Advertisement

RNA-Seq Data Analysis for Studying Abiotic Stress in Horticultural Plants

  • V. V. Mironova
  • C. Weinholdt
  • I. Grosse
Chapter

Abstract

Initiating the project on sequencing the Arabidopsis thaliana L. genome at the end of the twentieth century, researchers one day wished to expand the accumulated knowledge on Arabidopsis genetics to horticultural plants. The future arrived with the appearance of high-throughput sequencing technologies that allowed the investigation of transcriptomes of non-model plants at an unprecedented pace. RNA-seq experiments provide a unique opportunity of studying in depth the molecular-genetic basis for plant response to environmental cues.

Here we substantiate the potential of RNA-seq experiments in applications to horticultural plants. The basic steps in RNA-seq data analysis and available software packages are presented in the first section. Examples of RNA-seq data analyses, including studies of gene expression changes under various stresses in horticultural plants, and transcriptome analyses of the tolerance to abiotic stresses in horticultural plants are given in the second section.

Keywords

Genomics Horticultural plants RNA-seq Stress response Transcriptomics 

Notes

Acknowledgments

We thank A.V. Kochetov, I. Lemnian, and N.A. Omelyanchuk for fruitful discussions and Dynasty Foundation (grant for young biologists), DFG (grant no. GR 3523/2), RAS program 6.6, and RFBR Foundation (grant no. 12-04-33112) for financial support.

