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Prokaryotic Metatranscriptomics

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Book cover Hydrocarbon and Lipid Microbiology Protocols

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Abstract

Metatranscriptomics, the determination of RNA transcripts found in a microbiome, is a very active field nowadays. Improvements both in sequencing technologies and in software for analysis have made metatranscriptomics a very amenable technique, soon to be routinely used. In this chapter, we illustrate the steps needed to perform a metatranscriptomic experiment, from the collection of the samples to the production of the final results. The full protocol is composed of wet-lab procedures for preparing and sequencing the samples, and in silico, bioinformatic techniques to process and analyze the results.

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References

  1. Dugar G, Herbig A, Förstner KU, Heidrich N, Reinhardt R, Nieselt K et al (2013) High-resolution transcriptome maps reveal strain-specific regulatory features of multiple Campylobacter jejuni isolates. PLoS Genet 9:e1003495. doi:10.1371/journal.pgen.1003495

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sharma CM, Hoffmann S, Darfeuille F, Reignier J, Findeiss S, Sittka A et al (2010) The primary transcriptome of the major human pathogen Helicobacter pylori. Nature 464:250–255

    Article  CAS  PubMed  Google Scholar 

  3. Thomason MK, Bischler T, Eisenbart SK, Forstner KU, Zhang A, Herbig A et al (2015) Global transcriptional start site mapping using differential RNA sequencing reveals novel antisense RNAs in Escherichia coli. J Bacteriol 197:18–28

    Article  PubMed  CAS  Google Scholar 

  4. Creecy JP, Conway T (2015) Quantitative bacterial transcriptomics with RNA-seq. Curr Opin Microbiol 23:133–140

    Article  CAS  PubMed  Google Scholar 

  5. Chen Y-J, Liu P, Nielsen AA, Brophy JA, Clancy K, Peterson T et al (2013) Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nat Methods 10:659–664

    Article  CAS  PubMed  Google Scholar 

  6. Conway T, Creecy JP, Maddox SM, Grissom JE, Conkle TL, Shadid TM et al (2014) Unprecedented high-resolution view of bacterial operon architecture revealed by RNA sequencing. MBio 5:1–12

    Article  CAS  Google Scholar 

  7. Michaux C, Verneuil N, Hartke A, Giard J-C (2014) Physiological roles of small RNA molecules. Microbiology 1–33

    Google Scholar 

  8. Repoila F, Darfeuille F (2009) Small regulatory non-coding RNAs in bacteria: physiology and mechanistic aspects. Biol Cell 101:117–131

    Article  CAS  PubMed  Google Scholar 

  9. Lasa I, Toledo-Arana A, Dobin A, Villanueva M, de los Mozos IR, Vergara-Irigaray M et al (2011) Genome-wide antisense transcription drives mRNA processing in bacteria. Proc Natl Acad Sci 108:20172–20177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pinto AC, Melo-Barbosa HP, Miyoshi A, Silva A, Azevedo V (2011) Application of RNA-seq to reveal the transcript profile in bacteria. Genet Mol Res 10:1707–1718

    Article  CAS  PubMed  Google Scholar 

  11. Poretsky RS, Bano N, Buchan A, LeCleir G, Kleikemper J, Pickering M et al (2005) Analysis of microbial gene transcripts in environmental samples. Appl Environ Microbiol 71:4121–4126

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Leininger S, Urich T, Schloter M, Schwark L, Qi J, Nicol GW et al (2006) Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442:806–809

    Article  CAS  PubMed  Google Scholar 

  13. Parro V, Moreno-Paz M, González-Toril E (2007) Analysis of environmental transcriptomes by DNA microarrays. Environ Microbiol 9:453–464

    Article  CAS  PubMed  Google Scholar 

  14. Frias-Lopez J, Frias-Lopez J, Shi Y, Tyson G, Shi Y, Coleman M et al (2008) Microbial community gene expression in ocean surface waters. Proc Natl Acad Sci U S A 105:3805–3810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gilbert JA, Field D, Huang Y, Edwards RA, Li W, Gilna P et al (2011) Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities. In: de Bruijn FJ (ed) Handbook of molecular microbial ecology II: metagenomics in different habitats. Wiley, Hoboken, pp 277–286

    Chapter  Google Scholar 

  16. Kim Y, Liesack W (2015) Differential assemblage of functional units in paddy soil microbiomes. PLoS One 10:e0122221

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Hesse CN, Mueller RC, Vuyisich M, Gallegos-Graves LV, Gleasner CD, Zak DR et al (2015) Forest floor community metatranscriptomes identify fungal and bacterial responses to N deposition in two maple forests. Front Microbiol 6:337

