718–725CrossRefGoogle Scholar
  103. Thomas T, Gilbert J, Meyer F (2012) Metagenomics – a guide from sampling to data analysis. Microb Inform Exp 2:3CrossRefGoogle Scholar
  104. Trapnell C (2015) Defining cell types and states with single-cell genomics. Genome Res 10:1491–1498CrossRefGoogle Scholar
  105. Troutt AB, McHeyzer-Williams MG, Pulendran B, Nossal GJ (1992) Ligation-anchored PCR: a simple amplification technique with single-sided specificity. Proc Natl Acad Sci U S A 89:9823–9825CrossRefGoogle Scholar
  106. Tseng CH, Tang SL (2014) Marine microbial metagenomics: from individual to the environment. Int J Mol Sci 15:8878–8892CrossRefGoogle Scholar
  107. Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI (2007) The human microbiome project. Nature, 449(7164): 804-810.  https://doi.org/10.1038/nature06244CrossRefGoogle Scholar
  108. von Wintzingerode F, Gobel UB, Stackebrandt E (1997) Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. FEMS Microbiol Rev 21:213–229CrossRefGoogle Scholar
  109. Walker A, Parkhill J (2008) Single-cell genomics. Nat Rev Microbiol 6:176–177CrossRefGoogle Scholar
  110. Wang Z, Chen Y, Li Y (2004) A brief review of computational gene prediction methods. Genomics Proteomics Bioinformatics 2:216–221CrossRefGoogle Scholar
  111. Wang C, Dong D, Wang H, Müller K, Qin Y, Wang H, Wu W (2016) Metagenomic analysis of microbial consortia enriched from compost: new insights into the role of Actinobacteria in lignocellulose decomposition. Biotechnol Biofuels 9:22CrossRefGoogle Scholar
  112. White JR, Navlakha S, Nagarajan N, Ghodsi MR, Kingsford C, Pop M (2010) Alignment and clustering of phylogenetic markers – implications for microbial diversity studies. BMC Bioinform 11:152CrossRefGoogle Scholar
  113. White AK et al (2011) High-throughput microfluidic singlecell RT-qPCR. Proc Natl Acad Sci U S A 108:13999–14004CrossRefGoogle Scholar
  114. Wooley JC, Ye Y (2009) Metagenomics: facts and artifacts, and computational challenges. J Comput Sci Technol 25:71–81CrossRefGoogle Scholar
  115. Woyke T, Jarett J (2015) Function-driven single-cell genomics. Microb Biotechnol 8:38–39CrossRefGoogle Scholar
  116. Wu M, Eisen JA (2008) A simple, fast, and accurate method of phylogenomic inference. Genome Biol.  https://doi.org/10.1186/gb-2008-9-10-r151CrossRefGoogle Scholar
  117. Wu M, Scott AJ (2012) Phylogenomic analysis of bacterial and archaeal sequences with AMPHORA2. Bioinformatics 28:1033–1034CrossRefGoogle Scholar
  118. Wu G, Feng B, Xu J, Zhu XT, Li YC, Zeng NK, Yang ZL (2014) Molecular phylogenetic analyses redefine seven major clades and reveal 22 new generic clades in the fungal family Boletaceae. Fungal Divers. 69(1): 93–115CrossRefGoogle Scholar
  119. Yooseph S, Sutton G, Rusch DB et al (2007) The sorcerer II global ocean sampling expedition: expanding the universe of protein families. PLoS Biol 5:e16CrossRefGoogle Scholar
  120. Zarraonaindia I, Smith DP, Gilbert JA (2013) Beyond the genome: community-level analysis of the microbial world. Biol Philos 28:261–282CrossRefGoogle Scholar
  121. Zhang MQ (2002) Computational prediction of eukaryotic protein-coding genes. Nat Rev Genet 3:698–709CrossRefGoogle Scholar
  122. Zhang L et al (1992) Whole genome amplification from a single cell: implications for genetic analysis. Proc Natl Acad Sci U S A 89:5847–5851CrossRefGoogle Scholar
  123. Zhang DY, Brandwein M, Hsuih T, Li HB (2001) Ramification amplification: a novel isothermal DNA amplification method. Mol Diagn 6:141–150CrossRefGoogle Scholar
  124. Zhou J, Bruns MA, Tiedje J M (1996) DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62(2), 316–322.Google Scholar
  125. Zhou J, He Z, Yang Y, Deng Y, Tringe SG, Alvarez-Cohen L (2015) High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. MBio 27:6Google Scholar
  126. Zhou X, Li Z, Zheng T, Yan Y, Li P, Odey EA, Mang HP, Uddin SM (2018) Review of global sanitation development. Environ. Int. 120:246-61.CrossRefGoogle Scholar
  127. Zong C, Lu S, Chapman AR, Xie XS (2012) Genomewide detection of single-nucleotide and copy-number variations of a single human cell. Science 338:1622–1626CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rama Kant Dubey
    • 1
  • Vishal Tripathi
    • 1
  • Ratna Prabha
    • 2
  • Rajan Chaurasia
    • 1
  • Dhananjaya Pratap Singh
    • 3
  • Ch. Srinivasa Rao
    • 4
  • Ali El-Keblawy
    • 5
  • Purushothaman Chirakkuzhyil Abhilash
    • 1
  1. 1.Institute of Environment & Sustainable DevelopmentBanaras Hindu UniversityVaranasiIndia
  2. 2.Chhattisgarh Swami Vivekananda Technical UniversityBhilaiIndia
  3. 3.ICAR-National Bureau of Agriculturally Important MicroorganismsMau Nath BhanjanIndia
  4. 4.National Academy of Agricultural Research ManagementHyderabadIndia
  5. 5.Department of Applied BiologyUniversity of SharjahSharjahUnited Arab Emirates

Personalised recommendations