References

  1. Alamancos GP, Agirre E, Eyras E (2014) Methods to study splicing from high-throughput RNA sequencing data. Methods Mol Biol 1126:357–397. doi: 10.1007/978-1-62703-980-2_26 PubMedCrossRefGoogle Scholar
  2. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106. doi: 10.1186/gb-2010-11-10-r106 PubMedCentralPubMedCrossRefGoogle Scholar
  3. Anders S, Reyes A, Huber W (2012) Detecting differential usage of exons from RNA-seq data. Genome Res 22:2008–2017. doi: 10.1101/gr.133744.111 PubMedCentralPubMedCrossRefGoogle Scholar
  4. Anders S, McCarthy DJ, Chen Y et al (2013) Count-based differential expression analysis of RNA sequencing data using R and bioconductor. Nat Protoc 8:1765–1786. doi: 10.1038/nprot.2013.099 PubMedCrossRefGoogle Scholar
  5. Anders S, Pyl PT, Huber W (2014) HTSeq A Python framework to work with high-throughput sequencing data. BioRxiv doi:10.1101/002824Google Scholar
  6. Arenhart RA, de Lima JC, Pedron M et al (2013) Involvement of ASR genes in aluminium tolerance mechanisms in rice. Plant Cell Environ 36:52–67. doi: 10.1111/j.1365-3040.2012.02553.x PubMedCrossRefGoogle Scholar
  7. Birol I, Jackman SD, Nielsen CB et al (2009) De novo transcriptome assembly with ABySS. Bioinformatics 25:2872–2877. doi: 10.1093/bioinformatics/btp367 PubMedCrossRefGoogle Scholar
  8. Boley N, Stoiber MH, Booth BW et al (2014) Genome-guided transcript assembly by integrative analysis of RNA sequence data. Nat Biotechnol 32:341–346. doi: 10.1038/nbt.2850 PubMedCentralPubMedCrossRefGoogle Scholar
  9. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193PubMedCrossRefGoogle Scholar
  10. Bonnet E, He Y, Billiau K, Van de Peer Y (2010) TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 26:1566–1568. doi: 10.1093/bioinformatics/btq233 PubMedCrossRefGoogle Scholar
  11. Bowman MJ, Park W, Bauer PJ et al (2013) RNA-Seq transcriptome profiling of upland cotton (Gossypium hirsutum L.) root tissue under water-deficit stress. PLoS One 8:e82634PubMedCentralPubMedCrossRefGoogle Scholar
  12. Chen S, Huang X, Yan X et al (2013) Transcriptome analysis in sheepgrass (Leymus chinensis): a dominant perennial grass of the Eurasian Steppe. PLoS One 8:e67974. doi: 10.1371/journal.pone.0067974 PubMedCentralPubMedCrossRefGoogle Scholar
  13. Coate JE, Powell AF, Owens TG, Doyle JJ (2013) Transgressive physiological and transcriptomic responses to light stress in allopolyploid Glycine dolichocarpa (Leguminosae). Heredity (Edinb) 110:160–170. doi: 10.1038/hdy.2012.77 CrossRefGoogle Scholar
  14. Cossu RM, Giordani T, Cavallini A, Natali L (2013) High-throughput analysis of transcriptome variation during water deficit in a poplar hybrid: a general overview. Tree Genet Genomes 10:53–66. doi: 10.1007/s11295-013-0661-5 CrossRefGoogle Scholar
  15. Danecek P, Auton A, Abecasis G et al (2011) The variant call format and VCF tools. Bioinformatics 27:2156–2158. doi: 10.1093/bioinformatics/btr330 PubMedCentralPubMedCrossRefGoogle Scholar
  16. Dang Z, Zheng L, Wang J et al (2013) Transcriptomic profiling of the salt-stress response in the wild recretohalophyte Reaumuria trigyna. BMC Genomics 14:29 (doi:10.1186/1471-2164-14-29)Google Scholar
  17. 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. doi: 10.1371/journal.pone.0085024 PubMedCentralPubMedCrossRefGoogle Scholar
  18. Delahaie J, Hundertmark M, Bove J et al (2013) LEA polypeptide profiling of recalcitrant and orthodox legume seeds reveals ABI3-regulated LEA protein abundance linked to desiccation tolerance. J Exp Bot 64:4559–4573. doi: 10.1093/jxb/ert274 PubMedCentralPubMedCrossRefGoogle Scholar
  19. DePristo MA, Banks E, Poplin R et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–498. doi: 10.1038/ng.806 PubMedCentralPubMedCrossRefGoogle Scholar
  20. Dillies M-A, Rau A, Aubert J et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683. doi: 10.1093/bib/bbs046 PubMedCrossRefGoogle Scholar