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pearson GA, Lago-Leston A, Canovas F, Cox CJ, Verret F, Lasternas S et al (2015) Metatranscriptomes reveal functional variation in diatom communities from the Antarctic Peninsula. ISME J 9(10):2275–2289. doi:10.1038/ismej.2015.40

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Coolen MJL, Orsi WD (2015) The transcriptional response of microbial communities in thawing Alaskan permafrost soils. Front Microbiol 6:197

    Article  PubMed  PubMed Central  Google Scholar 

  20. Ganesh S, Bristow LA, Larsen M, Sarode N, Thamdrup B, Stewart FJ (2015) Size-fraction partitioning of community gene transcription and nitrogen metabolism in a marine oxygen minimum zone. ISME J. doi:10.1038/ismej.2015.44

    PubMed  PubMed Central  Google Scholar 

  21. Tsementzi D, Poretsky R, Rodriguez-R LM, Luo C, Konstantinidis KT (2014) Evaluation of metatranscriptomic protocols and application to the study of freshwater microbial communities. Environ Microbiol Rep 6:640–655

    Article  CAS  PubMed  Google Scholar 

  22. Voorhies AA, Eisenlord SD, Marcus DN, Duhaime MB, Biddanda BA, Cavalcoli JD et al (2015) Ecological and genetic interactions between cyanobacteria and viruses in a low-oxygen mat community inferred through metagenomics and metatranscriptomics. Environ Microbiol. doi:10.1111/1462-2920.12756

    PubMed  Google Scholar 

  23. Chen L-X, Hu M, Huang L-N, Hua Z-S, Kuang J-L, Li S-J et al (2015) Comparative metagenomic and metatranscriptomic analyses of microbial communities in acid mine drainage. ISME J 9:1579–1592

    Article  PubMed  Google Scholar 

  24. Hilton JA, Satinsky BM, Doherty M, Zielinski B, Zehr JP (2015) Metatranscriptomics of N2-fixing cyanobacteria in the Amazon River plume. ISME J 9:1557–1569

    Article  CAS  PubMed  Google Scholar 

  25. Hua Z-S, Han Y-J, Chen L-X, Liu J, Hu M, Li S-J et al (2015) Ecological roles of dominant and rare prokaryotes in acid mine drainage revealed by metagenomics and metatranscriptomics. ISME J 9:1280–1294

    Article  CAS  PubMed  Google Scholar 

  26. Alberti A, Belser C, Engelen S, Bertrand L, Orvain C, Brinas L et al (2014) Comparison of library preparation methods reveals their impact on interpretation of metatranscriptomic data. BMC Genomics 15:912

    Article  PubMed  PubMed Central  Google Scholar 

  27. Quaiser A, Bodi X, Dufresne A, Naquin D, Francez A-J, Dheilly A et al (2014) Unraveling the stratification of an iron-oxidizing microbial mat by metatranscriptomics. PLoS One 9:e102561

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Franzosa EA, Morgan XC, Segata N, Waldron L, Reyes J, Earl AM et al (2014) Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci U S A 111:E2329–E2338

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Moitinho-Silva L, Seridi L, Ryu T, Voolstra CR, Ravasi T, Hentschel U (2014) Revealing microbial functional activities in the Red Sea sponge Stylissa carteri by metatranscriptomics. Environ Microbiol 16:3683–3698

    Article  CAS  PubMed  Google Scholar 

  30. Gifford SM, Sharma S, Moran MA (2014) Linking activity and function to ecosystem dynamics in a coastal bacterioplankton community. Front Microbiol 5:185

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lamendella R, Strutt S, Borglin S, Chakraborty R, Tas N, Mason OU et al (2014) Assessment of the Deepwater Horizon oil spill impact on Gulf coast microbial communities. Front Microbiol 5:130

    Article  PubMed  PubMed Central  Google Scholar 

  32. Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M (2014) Metatranscriptomics of the human oral microbiome during health and disease. MBio 5:e01012–e01014

    Article  PubMed  PubMed Central  Google Scholar 

  33. Embree M, Nagarajan H, Movahedi N, Chitsaz H, Zengler K (2014) Single-cell genome and metatranscriptome sequencing reveal metabolic interactions of an alkane-degrading methanogenic community. ISME J 8:757–767

