Journal of Microbiology

, Volume 54, Issue 8, pp 527–536 | Cite as

On the study of microbial transcriptomes using second- and third-generation sequencing technologies

  • Sang Chul ChoiEmail author


Second-generation sequencing technologies transformed the study of microbial transcriptomes. They helped reveal the transcription start sites and antisense transcripts of microbial species, improving the microbial genome annotation. Quantification of genome-wide gene expression levels allowed for functional studies of microbial research. Ever-evolving sequencing technologies are reshaping approaches to studying microbial transcriptomes. Recently, Oxford Nanopore Technologies delivered a sequencing platform called MinION, a third-generation sequencing technology, to the research community. We expect it to be the next sequencing technology that enables breakthroughs in life science fields. The studies of microbial transcriptomes will be no exception. In this paper, we review microbial transcriptomics studies using second- generation sequencing technology. We also discuss the prospect of microbial transcriptomics studies with thirdgeneration sequencing.


library preparation bioinformatics quality control RNA-seq differential expression functional enrichment Oxford Nanopore MinION 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anders, S. and Huber, W. 2010. Differential expression analysis for sequence count data. Genome Biol. 11, R106.CrossRefGoogle Scholar
  2. Anders, S., McCarthy, D.J., Chen, Y., Okoniewski, M., Smyth, G.K., Huber, W., and Robinson, M.D. 2013. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat. Protoc. 8, 1765–1786.CrossRefPubMedGoogle Scholar
  3. Andrews, S. 2010. FastQC: A quality control tool for high throughput sequence data. Available: http://www.bioinformatics.babraham. Accessed May 14th, 2016.Google Scholar
  4. Armour, C.D., Castle, J.C., Chen, R., Babak, T., Loerch, P., Jackson, S., Shah, J.K., Dey, J., Rohl, C.A., Johnson, J.M., et al. 2009. Digital transcriptome profiling using selective hexamer priming for cDNA synthesis. Nat. Methods 6, 647–649.CrossRefPubMedGoogle Scholar
  5. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. 2000. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Benjamini, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300.Google Scholar
  7. Bhagwat, A.A., Ying, Z.I., and Smith, A. 2014. Evaluation of ribosomal RNA removal protocols for Salmonella RNA-seq projects. Adv. Microbiol. 4, 25–32.CrossRefGoogle Scholar
  8. Bischler, T., Kopf, M., and Voß, B. 2014. Transcript mapping based on dRNA-seq data. BMC Bioinformatics 15, 122.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Bolger, A.M., Lohse, M., and Usadel, B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bullard, J.H., Purdom, E., Hansen, K.D., and Dudoit, S. 2010. Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMC Bioinformatics 11, 94.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Busby, M.A., Stewart, C., Miller, C.A., Grzeda, K.R., and Marth, G.T. 2013. Scotty: a web tool for designing RNA-seq experiments to measure differential gene expression. Bioinformatics 29, 656–657.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Chen, Z. and Duan, X. 2011. Ribosomal RNA depletion for massively parallel bacterial RNA-sequencing applications. Methods Mol. Biol. 733, 93–103.CrossRefPubMedGoogle Scholar
  13. Ching, T., Huang, S., and Garmire, L.X. 2014. Power analysis and sample size estimation for RNA-seq differential expression. RNA 20, 1684–1696.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Conway, T., Creecy, J.P., Maddox, S.M., Grissom, J.E., Conkle, T.L., Shadid, T.M., Teramoto, J., Miguel, P.S., Shimada, T., Ishihama, A., et al. 2014. Unprecedented high-resolution view of bacterial operon architecture revealed by RNA sequencing. mBio 5, e01442–14.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Croucher, N.J. and Thomson, N.R. 2010. Studying bacterial transcriptomes using RNA-seq. Curr. Opin. Microbiol. 13, 619–624.