  21. Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21. doi: 10.1093/bioinformatics/bts635 PubMedCentralPubMedCrossRefGoogle Scholar
  22. Fan M, Huang Y, Zhong Y et al (2014) Comparative transcriptome profiling of potassium starvation responsiveness in two contrasting watermelon genotypes. Planta (Berl) 239:397–410. doi: 10.1007/s00425-013-1976-z CrossRefGoogle Scholar
  23. Fasold M, Langenberger D, Binder H et al (2011) DARIO: a ncRNA detection and analysis tool for next-generation sequencing experiments. Nucleic Acids Res 39:W112–W117. doi: 10.1093/nar/gkr357 PubMedCentralPubMedCrossRefGoogle Scholar
  24. Fonseca NA, Rung J, Brazma A, Marioni JC (2012) Tools for mapping high-throughput sequencing data. Bioinformatics 28:3169–3177. doi: 10.1093/bioinformatics/bts605 PubMedCrossRefGoogle Scholar
  25. Garg R, Verma M, Agrawal S et al (2014) Deep transcriptome sequencing of wild halophyte rice, Porteresia coarctata, provides novel insights into the salinity and submergence tolerance factors. DNA Res 21:69–84. doi: 10.1093/dnares/dst042 PubMedCentralPubMedCrossRefGoogle Scholar
  26. Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80. doi: 10.1186/gb-2004-5-10-r80 PubMedCentralPubMedCrossRefGoogle Scholar
  27. Git A, Dvinge H, Salmon-Divon M et al (2010) Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 16:991–1006. doi: 10.1261/rna.1947110 PubMedCentralPubMedCrossRefGoogle Scholar
  28. Gomes CPC, Cho J-H, Hood L et al (2013) A review of computational tools in microRNA discovery. Front Genet 4:81. doi: 10.3389/fgene.2013.00081 PubMedCentralPubMedCrossRefGoogle Scholar
  29. Gross SM, Martin JA, Simpson J et al (2013) De novo transcriptome assembly of drought tolerant CAM plants, Agave deserti and Agave tequilana. BMC Genomics 14:563. doi: 10.1186/1471-2164-14-563 PubMedCentralPubMedCrossRefGoogle Scholar
  30. Guttman M, Garber M, Levin JZ et al (2010) Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol 28:503–510. doi: 10.1038/nbt.1633 PubMedCentralPubMedCrossRefGoogle Scholar
  31. Haas BJ, Papanicolaou A, Yassour M 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. doi: 10.1038/nprot.2013.084 PubMedCrossRefGoogle Scholar
  32. Hackenberg M, Rodríguez-Ezpeleta N, Aransay AM (2011) miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 39:W132–W138. doi: 10.1093/nar/gkr247 PubMedCentralPubMedCrossRefGoogle Scholar
  33. Hardcastle TJ, Kelly KA (2010) BaySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11:422. doi: 10.1186/1471-2105-11-422 PubMedCentralPubMedCrossRefGoogle Scholar
  34. Hertel J, Hofacker IL, Stadler PF (2008) SnoReport: computational identification of snoRNAs with unknown targets. Bioinformatics 24:158–164. doi: 10.1093/bioinformatics/btm464 PubMedCrossRefGoogle Scholar
  35. Hoffmann S, Otto C, Doose G et al (2014) A multi-split mapping algorithm for circular RNA, splicing, trans-splicing, and fusion detection. Genome Biol 15:R34. doi: 10.1186/gb-2014-15-2-r34 PubMedCentralPubMedCrossRefGoogle Scholar
  36. Jean G, Kahles A, Sreedharan VT et al (2010) RNA-Seq read alignments with PALMapper. Curr Protoc Bioinformatics. doi: 10.1002/0471250953.bi1106s32 PubMedGoogle Scholar
  37. Jia J, Zhao S, Kong X et al (2013) Aegilops tauschii draft genome sequence reveals a gene repertoire for wheat adaptation. Nature (Lond) 496:91–95. doi: 10.1038/nature12028 CrossRefGoogle Scholar
  38. Kakumanu A, Ambavaram MMR, Klumas C et al (2012) Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-Seq. Plant Physiol 160:846–867. doi: 10.1104/pp. 112.200444 PubMedCentralPubMedCrossRefGoogle Scholar
  39. Keller O, Kollmar M, Stanke M, Waack S (2011) A novel hybrid gene prediction method employing protein multiple sequence alignments. Bioinformatics 27:757–763. doi: 10.1093/bioinformatics/btr010 PubMedCrossRefGoogle Scholar
  40. Koenig D, Jiménez-Gómez JM, Kimura S et al (2013) Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato. Proc Natl Acad Sci USA 110:E2655–E2662. doi: 10.1073/pnas.1309606110 PubMedCentralPubMedCrossRefGoogle Scholar
