Tree Genetics & Genomes

, 12:30 | Cite as

RNA-seq analysis in forest tree species: bioinformatic problems and solutions

  • Unai López de Heredia
  • José Luis Vázquez-Poletti
Review
Part of the following topical collections:
  1. Gene Expression

Abstract

Direct sequencing of RNA (RNA-seq) using next-generation sequencing platforms has allowed a growing number of gene expression studies focused on forest trees in the last 5 years. Bioinformatic analyses derived from RNA-seq of forest trees are particularly challenging, because the massive genome length (~20.1 Gbp for loblolly pine) and the absence of annotated reference genomes require specific bioinformatic pipelines to obtain sound biological results. In the present manuscript, we review common bioinformatic challenges that researchers need to consider when analyzing RNA-seq data from forest tree species at the light of the experience acquired from recent studies. Furthermore, we list bioinformatic pipelines and data processing software available to overcome RNA-seq limitations. Finally, we discuss the impact of novel computation solutions, such as the cloud computing paradigm that allows RNA-seq analysis even for small research centers with limited resources.

Keywords

Cloud computing De novo assembly Differential expression Functional annotation RNA-seq Transcriptome 

Supplementary material

11295_2016_995_MOESM1_ESM.xlsx (203 kb)
ESM 1(XLSX 202 kb)