    Article  CAS  PubMed  Google Scholar 

  34. Dumont MG, Pommerenke B, Casper P (2013) Using stable isotope probing to obtain a targeted metatranscriptome of aerobic methanotrophs in lake sediment. Environ Microbiol Rep 5:757–764

    CAS  PubMed  Google Scholar 

  35. Urich T, Lanzen A, Stokke R, Pedersen RB, Bayer C, Thorseth IH et al (2014) Microbial community structure and functioning in marine sediments associated with diffuse hydrothermal venting assessed by integrated meta-omics. Environ Microbiol 16:2699–2710

    Article  CAS  PubMed  Google Scholar 

  36. Twin J, Bradshaw CS, Garland SM, Fairley CK, Fethers K, Tabrizi SN (2013) The potential of metatranscriptomics for identifying screening targets for bacterial vaginosis. PLoS One 8:e76892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Turner TR, Ramakrishnan K, Walshaw J, Heavens D, Alston M, Swarbreck D et al (2013) Comparative metatranscriptomics reveals kingdom level changes in the rhizosphere microbiome of plants. ISME J 7:2248–2258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Baker BJ, Sheik CS, Taylor CA, Jain S, Bhasi A, Cavalcoli JD et al (2013) Community transcriptomic assembly reveals microbes that contribute to deep-sea carbon and nitrogen cycling. ISME J 7:1962–1973

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sanders JG, Beinart RA, Stewart FJ, Delong EF, Girguis PR (2013) Metatranscriptomics reveal differences in in situ energy and nitrogen metabolism among hydrothermal vent snail symbionts. ISME J 7:1556–1567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Maurice CF, Haiser HJ, Turnbaugh PJ (2013) Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152:39–50

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Vila-Costa M, Sharma S, Moran MA, Casamayor EO (2013) Diel gene expression profiles of a phosphorus limited mountain lake using metatranscriptomics. Environ Microbiol 15:1190–1203

    Article  CAS  PubMed  Google Scholar 

  42. Burow LC, Woebken D, Marshall IPG, Lindquist EA, Bebout BM, Prufert-Bebout L et al (2013) Anoxic carbon flux in photosynthetic microbial mats as revealed by metatranscriptomics. ISME J 7:817–829

    Article  CAS  PubMed  Google Scholar 

  43. Waters LS, Storz G (2009) Regulatory RNAs in bacteria. Cell 136:615–628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Stark L, Giersch T, Wünschiers R (2014) Efficiency of RNA extraction from selected bacteria in the context of biogas production and metatranscriptomics. Anaerobe 29:85–90

    Article  CAS  PubMed  Google Scholar 

  45. Sorek R, Cossart P (2010) Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nat Rev Genet 11:9–16

    Article  CAS  PubMed  Google Scholar 

  46. Giannoukos G, Ciulla DM, Huang K, Haas BJ, Izard J, Levin JZ et al (2012) Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol 13:R23

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Carvalhais LC, Dennis PG, Tyson GW, Schenk PM (2012) Application of metatranscriptomics to soil environments. J Microbiol Methods 91:246–251

    Article  CAS  PubMed  Google Scholar 

  48. Syed F (2010) Application of Nextera TM technology to RNA-seq library preparation ADVERTISING FEATURE. Nat Publ Gr 7:an2–an3

    Google Scholar 

  49. Landesfeind M, Meinicke P (2014) Predicting the functional repertoire of an organism from unassembled RNA-seq data. BMC Genomics 15:1003

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Cock PJA, Fields CJ, Goto N, Heuer ML, Rice PM (2009) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38:1767–1771

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Hansen KD, Brenner SE, Dudoit S (2010) Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res 38:e131

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Zhou X, Rokas A (2014) Prevention, diagnosis and treatment of high-throughput sequencing data pathologies. Mol Ecol 23:1679–1700

    Article  PubMed  Google Scholar 

  53. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Patel RK, Jain M (2012) NGS QC toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 7:e30619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10

    Article  Google Scholar 

  56. Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM (2013) An extensive evaluation of read trimming effects on illumina NGS data analysis. PLoS One 8:e85024

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Jiang H, Lei R, Ding S-W, Zhu S (2014) Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15:182

    Article  PubMed  PubMed Central  Google Scholar 

  58. Harismendy O, Ng PC, Strausberg RL, Wang X, Stockwell TB, Beeson KY et al (2009) Evaluation of next generation sequencing platforms for population targeted sequencing studies. Genome Biol 10:R32