  41. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39:D152–D157. doi: 10.1093/nar/gkq1027 PubMedCentralPubMedCrossRefGoogle Scholar
  42. Kvam VM, Liu P, Si Y (2012) A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot 99:248–256. doi: 10.3732/ajb.1100340 PubMedCrossRefGoogle Scholar
  43. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. doi: 10.1038/nmeth.1923 PubMedCentralPubMedCrossRefGoogle Scholar
  44. Lawrence M, Huber W, Pagès H et al (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9:e1003118. doi: 10.1371/journal.pcbi.1003118 PubMedCentralPubMedCrossRefGoogle Scholar
  45. Lee LW, Zhang S, Etheridge A et al (2010) Complexity of the microRNA repertoire revealed by next-generation sequencing. RNA 16:2170–2180. doi: 10.1261/rna.2225110 PubMedCentralPubMedCrossRefGoogle Scholar
  46. Lee J, Noh EK, Choi H-S et al (2013) Transcriptome sequencing of the Antarctic vascular plant Deschampsia antarctica Desv. under abiotic stress. Planta (Berl) 237:823–836. doi: 10.1007/s00425-012-1797-5 CrossRefGoogle Scholar
  47. Leng N, Dawson JA, Thomson JA et al (2013) EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29:1035–1043. doi: 10.1093/bioinformatics/btt087 PubMedCentralPubMedCrossRefGoogle Scholar
  48. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323. doi: 10.1186/1471-2105-12-323 PubMedCentralPubMedCrossRefGoogle Scholar
  49. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760. doi: 10.1093/bioinformatics/btp324 PubMedCentralPubMedCrossRefGoogle Scholar
  50. Li H, Homer N (2010) A survey of sequence alignment algorithms for next-generation sequencing. Brief Bioinform 11:473–483. doi: 10.1093/bib/bbq015 PubMedCentralPubMedCrossRefGoogle Scholar
  51. Li H, Ruan J, Durbin R (2008a) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18:1851–1858. doi: 10.1101/gr.078212.108 PubMedCentralPubMedCrossRefGoogle Scholar
  52. Li R, Li Y, Kristiansen K, Wang J (2008b) SOAP: short oligonucleotide alignment program. Bioinformatics 24:713–714. doi: 10.1093/bioinformatics/btn025 PubMedCrossRefGoogle Scholar
  53. Li J, Witten DM, Johnstone IM, Tibshirani R (2012) Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 13:523–538. doi: 10.1093/biostatistics/kxr031 PubMedCentralPubMedCrossRefGoogle Scholar
  54. Liao Y, Smyth GK, Shi W (2014) Feature counts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930. doi: 10.1093/bioinformatics/btt656 PubMedCrossRefGoogle Scholar
  55. Liu F, Wang W, Sun X et al (2013) RNA-Seq revealed complex response to heat stress on transcriptomic level in Saccharina japonica (Laminariales, Phaeophyta). J Appl Phycol. doi: 10.1007/s10811-013-0188-z PubMedCentralPubMedGoogle Scholar
  56. Lohse M, Bolger AM, Nagel A et al (2012) RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics. Nucleic Acids Res 40:W622–W627. doi: 10.1093/nar/gks540 PubMedCentralPubMedCrossRefGoogle Scholar
  57. Luo R, Liu B, Xie Y et al (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1:18. doi: 10.1186/2047-217X-1-18 PubMedCentralPubMedCrossRefGoogle Scholar
  58. Ma J, Zhang M, Xiao X et al (2013) Global transcriptome profiling of Salicornia europaea L. shoots under NaCl treatment. PLoS One 8:e65877PubMedCentralPubMedCrossRefGoogle Scholar
  59. Marcolino-Gomes J, Rodrigues FA, Oliveira MCN et al (2013) Expression patterns of GmAP2/EREB-like transcription factors involved in soybean responses to water deficit. PLoS One 8:e62294. doi: 10.1371/journal.pone.0062294 PubMedCentralPubMedCrossRefGoogle Scholar
  60. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10. doi:10.14806/ej.17.1.200Google Scholar
  61. Martin JA, Wang Z (2011) Next-generation transcriptome assembly. Nat Rev Genet 12:671–682. doi: 10.1038/nrg3068 PubMedCrossRefGoogle Scholar
  62. Martin J, Bruno VM, Fang Z et al (2010) Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. BMC Genomics 11:663. doi: 10.1186/1471-2164-11-663 PubMedCentralPubMedCrossRefGoogle Scholar
