Choice of Next-Generation Sequencing Pipelines

  • F. Del Chierico
  • M. Ancora
  • M. Marcacci
  • C. Cammà
  • L. Putignani
  • Salvatore ContiEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1231)


The next-generation sequencing (NGS) technologies are revolutionary tools which have made possible achieving remarkable advances in genetics since the beginning of the twenty-first century. Thanks to the possibility to produce large amount of sequence data, these tools are going to completely substitute other high-throughput technologies. Moreover, the large applications of NGS protocols are increasing the genetic decoding of biological systems through studies of genome anatomy and gene mapping, coupled to the transcriptome pictures. The application of NGS pipelines such as (1) de-novo genomic sequencing by mate-paired and whole-genome shotgun strategies; (2) specific gene sequencing on large bacterial communities; and (3) RNA-seq methods including whole transcriptome sequencing and Serial Analysis of Gene Expression (Sage-analysis) are fundamental in the genome-wide fields like metagenomics. Recently, the availability of these advanced protocols has allowed to overcome the usual sequencing technical issues related to the mapping specificity over standard shotgun library sequencing, the detection of large structural genomes variations and bridging sequencing gaps, as well as more precise gene annotation. In this chapter we will discuss how to manage a successful NGS pipeline from the planning of sequencing projects through the choice of the platforms up to the data analysis management.

Key words

NGS Metagenomics Whole-genome sequencing 16S rRNA gene Gene mapping RNA-seq Library preparation Template preparation NGS platforms 