References

  1. Abbott E, Hall D, Hamberger B, Bohlmann J (2010) Laser microdissection of conifer stem tissues: isolation and analysis of high quality RNA, terpene synthase enzyme activity and terpenoid metabolites from resin ducts and cambial zone tissue of white spruce (Picea glauca). BMC Plant Biol 10:106. doi:10.1186/1471-2229-10-106 PubMedPubMedCentralCrossRefGoogle Scholar
  2. Äijö T, Butty V, Chen Z, Salo V, Tripathi S, Burge CB, Lahesmaa R, Lähdesmäki H (2014) Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation. Bioinformatics 30(12):i113–20. doi:10.1093/bioinformatics/btu274 PubMedPubMedCentralCrossRefGoogle Scholar
  3. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106. doi:10.1186/gb-2010-11-10-r106 PubMedPubMedCentralCrossRefGoogle Scholar
  4. Anders S, Reyes A, Huber W (2012) Detecting differential usage of exons from RNA-seq data. Genome Res 22(10):2008–17. doi:10.1101/gr.133744.111 PubMedPubMedCentralCrossRefGoogle Scholar
  5. Au KF, Jiang H, Lin L, Xing Y, Wong WH (2010) Detection of splice junctions from paired-end RNA-seq data by SpliceMap. Nucleic Acids Res 38(14):4570–8. doi:10.1093/nar/gkq211 PubMedPubMedCentralCrossRefGoogle Scholar
  6. Au KF, Underwood JG, Lee L, Wong WH (2012) Improving PacBio long read accuracy by short read alignment. PLoS One 7(10):e46679. doi:10.1371/journal.pone.0046679 PubMedPubMedCentralCrossRefGoogle Scholar
  7. Auer PL, Doerge RW (2011) A two-stage poisson model for testing RNAseq data. Stat Appl Genet Mol Biol 10(1):1–26Google Scholar
  8. Axtell MJ (2013) ShortStack: comprehensive annotation and quantification of small RNA genes. RNA 19(6):740–51. doi:10.1261/rna.035279.112 PubMedPubMedCentralCrossRefGoogle Scholar
  9. Baker M (2010) Next-generation sequencing: adjusting to data overload. Nat Methods 7(7):495–499. doi:10.1038/nmeth0710-495 CrossRefGoogle Scholar
  10. Birol I, Raymond A, Jackman SD, Pleasance S, Coope R, Taylor GA, Yuen MM, Keeling CI, Brand D, Vandervalk BP, Kirk H, Pandoh P, Moore RA, Zhao Y, Mungall AJ, Jaquish B, Yanchuk A, Ritland C, Boyle B, Bousquet J, Ritland K, Mackay J, Bohlmann J, Jones SJ (2013) Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics 29(12):1492–7. doi:10.1093/bioinformatics/btt178 PubMedPubMedCentralCrossRefGoogle Scholar
  11. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–20. doi:10.1093/bioinformatics/btu170 PubMedPubMedCentralCrossRefGoogle Scholar
  12. Brawand D, Soumillon M, Necsulea A, Julien P, Csárdi G, Harrigan P, Weier M, Liechti A, Aximu-Petri A, Kircher M, Albert FW, Zeller U, Khaitovich P, Grützner F, Bergmann S, Nielsen R, Pääbo S, Kaessmann H (2011) The evolution of gene expression levels in mammalian organs. Nature 478(7369):343–8. doi:10.1038/nature10532 PubMedCrossRefGoogle Scholar
  13. Brown CT, Howe, Zhang Q, Pyrkosz AB, Brom TH (2012) A reference-free algorithm for computational normalization of shotgun sequencing data. http://arxiv.org/pdf/1203.4802.pdf
  14. Bullard JH, Purdom E, Hansen KD, Dudoit S (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11:94. doi:10.1186/1471-2105-11-94 PubMedPubMedCentralCrossRefGoogle Scholar
  15. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. doi:10.1186/1471-2105-10-421 PubMedPubMedCentralCrossRefGoogle Scholar
  16. Campbell MS, Law MY, Holt C, Stein JC, Moghe GD, Hufnagel DE, Lei J, Achawanantakun R, Jiao D, Lawrence CJ, Ware D, Shiu S, Childs KL, Sun Y, Jiang N, Yandell M (2014) MAKER-P: a tool kit for the rapid creation, management, and quality control of plant genome annotations. Plant Physiol 164(2):513–524. doi:10.1104/pp.113.230144 PubMedPubMedCentralCrossRefGoogle Scholar