  63. Massa AN, Childs KL, Buell CR (2013) Abiotic and biotic stress responses in group Phureja DM1-3 516 R44 as measured through whole transcriptome sequencing. Plant Genome 6:1–10. doi: 10.3835/plantgenome2013.05.0014 CrossRefGoogle Scholar
  64. McGettigan PA (2013) Transcriptomics in the RNA-seq era. Curr Opin Chem Biol 17:4–11. doi: 10.1016/j.cbpa.2012.12.008 PubMedCrossRefGoogle Scholar
  65. Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev Genet 11:31–46. doi: 10.1038/nrg2626 PubMedCrossRefGoogle Scholar
  66. Motameny S, Wolters S, Nürnberg P, Schumacher B (2010) Next generation sequencing of miRNAs: strategies, resources and methods. Genes (Basel) 1:70–84. doi: 10.3390/genes1010070 CrossRefGoogle Scholar
  67. Müller BSDF, Sakamoto T, Silveira RDD et al. (2013) Differentially expressed genes during flowering and grain filling in common bean (Phaseolus vulgaris) grown under drought stress conditions. Plant Mol Biol Rep 438–451. doi:10.1007/s11105-013-0651-7Google Scholar
  68. O’Rourke JA, Yang SS, Miller SS et al (2013) An RNA-Seq transcriptome analysis of orthophosphate-deficient white lupin reveals novel insights into phosphorus acclimation in plants. Plant Physiol 161:705–724. doi: 10.1104/pp. 112.209254 PubMedCentralPubMedCrossRefGoogle Scholar
  69. Oono Y, Kawahara Y, Yazawa T et al (2013) Diversity in the complexity of phosphate starvation transcriptomes among rice cultivars based on RNA-Seq profiles. Plant Mol Biol 83:523–537. doi: 10.1007/s11103-013-0106-4 PubMedCentralPubMedCrossRefGoogle Scholar
  70. Ozhuner E, Eldem V, Ipek A et al (2013) Boron stress responsive microRNAs and their targets in barley. PLoS One 8:e59543. doi: 10.1371/journal.pone.0059543 PubMedCentralPubMedCrossRefGoogle Scholar
  71. Pang T, Ye C-Y, Xia X, Yin W (2013) De novo sequencing and transcriptome analysis of the desert shrub, Ammopiptanthus mongolicus, during cold acclimation using Illumina/Solexa. BMC Genomics 14:488. doi: 10.1186/1471-2164-14-488 PubMedCentralPubMedCrossRefGoogle Scholar
  72. Pantano L, Estivill X, Martí E (2010) SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Res 38:e34. doi: 10.1093/nar/gkp1127 PubMedCentralPubMedCrossRefGoogle Scholar
  73. Pertea G, Huang X, Liang F et al (2003) TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics 19:651–652. doi: 10.1093/bioinformatics/btg034 PubMedCrossRefGoogle Scholar
  74. Postnikova OA, Shao J, Nemchinov LG (2013) Analysis of the alfalfa root transcriptome in response to salinity stress. Plant Cell Physiol 54:1041–1055. doi: 10.1093/pcp/pct056 PubMedCrossRefGoogle Scholar
  75. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842. doi: 10.1093/bioinformatics/btq033 PubMedCentralPubMedCrossRefGoogle Scholar
  76. Raney JA, Reynolds DJ, Elzinga DB et al (2014) Transcriptome analysis of drought-induced stress in Chenopodium quinoa. Am J Plant Sci 2014:338–357CrossRefGoogle Scholar
  77. Rapaport F, Khanin R, Liang Y et al (2013) Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol 14:R95. doi: 10.1186/gb-2013-14-9-r95 PubMedCentralPubMedCrossRefGoogle Scholar
  78. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. doi: 10.1093/bioinformatics/btp616 PubMedCentralPubMedCrossRefGoogle Scholar
  79. Rumble SM, Lacroute P, Dalca AV et al (2009) SHRiMP: accurate mapping of short color-space reads. PLoS Comput Biol 5:e1000386. doi: 10.1371/journal.pcbi.1000386 PubMedCentralPubMedCrossRefGoogle Scholar
  80. Schmieder R, Edwards R (2011) Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One 6:e17288. doi: 10.1371/journal.pone.0017288 PubMedCentralPubMedCrossRefGoogle Scholar
  81. Schweikert G, Zien A, Zeller G et al (2009) mGene: accurate SVM-based gene finding with an application to nematode genomes. Genome Res 19:2133–2143. doi: 10.1101/gr.090597.108 PubMedCentralPubMedCrossRefGoogle Scholar
  82. Silva GG, Dutilh BE, Matthews TD et al (2013) Combining de novo and reference-guided assembly with scaffold_builder. Source Code Biol Med 8:23. doi: 10.1186/1751-0473-8-23 PubMedCentralPubMedCrossRefGoogle Scholar