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Dhillon, B.K., Laird, M.R., Shay, J.A., Winsor, G.L., Lo, R., Nizam, F., Pereira, S.K., Waglechner, N., McArthur, A.G., Langille, M.G.I., et al. 2015. IslandViewer 3: more flexible, interactive genomic island discovery, visualization and analysis. Nucleic Acids Res. 43, W104–108.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Di, Y., Emerson, S.C., Schafer, D.W., Kimbrel, J.A., and Chang, J.H. 2013. Higher order asymptotics for negative binomial regression inferences from RNA-sequencing data. Stat. Appl. Genet. Mol. Biol. 12, 49–70.PubMedPubMedCentralGoogle Scholar
  18. Dillies, M.A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., et al. 2013. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 14, 671–683.CrossRefPubMedGoogle Scholar
  19. Eid, J., Fehr, A., Gray, J., Luong, K., Lyle, J., Otto, G., Peluso, P., Rank, D., Baybayan, P., Bettman, B., et al. 2009. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138.CrossRefPubMedGoogle Scholar
  20. Erlich, Y. 2015. A vision for ubiquitous sequencing. Genome Res. 25, 1411–1416.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Fleischmann, R.D., Adams, M.D., White, O., Clayton, R.A., Kirkness, E.F., Kerlavage, A.R., Bult, C.J., Tomb, J.F., Dougherty, B.A., Merrick, J.M., et al. 1995. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269, 496–512.CrossRefPubMedGoogle Scholar
  22. Forde, B.M. and O’Toole, P.W. 2013. Next-generation sequencing technologies and their impact on microbial genomics. Brief. Funct. Genomics 12, 440–453.CrossRefPubMedGoogle Scholar
  23. Giannoukos, G., Ciulla, D.M., Huang, K., and Haas, B.J. 2012. Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol. 13, R23.CrossRefGoogle Scholar
  24. Glenn, T.C. 2011. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759–769.CrossRefPubMedGoogle Scholar
  25. Güell, M., van Noort, V., Yus, E., Chen, W.H., Leigh-Bell, J., Michalodimitrakis, K., Yamada, T., Arumugam, M., Doerks, T., Kü hner, S., et al. 2009. Transcriptome complexity in a genomereduced bacterium. Science 326, 1268–1271.CrossRefPubMedGoogle Scholar
  26. Haas, B.J., Chin, M., Nusbaum, C., Birren, B.W., and Livny, J. 2012). How deep is deep enough for RNA-seq profiling of bacterial transcriptomes? BMC Genomics 13, 734.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Hardcastle, T.J. and Kelly, K.A. 2010. baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11, 422.CrossRefPubMedPubMedCentralGoogle Scholar
  28. He, S., Wurtzel, O., Singh, K., Froula, J.L., Yilmaz, S., Tringe, S.G., Wang, Z., Chen, F., Lindquist, E.A., Sorek, R., et al. 2010. Validation of two ribosomal RNA removal methods for microbial metatranscriptomics. Nat. Methods 7, 807–812.CrossRefPubMedGoogle Scholar
  29. Hong, C., Manimaran, S., and Johnson, W.E. 2014. PathoQC: Computationally efficient read preprocessing and quality control for high-throughput sequencing data sets. Cancer Inform. 13, 167–176.PubMedGoogle Scholar
  30. Huang, D.W., Sherman, B.T., and Lempicki, R.A. 2009. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13.CrossRefGoogle Scholar
  31. Huang, D.W., Sherman, B.T., Tan, Q., Kir, J., Liu, D., Bryant, D., Guo, Y., Stephens, R., Baseler, M.W., Lane, H.C., et al. 2007. DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35, W169–175.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Jiang, H., Lei, R., Ding, S.W., and Zhu, S. 2014. Skewer: a fast and accurate adapter trimmer for next-generation sequencing pairedend reads. BMC Bioinformatics 15, 1.CrossRefGoogle Scholar
  33. Jorjani, H. and Zavolan, M. 2014. TSSer: an automated method to identify transcription start sites in prokaryotic genomes from differential RNA sequencing data. Bioinformatics 30, 971–974.CrossRefPubMedGoogle Scholar