  1. 1.
    Luo G, Wang W, Angelidaki I (2013) Anaerobic digestion for simultaneous sewage sludge treatment and CO biomethanation: process performance and microbial ecology. Environ Sci Technol 47:10685–10693PubMedGoogle Scholar
  2. 2.
    Salipante SJ, Sengupta DJ, Hoogestraat DR et al (2013) Molecular diagnosis of Actinomadura madurae infection by 16S rRNA deep sequencing. J Clin Microbiol 51:4262–4265CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Salipante SJ, Sengupta DJ, Rosenthal C et al (2013) Rapid 16S rRNA next-generation sequencing clinical of polymicrobial samples for diagnosis of complex bacterial infections. PLoS One. doi: 10.1371/journal.pone.0065226 PubMedPubMedCentralGoogle Scholar
  4. 4.
    Thomas T, Gilbert J, Meyer F (2012) Metagenomics—a guide from sampling to data analysis. Microb Inform Exp. doi: 10.1186/2042-5783-2-3 PubMedPubMedCentralGoogle Scholar
  5. 5.
    Luo C, Tsementzi D, Kyrpides N et al (2012) Direct comparisons of Illumina vs. Roche 454 sequencing technologies on the same microbial community DNA sample. PLoS One. doi: 10.1371/journal.pone.0030087 Google Scholar
  6. 6.
    Schatz MC, Delcher AL, Salzberg SL et al (2010) Assembly of large genomes using second-generation sequencing. Genome Res 20:1165–1173CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Powers JG, Weigman VJ, Shu J et al (2013) Efficient and accurate whole genome assembly and methylome profiling of E. coli. BMC Genomics 14:675CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Durfee T, Nelson R, Baldwin S et al (2008) The complete genome sequence of Escherichia coli DH10B: insights into the biology of a laboratory workhorse. J Bacteriol 190:2597–2606CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Jucá Ramos RT, Ribeiro Carneiro A, De Castro Soares S et al (2013) High efficiency application of a mate-paired library from next-generation sequencing to postlight sequencing: Corynebacterium pseudotuberculosis as a case study for microbial de novo genome assembly. J Microbiol Methods 95:441–447CrossRefGoogle Scholar
  10. 10.
    Milani C, Hevia A, Foroni E et al (2013) Assessing the fecal microbiota: an optimized ion torrent 16S rRNA gene-based analysis protocol. PLoS One. doi: 10.1371/journal.pone.0068739 Google Scholar
  11. 11.
    White AG, Watts GS, Lu Z et al (2014) Environmental arsenic exposure and microbiota in induced sputum. Int J Environ Res Public Health 21:2299–2313CrossRefGoogle Scholar
  12. 12.
    Hasman H, Saputra D, Sicheritz-Ponten T et al (2014) Rapid whole-genome sequencing for detection and characterization of microorganisms directly from clinical samples. J Clin Microbiol 52:139–146CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Van Hal SJ, Steen JA, Espedido BA et al (2014) In vivo evolution of antimicrobial resistance in a series of Staphylococcus aureus patient isolates: the entire picture or a cautionary tale? J Antimicrob Chemother 69:363–367CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Tyakht AV, Kostryukova ES, Popenko AS et al (2013) Human gut microbiota community structures in urban and rural populations in Russia. Nat Commun. doi: 10.1038/ncomms3469 PubMedPubMedCentralGoogle Scholar
  15. 15.
    Zhang T, Zhang XX, Ye L (2011) Plasmid metagenome reveals high levels of antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS One. doi: 10.1371/journal.pone.0026041 Google Scholar
  16. 16.
    Lai Z, Zou Y, Kane NC et al (2012) Preparation of normalized cDNA libraries for 454 Titanium transcriptome sequencing. Methods Mol Biol 888:119–133CrossRefPubMedGoogle Scholar
  17. 17.
    Wan M, Faruq J, Rosenberg JN et al (2013) Achieving high throughput sequencing of a cDNA library utilizing an alternative protocol for the bench top next-generation sequencing system. J Microbiol Methods 92:122–126CrossRefPubMedGoogle Scholar
  18. 18.
    Chaisson MJ, Pevzner PA (2008) Short read fragment assembly of bacterial genomes. Genome Res 18:324–330CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Rodrigue S, Materna AC, Timberlake SC et al (2010) Unlocking short read sequencing for metagenomics. PLoS One. doi: 10.1371/journal.pone.0011840 PubMedPubMedCentralGoogle Scholar
  20. 20.
    Umemura M, Koyama Y, Takeda I (2013) Fine de novo sequencing of a fungal genome using only SOLiD short read data: verification on Aspergillus oryzae RIB40. PLoS One. doi: 10.1371/journal.pone.0063673 Google Scholar
  21. 21.
    Ancora M, Marcacci M, Orsini M et al (2014) Complete genome sequence of a Brucella ceti ST26 strain isolated from a striped Dolphin (Stenella coeruleoalba) on the coast of Italy. Genome Announc. doi: 10.1128/genomeA.00068-14 Google Scholar
  22. 22.
    Merriman B, Ion Torrent R&D Team, Rothberg JM (2012) Progress in ion torrent semiconductor chip based sequencing. Electrophoresis 33:397–417CrossRefGoogle Scholar
  23. 23.
    Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–829CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chain PSG, Grafham DV, Fulton RS et al (2009) Genome project standards in a new era of sequencing. Science 326:236–237CrossRefPubMedGoogle Scholar
  25. 25.
    Toledo-Arana A, Repoila F, Cossart P (2007) Small noncoding RNAs controlling pathogenesis. Curr Opin Microbiol 10:182–188CrossRefPubMedGoogle Scholar
  26. 26.
    Pierlé SA, Dark MJ, Dahmen D et al (2012) Comparative genomics and transcriptomics of trait-gene association. BMC Genomics 13:669CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Pinto AC, Melo-Barbosa HP, Miyoshi A et al (2011) Application of RNA-seq to reveal the transcript profile in bacteria. Genet Mol Res 10:1707–1718CrossRefPubMedGoogle Scholar
  29. 29.
    Parkhomchuk D, Borodina T, Amstislavskiy V et al (2009) Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res 37:e123CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Westermann AJ, Gorski SA, Vogel J (2012) Dual RNA-seq of pathogen and host. Nat Rev Microbiol 10:618–630CrossRefPubMedGoogle Scholar
  31. 31.
    Sharma CM, Hoffmann S, Darfeuille F et al (2010) The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 464:250–255CrossRefPubMedGoogle Scholar
  32. 32.
    Cox ML, Eddy SM, Stewart ZS et al (2008) Investigating fixative-induced changes in RNA quality and utility by microarray analysis. Exp Mol Pathol 84:156–172CrossRefPubMedGoogle Scholar
  33. 33.
    Armour CD, Castle JC, Chen R et al (2009) Digital transcriptome profiling using selective hexamer priming for cDNA synthesis. Nat Methods 6:647–649CrossRefPubMedGoogle Scholar
  34. 34.
    Giannoukos G, Ciulla DM, Huang K et al (2012) Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol 13:R23CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    McIntyre LM, Lopiano KK, Morse AM et al (2011) RNA-seq: technical variability and sampling. BMC Genomics 12:293CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Jiang L, Schlesinger F, Davis CA et al (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res 21:1543–1551CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Del Chierico F, Gnani D, Vernocchi P et al (2014) Meta-omic platforms to assist in the understanding of NAFLD gut microbiota alterations: tools and applications. Int J Mol Sci 15:684–711CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Janda JM, Abbott SL (2007) 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J Clin Microbiol 45:2761–2764CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Chakravorty S, Helb D, Burday M et al (2007) A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods 69:330–339CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Patel JB (2001) 16S rRNA gene sequencing for bacterial pathogen identification in the clinical laboratory. Mol Diagn 6:313–321CrossRefPubMedGoogle Scholar
  41. 41.
    Margulies M, Egholm M, Altman WE et al (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437:376–380PubMedPubMedCentralGoogle Scholar
  42. 42.
    Human Microbiome Project Consortium (2012) Structure, function and diversity of the healthy human microbiome. Nature 486:207–214CrossRefGoogle Scholar
  43. 43.
    Amir A, Zeisel A, Zuk O et al (2013) High-resolution microbial community reconstruction by integrating short reads from multiple 16S rRNA regions. Nucleic Acids Res 41:e205. doi: 10.1093/nar/gkt1070 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Wang Q, Garrity GM, Tiedje JM et al (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Kuczynski J, Lauber CL, Walters WA et al (2012) Experimental and analytical tools for studying the human microbiome. Nat Rev Genet 13:47–58CrossRefGoogle Scholar
  46. 46.
    Peterson DA, Frank DN, Pace NR et al (2008) Metagenomic approaches for defining the pathogenesis of inflammatory bowel diseases. Cell Host Microbe 3:417–427CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Cole JR, Chai B, Farris RJ et al (2007) The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucleic Acids Res 35:D169–D172CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    DeSantis TZ, Hugenholtz P, Larsen N et al (2006) Greengenes, a chimerachecked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Pruesse E, Quast C, Knittel K et al (2007) SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35:7188–7196CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Petrosino JF, Highlander S, Luna RA et al (2009) Metagenomic pyrosequencing and microbial identification. Clin Chem 55:856–866CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • F. Del Chierico
    • 1
  • M. Ancora
    • 2
  • M. Marcacci
    • 2
  • C. Cammà
    • 2
  • L. Putignani
    • 1
  • Salvatore Conti
    • 3
    Email author
  1. 1.Unit of Parasitology & Unit of Metagenomics, Bambino Gesù Children’s HospitalIRCCSRomeItaly
  2. 2.Istituto Zooprofilattico Sperimentale dell’ Abruzzo e Molise “G. Caporale”National and OIE Reference Laboratory for BrucellosisTeramoItaly
  3. 3.Thermo Fisher ScientificMonzaItaly

Personalised recommendations