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Brockman W, Alvarez P, Young S, Garber M, Giannoukos G, Lee WL et al (2008) Quality scores and SNP detection in sequencing-by-synthesis systems. Genome Res 18:763–770

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27:863–864

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Van Gurp TP, McIntyre LM, Verhoeven KJF (2013) Consistent errors in first strand cDNA due to random hexamer mispriming. PLoS One 8:e85583

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Schmieder R, Lim YW, Edwards R (2012) Identification and removal of ribosomal RNA sequences from metatranscriptomes. Bioinformatics 28:433–435

    Article  CAS  PubMed  Google Scholar 

  63. Kopylova E, Noé L, Touzet H (2012) SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28:3211–3217

    Article  CAS  PubMed  Google Scholar 

  64. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596

    Article  CAS  PubMed  Google Scholar 

  65. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K et al (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR (2003) Rfam: an RNA family database. Nucleic Acids Res 31:439–441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ et al (2009) The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 37:D141–D145

    Article  CAS  PubMed  Google Scholar 

  68. Namiki T, Hachiya T, Tanaka H, Sakakibara Y (2012) MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res 40:e155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Schulz MH, Zerbino DR, Vingron M, Birney E (2012) Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28:1086–1092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J et al (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8:1494–1512

    Article  CAS  PubMed  Google Scholar 

  71. Leung HCM, Yiu S-M, Parkinson J, Chin FYL (2013) IDBA-MT: de novo assembler for metatranscriptomic data generated from next-generation sequencing technology. J Comput Biol 20:540–550

    Article  CAS  PubMed  Google Scholar 

  72. Leung HCM, Yiu SM, Chin FYL (2015) IDBA-MTP: a hybrid metatranscriptomic assembler based on protein information. J Comput Biol 22:367–376

    Article  CAS  PubMed  Google Scholar 

  73. Celaj A, Markle J, Danska J, Parkinson J (2014) Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation. Microbiome 2:39

    Article  PubMed  PubMed Central  Google Scholar 

  74. Fonseca NA, Rung J, Brazma A, Marioni JC (2012) Tools for mapping high-throughput sequencing data. Bioinformatics 28:3169–3177

    Article  CAS  PubMed  Google Scholar 

  75. Ruffalo M, Laframboise T, Koyutürk M (2011) Comparative analysis of algorithms for next-generation sequencing read alignment. Bioinformatics 27:2790–2796

    Article  CAS  PubMed  Google Scholar 

  76. Li H, Ruan J, Durbin R (2008) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18:1851–1858

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Li R, Li Y, Kristiansen K, Wang J (2008) SOAP: short oligonucleotide alignment program. Bioinformatics 24:713–714

    Article  CAS  PubMed  Google Scholar 

  78. Ning Z, Cox AJ, Mullikin JC (2001) SSAHA: a fast search method for large DNA databases. Genome Res 11:1725–1729

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K et al (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25:1966–1967

    Article  CAS  PubMed  Google Scholar 

  80. Liu CM, Wong T, Wu E, Luo R, Yiu SM, Li Y et al (2012) SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics 28:878–879

    Article  CAS  PubMed  Google Scholar 

  81. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 1–10

    Google Scholar 

  83. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192

    Article  PubMed  CAS  Google Scholar 

  87. Nicol JW, Helt GA, Blanchard SG, Raja A, Loraine AE (2009) The Integrated Genome Browser: free software for distribution and exploration of genome-scale datasets. Bioinformatics 25:2730–2731

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Carver T, Harris SR, Berriman M, Parkhill J, McQuillan JA (2012) Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 28:464–469

    Article  CAS  PubMed  Google Scholar 

  89. Abeel T, Van Parys T, Saeys Y, Galagan J, Van De Peer Y (2012) GenomeView: a next-generation genome browser. Nucleic Acids Res 40:e12

    Article  CAS  PubMed  Google Scholar 

  90. Skinner ME, Uzilov AV, Stein LD, Mungall CJ, Holmes IH (2009) JBrowse: a next-generation genome browser. Genome Res 19:1630–1638

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Milne I, Stephen G, Bayer M, Cock PJA, Pritchard L, Cardle L et al (2013) Using tablet for visual exploration of second-generation sequencing data. Brief Bioinform 14:193–202