  34. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M. 2011. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Kasianowicz, J.J., Brandin, E., Branton, D., and Deamer, D.W. 1996. Characterization of individual polynucleotide molecules using a membrane channel. Proc. Natl. Acad. Sci. USA 93, 13770–13773.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Kaspar, J., Ahn, S.J., Palmer, S.R., Choi, S.C., Stanhope, M.J., and Burne, R.A. 2015. A unique open reading frame within the comX gene of Streptococcus mutans regulates genetic competence and oxidative stress tolerance. Mol. Microbiol. 96, 463–482.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M., Sturrock, S., Buxton, S., Cooper, A., Markowitz, S., Duran, C., et al. 2012. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Kent, W.J., Sugnet, C.W., Furey, T.S., Roskin, K.M., Pringle, T.H., Zahler, A.M., and Haussler, D. 2002. The human genome browser at UCSC. Genome Res. 12, 996–1006.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Khatri, P., Voichita, C., Kattan, K., Ansari, N., Khatri, A., Georgescu, C., Tarca, A.L., and Draghici, S. 2007. Onto-Tools: new additions and improvements in 2006. Nucleic Acids Res. 35, W206–211.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Koren, S., Harhay, G.P., Smith, T.P.L., Bono, J.L., Harhay, D.M., McVey, S.D., Radune, D., Bergman, N.H., and Phillippy, A.M. 2013. Reducing assembly complexity of microbial genomes with single-molecule sequencing. Genome Biol. 14, R101.CrossRefGoogle Scholar
  41. Kumar, K., Desai, V., Cheng, L., Khitrov, M., Grover, D., Satya, R.V., Yu, C., Zavaljevski, N., and Reifman, J. 2011. AGeS: a software system for microbial genome sequence annotation. PLoS One 6, e17469.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Langmead, B. and Salzberg, S.L. 2012. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Leng, N., Dawson, J.A., Thomson, J.A., Ruotti, V., Rissman, A.I., Smits, B.M.G., Haag, J.D., Gould, M.N., Stewart, R.M., and Kendziorski, C. 2013. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29, 1035–1043.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Levin, J.Z., Yassour, M., Adiconis, X., Nusbaum, C., Thompson, D.A., Friedman, N., Gnirke, A., and Regev, A. 2010. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat. Methods 7, 709–715.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Li, H. and Durbin, R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Li, B., Ruotti, V., Stewart, R.M., Thomson, J.A., and Dewey, C.N. 2010. RNA-seq gene expression estimation with read mapping uncertainty. Bioinformatics 26, 493–500.CrossRefPubMedGoogle Scholar
  47. Li, J. and Tibshirani, R. 2013. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-seq data. Stat. Methods Med. Res. 22, 519–536.CrossRefPubMedGoogle Scholar
  48. Liao, Y., Smyth, G.K., and Shi, W. 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930.CrossRefPubMedGoogle Scholar
  49. Lindgreen, S. 2012. AdapterRemoval: easy cleaning of next-generation sequencing reads. BMC Res. Notes 5, 337.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Lister, R., O’Malley, R.C., Tonti-Filippini, J., Gregory, B.D., Berry, C.C., Millar, A.H., and Ecker, J.R. 2008. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Liu, Y., Zhou, J., and White, K.P. 2014). RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304.Google Scholar
  52. Loman, N.J., Quick, J., and Simpson, J.T. 2015. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods 12, 733–735.CrossRefPubMedGoogle Scholar
  53. Loman, N.J. and Watson, M. 2015. Successful test launch for nanopore sequencing. Nat. Methods 12, 303–304.CrossRefPubMedGoogle Scholar
  54. Love, M.I., Huber, W., and Anders, S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550.CrossRefPubMedPubMedCentralGoogle Scholar
  55. Lugli, G.A., Milani, C., Mancabelli, L., van Sinderen, D., and Ventura, M. 2016. MEGAnnotator: a user-friendly pipeline for microbial genomes assembly and annotation. FEMS Microbiol. Lett. 363, fnw049.CrossRefPubMedGoogle Scholar
  56. Margulies, M., Egholm, M., Altman, W.E., Attiya, S., Bader, J.S., Bemben, L.A., Berka, J., Braverman, M.S., Chen, Y.J., Chen, Z., et al. 2005. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380.PubMedPubMedCentralGoogle Scholar