  17. Canales J, Bautista R, Label P, Gómez-Maldonado J, Lesur I, Fernández-Pozo N, Rueda-López M, Guerrero-Fernández D, Castro-Rodríguez V, Benzekri H, Cañas RA, Guevara MA, Rodrigues A, Seoane P, Teyssier C, Morel A, Ehrenmann F, Le Provost G, Lalanne C, Noirot C, Klopp C, Reymond I, García-Gutiérrez A, Trontin JF, Lelu-Walter MA, Miguel C, Cervera MT, Cantón FR, Plomion C, Harvengt L, Avila C, Gonzalo Claros M, Cánovas FM (2014) De novo assembly of maritime pine transcriptome: implications for forest breeding and biotechnology. Plant Biotechnol J 12(3):286–99. doi:10.1111/pbi.12136 PubMedCrossRefGoogle Scholar
  18. Chang Z, Li G, Liu J, Zhang Y, Ashby C, Liu D, Cramer CL, Huang X (2015) Bridger: a new framework for de novo transcriptome assembly using RNA-seq data. Genome Biol 16:30. doi:10.1186/s13059-015-0596-2 PubMedPubMedCentralCrossRefGoogle Scholar
  19. Chaves I, Lin YC, Pinto Ricardo C, Van de Peer Y, Miguel C (2014) miRNA profiling in leaf and cork tissues of Quercus suber reveals novel miRNAs and tissue-specific expression patterns. Tree Genet Genomes 10:721–737. doi:10.1007/s11295-014-0717-1 CrossRefGoogle Scholar
  20. Chen TW, Gan RC, Wu TH, Huang PJ, Lee CY, Chen YY, Chen CC, Tang P (2012) FastAnnotator—an efficient transcript annotation web tool. BMC Genomics 13(Suppl 7):S9. doi:10.1186/1471-2164-13-S7-S9 CrossRefGoogle Scholar
  21. Chevreaux G, Wetter T, Suhai S (1999) Genome sequence assembly using trace signals and additional sequence information. Comput Sci Biol Proc Ger Conf Bioinformatics 99:45–56Google Scholar
  22. Chu HT, Hsiao WW, Chen JC, Yeh TJ, Tsai MH, Lin H, Liu YW, Lee SA, Chen CC, Tsao TT, Kao CY (2013) EBARDenovo: highly accurate de novo assembly of RNA-Seq with efficient chimera-detection. Bioinformatics 29(8):1004–10. doi:10.1093/bioinformatics/btt092 PubMedCrossRefGoogle Scholar
  23. Chu C, Fang Z, Hua X, Yang Y, Chen E, Cowley AW Jr, Liang M, Liu P, Lu Y (2015) deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies. BMC Genomics 16:455. doi:10.1186/s12864-015-1676-0 PubMedPubMedCentralCrossRefGoogle Scholar
  24. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18):3674–6. doi:10.1093/bioinformatics/bti610 PubMedCrossRefGoogle Scholar
  25. Corney DC (2013) RNA-seq using next generation sequencing. Mater Methods 3:203. doi:10.13070/mm.en.3.203 CrossRefGoogle Scholar
  26. David M, Dzamba M, Lister D, Ilie L, Brudno M (2011) SHRiMP2: sensitive yet practical SHort Read Mapping. Bioinformatics 27(7):1011–2. doi:10.1093/bioinformatics/btr046 PubMedCrossRefGoogle Scholar
  27. López de Heredia (2014) ENT-RS-CLOUD RNA-seq differential Expression aNalysis for Tree species in the Cloud. Master Thesis. Bioinformatics and Computational Biology (CNIO-ENS). doi: 10.13140/2.1.1539.7129Google Scholar
  28. Deelman E, Singh G, Livny M, Berriman B, Good J (2008) The cost of doing science on the cloud: the montage example. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, IEEE Press, 2008; 50Google Scholar
  29. DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G (2012) RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28(11):1530–2. doi:10.1093/bioinformatics/bts196 PubMedPubMedCentralCrossRefGoogle Scholar
  30. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. doi:10.1093/bioinformatics/bts635 PubMedPubMedCentralCrossRefGoogle Scholar
  31. Dou Y, Guo X, Holding DR, Zhang C (2015) Differential expression analysis in RNA-Seq by a naive Bayes classifier with local normalization. BioMed Res Int Article ID 789516. doi:10.1155/2015/789516
  32. Eddy SR (2008) A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PLoS Comput Biol 4(5):e1000069. doi:10.1371/journal.pcbi.1000069 PubMedPubMedCentralCrossRefGoogle Scholar