    Article  CAS  PubMed  Google Scholar 

  92. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683

    Article  CAS  PubMed  Google Scholar 

  93. Anders S, Pyl PT, Huber W (2015) HTSeq – a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169

    Article  CAS  PubMed  Google Scholar 

  94. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628

    Article  CAS  PubMed  Google Scholar 

  95. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Wagner GP, Kin K, Lynch VJ (2012) Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131:281–285

    Article  CAS  PubMed  Google Scholar 

  97. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Liao Y, Smyth GK, Shi W (2014) FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930

    Article  CAS  PubMed  Google Scholar 

  99. García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S et al (2012) Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 28:2678–2679

    Article  PubMed  CAS  Google Scholar 

  100. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  CAS  PubMed  Google Scholar 

  101. Kent WJ (2002) BLAT - the BLAST-like alignment tool. Genome Res 12:656–664

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Zhao Y, Tang H, Ye Y (2012) RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data. Bioinformatics 28:125–126

    Article  CAS  PubMed  Google Scholar 

  103. Eddy SR (2009) A new generation of homology search tools based on probabilistic inference. Genome Inform 23:205–211

    PubMed  Google Scholar 

  104. Huson DH, Weber N (2013) Microbial community analysis using MEGAN. Methods Enzymol 531:465–485

    Article  CAS  PubMed  Google Scholar 

  105. Glass EM, Meyer F (2011) The metagenomics RAST server: a public resource for the automatic phylogenetic and functional analysis of metagenomes. In: de Bruijn FJ (ed) Handbook of molecular microbial ecology I: metagenomics and complementary approaches. Wiley, Hoboken, pp 325–331

    Chapter  Google Scholar 

  106. Li W (2009) Analysis and comparison of very large metagenomes with fast clustering and functional annotation. BMC Bioinformatics 10:359

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Sohn MB, An L, Pookhao N, Li Q (2014) Accurate genome relative abundance estimation for closely related species in a metagenomic sample. BMC Bioinformatics 15:242

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Dinsdale EA, Edwards RA, Bailey BA, Tuba I, Akhter S, McNair K et al (2013) Multivariate analysis of functional metagenomes. Front Genet 4:41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Ramette A (2007) Multivariate analyses in microbial ecology. FEMS Microbiol Ecol 62:142–160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Hammer Ø, Harper DAT, Ryan PD (2001) Paleontological statistics software package for education and data analysis. Palaeontol Electron 4:9–18

    Google Scholar 

  112. Kristiansson E, Hugenholtz P, Dalevi D (2009) ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes. Bioinformatics 25:2737–2738

    Article  CAS  PubMed  Google Scholar 

  113. White JR, Nagarajan N, Pop M (2009) Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol 5:e1000352

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Koski LB, Golding GB (2001) The closest BLAST hit is often not the nearest neighbor. J Mol Evol 52:540–542

    Article  CAS  PubMed  Google Scholar 

  115. Tatusov RL, Koonin EV, Lipman DJ (1997) A genomic perspective on protein families. Science 278:631–637

    Article  CAS  PubMed  Google Scholar 

  116. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by project CTM2013-48292-C3 (Ministerio de Economía y Competitividad, Spain).

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Authors and Affiliations

  1. Departamento de Biología de Sistemas, Centro Nacional de Biotecnología (CNB-CSIC), C/Darwin 3, 28049, Madrid, Spain

    Danilo Pérez-Pantoja & Javier Tamames

  2. Facultad de Ciencias Biológicas, Departamento de Bioquímica y Biología Molecular, Universidad de Concepción, Concepción, Chile

    Danilo Pérez-Pantoja

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  2. Javier Tamames
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Correspondence to Javier Tamames .

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Editors and Affiliations

  1. Dept. Biological Sciences, University of Essex Dept. Biological Sciences, Colchester, Essex, United Kingdom

    Terry J. McGenity

  2. Braunschweig, Germany

    Kenneth N. Timmis

  3. Department of Biology, University of the Balearic Islands and Mediterranean Institute for Advanced Studies (IMEDEA, UIB-CSIC), Palma de Mallorca, Spain

    Balbina Nogales

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© 2015 Springer-Verlag Berlin Heidelberg

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Pérez-Pantoja, D., Tamames, J. (2015). Prokaryotic Metatranscriptomics. In: McGenity, T., Timmis, K., Nogales , B. (eds) Hydrocarbon and Lipid Microbiology Protocols. Springer Protocols Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8623_2015_146

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  • DOI: https://doi.org/10.1007/8623_2015_146

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-50449-9

  • Online ISBN: 978-3-662-50450-5

  • eBook Packages: Springer Protocols

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