  57. Martin, M. 2011. Cutadapt removes adapter sequences from highthroughput sequencing reads. EMBnet. J. 17, 10–12.CrossRefGoogle Scholar
  58. Martin, J., Zhu, W., Passalacqua, K.D., Bergman, N., and Borodovsky, M. 2010. Bacillus anthracis genome organization in light of whole transcriptome sequencing. BMC Bioinformatics 11, S10.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Marx, V. 2015. Nanopores: a sequencer in your backpack. Nat. Methods 12, 1015–1018.CrossRefGoogle Scholar
  60. Metzker, M.L. 2010. Sequencing technologies - the next generation. Nat. Rev. Genet. 11, 31–46.CrossRefPubMedGoogle Scholar
  61. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., and Wold, B. 2008. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628.CrossRefPubMedGoogle Scholar
  62. Nagarajan, N., Cook, C., Bonaventura, M.D., Ge, H., Richards, A., Bishop-Lilly, K.A., DeSalle, R., Read, T.D., and Pop, M. 2010. Finishing genomes with limited resources: lessons from an ensemble of microbial genomes. BMC Genomics 11, 242.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Overmars, L., van Hijum, S.A.F.T., Siezen, R.J., and Francke, C. 2015. CiVi: circular genome visualization with unique features to analyze sequence elements. Bioinformatics 31, 2867–2869.CrossRefPubMedGoogle Scholar
  64. Pandey, R.V., Pabinger, S., Kriegner, A., and Weinhäusel, A. 2016. ClinQC: a tool for quality control and cleaning of Sanger and NGS data in clinical research. BMC Bioinformatics 17, 56.CrossRefPubMedPubMedCentralGoogle Scholar
  65. Parkhomchuk, D., Borodina, T., Amstislavskiy, V., Banaru, M., Hallen, L., Krobitsch, S., Lehrach, H., and Soldatov, A. 2009. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 37, e123.CrossRefPubMedPubMedCentralGoogle Scholar
  66. Passalacqua, K.D., Varadarajan, A., Weist, C., Ondov, B.D., Byrd, B., Read, T.D., and Bergman, N.H. 2012. Strand-specific RNA-seq reveals ordered patterns of sense and antisense transcription in Bacillus anthracis. PLoS One 7, e43350.CrossRefPubMedPubMedCentralGoogle Scholar
  67. Quick, J., Ashton, P., Calus, S., Chatt, C., Gossain, S., Hawker, J., Nair, S., Neal, K., Nye, K., Peters, T., et al. 2015. Rapid draft sequencing and real-time nanopore sequencing in a hospital outbreak of Salmonella. Genome Biol. 16, 114.CrossRefPubMedPubMedCentralGoogle Scholar
  68. Quick, J., Loman, N.J., Duraffour, S., Simpson, J.T., Severi, E., Cowley, L., Bore, J.A., Koundouno, R., Dudas, G., Mikhail, A., et al. 2016. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232.CrossRefPubMedGoogle Scholar
  69. Reddy, T.B.K., Thomas, A.D., Stamatis, D., Bertsch, J., Isbandi, M., Jansson, J., Mallajosyula, J., Pagani, I., Lobos, E.A., and Kyrpides, N.C. 2015. The Genomes OnLine Database (GOLD) v.5: a metadata management system based on a four level (meta)genome project classification. Nucleic Acids Res. 43, D1099–1106.CrossRefPubMedGoogle Scholar
  70. Richards, V.P., Choi, S.C., Pavinski Bitar, P.D., Gurjar, A.A., and Stanhope, M.J. 2013. Transcriptomic and genomic evidence for Streptococcus agalactiae adaptation to the bovine environment. BMC Genomics 14, 920.CrossRefPubMedPubMedCentralGoogle Scholar
  71. Robinson, M.D., McCarthy, D.J., and Smyth, G.K. 2010. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140.CrossRefPubMedGoogle Scholar
  72. Robinson, M.D. and Oshlack, A. 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25.CrossRefGoogle Scholar
  73. Rothberg, J.M., Hinz, W., Rearick, T.M., Schultz, J., Mileski, W., Davey, M., Leamon, J.H., Johnson, K., Milgrew, M.J., Edwards, M., et al. 2011. An integrated semiconductor device enabling non-optical genome sequencing. Nature 475, 348–352.CrossRefPubMedGoogle Scholar
  74. Schmieder, R. and Edwards, R. 2011. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864.CrossRefPubMedPubMedCentralGoogle Scholar
  75. Sharma, C.M., Hoffmann, S., Darfeuille, F., Reignier, J., Findeiss, S., Sittka, A., Chabas, S., Reiche, K., Hackermü ller, J., Reinhardt, R., et al. 2010. The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 464, 250–255.CrossRefPubMedGoogle Scholar