  83. Simpson JT, Wong K, Jackman SD et al (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19:1117–1123. doi: 10.1101/gr.089532.108 PubMedCentralPubMedCrossRefGoogle Scholar
  84. Smyth G (2005) Limma: linear models for microarray data. Bioinform Comput Biol Sol R Bioconductor. doi: 10.1007/0-387-29362-0 Google Scholar
  85. Soneson C, Delorenzi M (2013) A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14:91. doi: 10.1186/1471-2105-14-91 PubMedCentralPubMedCrossRefGoogle Scholar
  86. Stanke M, Diekhans M, Baertsch R, Haussler D (2008) Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics 24:637–644. doi: 10.1093/bioinformatics/btn013 PubMedCrossRefGoogle Scholar
  87. Steijger T, Abril JF, Engström PG et al (2013) Assessment of transcript reconstruction methods for RNA-seq. Nat Methods 10:1177–1184. doi: 10.1038/nmeth.2714 PubMedCrossRefGoogle Scholar
  88. Tang S, Liang H, Yan D et al (2013) Populus euphratica: the transcriptomic response to drought stress. Plant Mol Biol 83:539–557. doi: 10.1007/s11103-013-0107-3 PubMedCrossRefGoogle Scholar
  89. Teune J-H, Steger G (2010) NOVOMIR: de novo prediction of microRNA-coding regions in a single plant-genome. J Nucleic Acids. doi: 10.4061/2010/495904 PubMedCentralPubMedGoogle Scholar
  90. Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. doi: 10.1093/bib/bbs017 PubMedCentralPubMedCrossRefGoogle Scholar
  91. Tian D-Q, Pan X-Y, Yu Y-M et al (2013) De novo characterization of the Anthurium transcriptome and analysis of its digital gene expression under cold stress. BMC Genomics 14:827. doi: 10.1186/1471-2164-14-827 PubMedCentralPubMedCrossRefGoogle Scholar
  92. Tombuloglu H, Kekec G, Sakcali MS, Unver T (2013) Transcriptome-wide identification of R2R3-MYB transcription factors in barley with their boron responsive expression analysis. Mol Genet Genomics 288:141–155. doi: 10.1007/s00438-013-0740-1 PubMedCrossRefGoogle Scholar
  93. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–1111. doi: 10.1093/bioinformatics/btp120 PubMedCentralPubMedCrossRefGoogle Scholar
  94. Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515. doi: 10.1038/nbt.1621 PubMedCentralPubMedCrossRefGoogle Scholar
  95. Trapnell C, Hendrickson DG, Sauvageau M et al (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31:46–53. doi: 10.1038/nbt.2450 PubMedCrossRefGoogle Scholar
  96. Villar E, Klopp C, Noirot C et al (2011) RNA-Seq reveals genotype-specific molecular responses to water deficit in eucalyptus. BMC Genomics 12:538. doi: 10.1186/1471-2164-12-538 PubMedCentralPubMedCrossRefGoogle Scholar
  97. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63PubMedCentralPubMedCrossRefGoogle Scholar
  98. Wang C, Gao C, Wang L, et al. (2013a) Comprehensive transcriptional profiling of NaHCO3-stressed Tamarix hispida roots reveals networks of responsive genes. Plant Mol Biol 84(1-2):145–157. doi: 10.1007/s11103-013-0124-2Google Scholar
  99. Wang H, Zou Z, Wang S, Gong M (2013b) Global analysis of transcriptome responses and gene expression profiles to cold stress of Jatropha curcas L. PLoS One 8:e82817. doi: 10.1371/journal.pone.0082817 PubMedCentralPubMedCrossRefGoogle Scholar
  100. Wang J, Lan P, Gao H et al (2013c) Expression changes of ribosomal proteins in phosphate- and iron-deficient Arabidopsis roots predict stress-specific alterations in ribosome composition. BMC Genomics 14:783. doi: 10.1186/1471-2164-14-783 PubMedCentralPubMedCrossRefGoogle Scholar
  101. Wang X-C, Zhao Q-Y, Ma C-L et al (2013d) Global transcriptome profiles of Camellia sinensis during cold acclimation. BMC Genomics 14:415. doi: 10.1186/1471-2164-14-415 PubMedCentralPubMedCrossRefGoogle Scholar
  102. Wang Y, Xu L, Chen Y et al (2013e) Transcriptome profiling of radish (Raphanus sativus L.) root and identification of genes involved in response to lead (Pb) stress with next generation sequencing. PLoS One 8:e66539PubMedCentralPubMedCrossRefGoogle Scholar