  33. Eid J, Fehr A, Gray J, Luong K, Lyle J, Otto G, Peluso P, Rank D, Baybayan P, Bettman B, Bibillo A, Bjornson K, Chaudhuri B, Christians F, Cicero R, Clark S, Dalal R, Dewinter A, Dixon J, Foquet M, Gaertner A, Hardenbol P, Heiner C, Hester K, Holden D, Kearns G, Kong X, Kuse R, Lacroix Y, Lin S, Lundquist P, Ma C, Marks P, Maxham M, Murphy D, Park I, Pham T, Phillips M, Roy J, Sebra R, Shen G, Sorenson J, Tomaney A, Travers K, Trulson M, Vieceli J, Wegener J, Wu D, Yang A, Zaccarin D, Zhao P, Zhong F, Korlach J, Turner S (2009) Real-time DNA sequencing from single polymerase molecules. Science 323(5910):133–8. doi:10.1126/science.1162986 PubMedCrossRefGoogle Scholar
  34. Ekblom R, Galindo J (2011) Applications of next generation sequencing in molecular ecology of non-model organisms. Heredity 107:1–15. doi:10.1038/hdy.2010.152 PubMedPubMedCentralCrossRefGoogle Scholar
  35. Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR (2013) A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated rna-seq experiments. BMC Bioinformatics 14:254. doi:10.1186/1471-2105-14-254 PubMedPubMedCentralCrossRefGoogle Scholar
  36. Fernández-Pozo N, Canales J, Guerrero-Fernández D, Villalobos DP, Díaz-Moreno SM, Bautista R, Flores-Monterroso A, Guevara MA, Perdiguero P, Collada C, Cervera MT, Soto A, Ordás R, Cantón FR, Avila C, Cánovas FM, Claros MG (2011) EuroPineDB: a high-coverage web database for maritime pine transcriptome. BMC Genomics 12:366. doi:10.1186/1471-2164-12-36 PubMedPubMedCentralCrossRefGoogle Scholar
  37. Forster SC, Finkel AM, Gould JA, Hertzog PJ (2013) RNA-eXpress annotates novel transcript features in RNA-seq data. Bioinformatics 29(6):810–2. doi:10.1093/bioinformatics/btt034 PubMedPubMedCentralCrossRefGoogle Scholar
  38. Gebelin V, Argout X, Engchuan W, Pitollat B, Duan C, Montoro P, Leclercq J (2012) Identification of novel microRNAs in Hevea brasiliensis and computational prediction of their targets. BMC Plant Biol 12:18. doi:10.1186/1471-2229-12-18 PubMedPubMedCentralCrossRefGoogle Scholar
  39. Gibbons JG, Janson EM, Hittinger CT, Johnston M, Abbot P, Rokas A (2009) Benchmarking next-generation transcriptome sequencing for functional and evolutionary genomics. Mol Biol Evol 26(12):2731–44. doi:10.1093/molbev/msp188 PubMedCrossRefGoogle Scholar
  40. Glaus P, Honkela A, Rattray M (2012) Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics 28(13):1721–8. doi:10.1093/bioinformatics/bts260 PubMedPubMedCentralCrossRefGoogle Scholar
  41. Goecks J, Nekrutenko A, Taylor J (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11(8):R86. doi:10.1186/gb-2010-11-8-r86 PubMedPubMedCentralCrossRefGoogle Scholar
  42. Goff SA, Vaughn M, McKay S, Lyons E, Stapleton AE, Gessler D, Matasci N, Wang L, Hanlon M, Lenards A, Muir A, Merchant N, Lowry S, Mock S, Helmke M, Kubach A, Narro M, Hopkins N, Micklos D, Hilgert U, Gonzales M, Jordan C, Skidmore E, Dooley R, Cazes J, McLay R, Lu Z, Pasternak S, Koesterke L, Piel WH, Grene R, Noutsos C, Gendler K, Feng X, Tang C, Lent M, Kim SJ, Kvilekval K, Manjunath BS, Tannen V, Stamatakis A, Sanderson M, Welch SM, Cranston KA, Soltis P, Soltis D, O’Meara B, Ane C, Brutnell T, Kleibenstein DJ, White JW, Leebens-Mack J, Donoghue MJ, Spalding EP, Vision TJ, Myers CR, Lowenthal D, Enquist BJ, Boyle B, Akoglu A, Andrews G, Ram S, Ware D, Stein L, Stanzione D (2011) The iPlant collaborative: cyberinfrastructure for plant biology. Front Plant Sci 2:34. doi:10.3389/fpls.2011.00034 PubMedPubMedCentralCrossRefGoogle Scholar