  76. Shen, R., Fan, J.B., Campbell, D., Chang, W., Chen, J., Doucet, D., Yeakley, J., Bibikova, M., Wickham Garcia, E., McBride, C., et al. 2005. High-throughput SNP genotyping on universal bead arrays. Mutat. Res. 573, 70–82.CrossRefPubMedGoogle Scholar
  77. Shrestha, R.K., Lubinsky, B., Bansode, V.B., Moinz, M.B.J., McCormack, G.P., and Travers, S.A. 2014. QTrim: a novel tool for the quality trimming of sequence reads generated using the Roche/454 sequencing platform. BMC Bioinformatics 15, 33.CrossRefPubMedPubMedCentralGoogle Scholar
  78. Soneson, C. and Delorenzi, M. 2013. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14, 1.CrossRefGoogle Scholar
  79. Sorek, R. and Cossart, P. 2010. Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nat. Rev. Genet. 11, 9–16.CrossRefPubMedGoogle Scholar
  80. Tabas-Madrid, D., Nogales-Cadenas, R., and Pascual-Montano, A. 2012. GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res. 40, W478–W483.CrossRefPubMedPubMedCentralGoogle Scholar
  81. Tarazona, S., Garcí a-Alcalde, F., Dopazo, J., Ferrer, A., and Conesa, A. 2011. Differential expression in RNA-seq: a matter of depth. Genome Res. 21, 2213–2223.CrossRefPubMedPubMedCentralGoogle Scholar
  82. The UniProt Consortium. 2013. Update on activities at the universal protein resource (UniProt) in 2013. Nucleic Acids Res. 41, D43–47.CrossRefGoogle Scholar
  83. Thorvaldsdóttir, H., Robinson, J.T., and Mesirov, J.P. 2013. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192.CrossRefPubMedGoogle Scholar
  84. Vivancos, A.P., Güell, M., Dohm, J.C., Serrano, L., and Himmelbauer, H. 2010. Strand-specific deep sequencing of the transcriptome. Genome Res. 20, 989–999.CrossRefPubMedPubMedCentralGoogle Scholar
  85. Wade, J.T. and Grainger, D.C. 2014. Pervasive transcription: illuminating the dark matter of bacterial transcriptomes. Nat. Rev. Microbiol. 12, 647–653.CrossRefPubMedGoogle Scholar
  86. Wagle, P., Nikolic, M., and Frommolt, P. 2015. QuickNGS elevates next-generation sequencing data analysis to a new level of automation. BMC Genomics 16, 487.CrossRefPubMedPubMedCentralGoogle Scholar
  87. Wagner, G.P., Kin, K., and Lynch, V.J. 2012. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131, 281–285.CrossRefPubMedGoogle Scholar
  88. Williams, A.G., Thomas, S., Wyman, S.K., and Holloway, A.K. 2014. RNA-seq data: challenges in and recommendations for experimental design and analysis. Curr. Protoc. Hum. Genet. 83, 11.13.1–20.CrossRefGoogle Scholar
  89. Xu, H., Luo, X., Qian, J., Pang, X., Song, J., Qian, G., Chen, J., and Chen, S. 2012. FastUniq: a fast de novo duplicates removal tool for paired short reads. PLoS One 7, e52249.CrossRefPubMedPubMedCentralGoogle Scholar
  90. Young, M.D., Wakefield, M.J., Smyth, G.K., and Oshlack, A. 2010. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14.CrossRefGoogle Scholar
  91. Zeng, L., Choi, S.C., Danko, C.G., Siepel, A., Stanhope, M.J., and Burne, R.A. 2013. Gene regulation by CcpA and catabolite repression explored by RNA-seq in Streptococcus mutans. PLoS One 8, e60465.CrossRefPubMedPubMedCentralGoogle Scholar
  92. Zhang, M., Sun, H., Fei, Z., Zhan, F., Gong, X., and Gao, S. 2014). Fastq_clean: an optimized pipeline to clean the Illumina sequencing data with quality control. BIBM. 2014 IEEE Int. Conf. pp. 44–48, DOI:10.1109/BIBM.2014.6999309.Google Scholar
  93. Zhao, S., Xi, L., Quan, J., Xi, H., Zhang, Y., von Schack, D., Vincent, M., and Zhang, B. 2016. QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization. BMC Genomics 17, 39.CrossRefPubMedPubMedCentralGoogle Scholar
  94. Zhu, Y.Y., Machleder, E.M., Chenchik, A., Li, R., and Siebert, P.D. 2001. Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 30, 892–897.PubMedGoogle Scholar

Copyright information

© The Microbiological Society of Korea and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of BiologySungshin Women’s UniversitySeoulRepublic of Korea

Personalised recommendations