  103. Wen M, Shen Y, Shi S, Tang T (2012) MiREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments. BMC Bioinformatics 13:140. doi: 10.1186/1471-2105-13-140 PubMedCentralPubMedCrossRefGoogle Scholar
  104. Witkos TM, Koscianska E, Krzyzosiak WJ (2011) Practical aspects of microRNA target prediction. Curr Mol Med 11:93–109PubMedCentralPubMedCrossRefGoogle Scholar
  105. Wu J, Liu Q, Wang X et al (2013) mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol 10:1087–1092. doi: 10.4161/rna.25193 PubMedCentralPubMedCrossRefGoogle Scholar
  106. Xie F, Stewart CN, Taki FA et al (2013) High-throughput deep sequencing shows that microRNAs play important roles in switchgrass responses to drought and salinity stress. Plant Biotechnol J 159:1–13. doi: 10.1111/pbi.12142 Google Scholar
  107. Xie Y, Wu G, Tang J et al (2014) SOAP denovo-trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics 30:1660–1666. doi: 10.1093/bioinformatics/btu077 PubMedCrossRefGoogle Scholar
  108. Xu P, Liu Z, Fan X et al (2013a) De novo transcriptome sequencing and comparative analysis of differentially expressed genes in Gossypium aridum under salt stress. Gene (Amst) 525:26–34. doi: 10.1016/j.gene.2013.04.066 CrossRefGoogle Scholar
  109. Xu Y, Gao S, Yang Y et al (2013b) Transcriptome sequencing and whole genome expression profiling of chrysanthemum under dehydration stress. BMC Genomics 14:662. doi: 10.1186/1471-2164-14-662 PubMedCentralPubMedCrossRefGoogle Scholar
  110. Yang X, Li L (2011) miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics 27:2614–2615. doi: 10.1093/bioinformatics/btr430 PubMedGoogle Scholar
  111. Zavolan M, Kondo S, Schonbach C et al (2003) Impact of alternative initiation, splicing, and termination on the diversity of the mRNA transcripts encoded by the mouse transcriptome. Genome Res 13:1290–1300. doi: 10.1101/gr.1017303 PubMedCentralPubMedCrossRefGoogle Scholar
  112. Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–829. doi: 10.1101/gr.074492.107 PubMedCentralPubMedCrossRefGoogle Scholar
  113. Zhang J, Mao Z, Chong K (2013a) A global profiling of uncapped mRNAs under cold stress reveals specific decay patterns and endonucleolytic cleavages in Brachypodium distachyon. Genome Biol 14:R92. doi: 10.1186/gb-2013-14-8-r92 PubMedCentralPubMedCrossRefGoogle Scholar
  114. Zhang L-M, Liu X-G, Qu X-N et al (2013b) Early transcriptomic adaptation to Na2CO3 stress altered the expression of a quarter of the total genes in the maize genome and exhibited shared and distinctive profiles with NaCl and high pH stresses. J Integr Plant Biol 55:1147–1165. doi: 10.1111/jipb.12100 PubMedCrossRefGoogle Scholar
  115. Zhang X, Yao D, Wang Q et al (2013c) mRNA-seq analysis of the Gossypium arboreum transcriptome reveals tissue selective signaling in response to water stress during seedling stage. PLoS One 8:e54762. doi: 10.1371/journal.pone.0054762 PubMedCentralPubMedCrossRefGoogle Scholar
  116. Zhang N, Liu B, Ma C et al (2014) Transcriptome characterization and sequencing-based identification of drought-responsive genes in potato. Mol Biol Rep 41:505–517. doi: 10.1007/s11033-013-2886-7 PubMedCrossRefGoogle Scholar
  117. Ziemann M, Kamboj A, Hove RM et al (2013) Analysis of the barley leaf transcriptome under salinity stress using mRNA-Seq. Acta Physiol Plant 35:1915–1924. doi: 10.1007/s11738-013-1230-0 CrossRefGoogle Scholar
  118. Zong W, Zhong X, You J, Xiong L (2013) Genome-wide profiling of histone H3K4-tri-methylation and gene expression in rice under drought stress. Plant Mol Biol 81:175–188. doi:10.1007/s11103-012-9990-2 10.1007/s11103-012-9990-2 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  1. 1.Institute of Cytology and Genetics SB RASNovosibirskRussia
  2. 2.Novosibirsk State UniversityNovosibirskRussia
  3. 3.Institute of Computer ScienceMartin Luther University Halle-WittenbergHalleGermany
  4. 4.German Center of Integrative Biodiversity Research (iDiv) Halle-Jena-LeipzigLeipzigGermany

Personalised recommendations