  43. González-Ibeas D, Martinez-Garcia PJ, Famula L, Loopstra CA, Puryear J, Neale DB, Wegrzyn JL (2015) Survey of the sugar pine (Pinus lambertiana) transcriptome by deep sequencing. Plant and Animal Genome XXIII, Jan 10-14 2015, San Diego, CAGoogle Scholar
  44. Gordon A, Hannon GJ (2010) Fastx-toolkit. FASTQ/A short-reads preprocessing tools (unpublished). http://hannonlab.cshl.edu/fastx_toolkit
  45. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, Li B, Lieber M, Macmanes MD, Ott M, Orvis J, Pochet N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R, Leduc RD, Friedman N, Regev A (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8(8):1494–512. doi:10.1038/nprot.2013.084 PubMedCrossRefGoogle Scholar
  46. 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 PubMedPubMedCentralCrossRefGoogle Scholar
  47. Hayden KJ, Garbelotto M, Knaus BJ, Cronn RC, Rai H, Wright JW (2014) Dual RNA-seq of the plant pathogen Phytophthora ramorum and its tanoak host. Tree Genet Genomes 10(3):489–502. doi:10.1007/s11295-014-0698-0 CrossRefGoogle Scholar
  48. Hu M, Zhu Y, Taylor JMG, Liu JS, Qin Z (2012) Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.Vol. Bioinformatics 28(1):63–68. doi:10.1093/bioinformatics/btr616 PubMedPubMedCentralCrossRefGoogle Scholar
  49. Huang X, Madan A (1999) CAP3: a DNA sequence assembly program. Genome Res 9(9):868–77PubMedPubMedCentralCrossRefGoogle Scholar
  50. Huang S, Zhang J, Li R, Zhang W, He Z, Lam TW, Peng Z, Yiu SM (2011) SOAPsplice: genome-wide ab initio detection of splice junctions from RNA-Seq data. Front Genet 2:46. doi:10.3389/fgene.2011.00046 PubMedPubMedCentralCrossRefGoogle Scholar
  51. Jäger M, Ott CE, Grünhagen J, Hecht J, Schell H, Mundlos S, Duda GN, Robinson PN, Lienau J (2011) Composite transcriptome assembly of RNA-seq data in a sheep model for delayed bone healing. BMC Genomics 12:158. doi:10.1186/1471-2164-12-158 PubMedPubMedCentralCrossRefGoogle Scholar
  52. Jia W, Qiu K, He M, Song P, Zhou Q, Zhou F, Yu Y, Zhu D, Nickerson ML, Wan S, Liao X, Zhu X, Peng S, Li Y, Wang J, Guo G (2013) SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome Biol 14(2):R12. doi:10.1186/gb-2013-14-2-r12 PubMedPubMedCentralCrossRefGoogle Scholar
  53. Kent WJ (2002) BLAT—the BLAST-like alignment tool. Genome Res 12(4):656–64. doi:10.1101/gr.229202 PubMedPubMedCentralCrossRefGoogle Scholar
  54. Kim W (2009) Cloud computing: today and tomorrow. J Object Tech 8(1):65–72CrossRefGoogle Scholar
  55. Klambauer G, Unterthiner T, Hochreiter S (2013) DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions. Nucleic Acids Res 41(21):e198. doi:10.1093/nar/gkt834 PubMedPubMedCentralCrossRefGoogle Scholar
  56. Kovach A, Wegrzyn JL, Parra G, Holt C, Bruening GE, Loopstra CA, Hartigan J, Yandell M, Langley CH, Korf I, Neale DB (2010) The Pinus taeda genome is characterized by diverse and highly diverged repetitive sequences. BMC Genomics 11:420. doi:10.1186/1471-2164-11-420 PubMedPubMedCentralCrossRefGoogle Scholar
  57. Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42(Database issue):D68–73. doi:10.1093/nar/gkt1181 PubMedPubMedCentralCrossRefGoogle Scholar
  58. Krampis K, Booth T, Chapman B, Tiwari B, Bicak M, Field D, Nelson KE (2012) Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community. BMC Bioinformatics 3:42. doi:10.1186/1471-2105-13-42 CrossRefGoogle Scholar
  59. Kuosmanen A (2013) Comparison of spliced alignment software in analyzing RNA-Seq data. M.Sc. Thesis, University of Helsinki, Department of Computer Science. https://helda.helsinki.fi/bitstream/handle/10138/41904/masters_thesis_Kuosmanen_Anna.pdf?sequence=2
  60. Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25. doi:10.1186/gb-2009-10-3-r25 PubMedPubMedCentralCrossRefGoogle Scholar
  61. Langmead B, Hansen KD, Leek JT (2010) Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol 11:R83. doi:10.1186/gb-2010-11-8-r83 PubMedPubMedCentralCrossRefGoogle Scholar
  62. Le HS, Schulz MH, McCauley BM, Hinman VF, Bar-Joseph Z (2013) Probabilistic error correction for RNA sequencing. Nucleic Acids Res 41(10):e109. doi:10.1093/nar/gkt215 PubMedPubMedCentralCrossRefGoogle Scholar
  63. Lei J, Sun Y (2014) miR-PREFeR: an accurate, fast and easy-to-use plant miRNA prediction tool using small RNA-Seq data. Bioinformatics 30(19):2837–9. doi:10.1093/bioinformatics/btu380 PubMedCrossRefGoogle Scholar
  64. Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, Haag JD, Gould MN, Stewart RM, Kendziorski C (2013) EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 15;29(8):1035–43. doi:10.1093/bioinformatics/btt087 CrossRefGoogle Scholar
  65. Leroy P, Guilhot N, Sakai H, Bernard A, Choulet F, Theil S, Reboux S, Amano N, Flutre T, Pelegrin C, Ohyanagi H, Seidel M, Giacomoni F, Reichstadt M, Alaux M, Gicquello E, Legeai F, Cerutti L, Numa H, Tanaka T, Mayer K, Itoh T, Quesneville H, Feuillet C (2011) TriAnnot: a versatile and high performance pipeline for the automated annotation of plant genomes. Frontiers in Plant Science 3:1–14Google Scholar
  66. Levy A, Szwerdszarf D, Abu-Abied M, Mordehaev I, Yaniv Y, Riov J, Arazi T, Sadot E (2014) Profiling microRNAs in Eucalyptus grandis reveals no mutual relationship between alterations in miR156 and miR172 expression and adventitious root induction during development. BMC Genomics 15:524. doi:10.1186/1471-2164-15-524 PubMedPubMedCentralCrossRefGoogle Scholar
  67. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–60. doi:10.1093/bioinformatics/btp324 PubMedPubMedCentralCrossRefGoogle Scholar
  68. Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26(5):589–95. doi:10.1093/bioinformatics/btp698 PubMedPubMedCentralCrossRefGoogle Scholar
  69. Li J, Tibshirani R (2011) Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res 22(5):519–36. doi:10.1177/0962280211428386 PubMedPubMedCentralCrossRefGoogle Scholar
  70. Li R, Zhu H, Ruan J, Qian W, Fang X, Shi Z, Li Y, Li S, Shan G, Kristiansen K, Li S, Yang H, Wang J, Wang J (2010a) De novo assembly of human genomes with massively parallel short read sequencing. Genome Res 20(2):265–72. doi:10.1101/gr.097261.109 PubMedPubMedCentralCrossRefGoogle Scholar
  71. Li P, Ponnala L, Gandotra N, Wang L, Si Y, Tausta SL, Kebrom TH, Provart N, Patel R, Myers CR, Reidel EJ, Turgeon R, Liu P, Sun Q, Nelson T, Brutnell TP (2010b) The developmental dynamics of the maize leaf transcriptome. Nat Genet 42(12):1060–7. doi:10.1038/ng.703 PubMedCrossRefGoogle Scholar
  72. Li B, Qin Y, Duan H, Yin W, Xia X (2011a) Genome-wide characterization of new and drought stress responsive microRNAs in Populus euphratica. J Exp Bot 62(11):3765–79. doi:10.1093/jxb/err051 PubMedPubMedCentralCrossRefGoogle Scholar
  73. Li J, Witten DM, Johnstone I, Tibshirani R (2012) Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 13(3):523–38. doi:10.1093/biostatistics/kxr031
  74. Li W, Han S, Qi L (2014a) Transcriptome resources and genome-wide marker development for Japanese larch (Larix kaempferi). Front Agr Sci Eng 1(1):77–84. doi:10.15302/J-FASE-2014010 CrossRefGoogle Scholar
  75. Li B, Fillmore N, Bai Y, Collins M, Thomson JA, Stewart R, Dewey CN (2014b) Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biol 15(12):553. doi:10.1186/s13059-014-0553-5 PubMedPubMedCentralCrossRefGoogle Scholar
  76. Liu JJ, Sturrock RN, Benton R (2013) Transcriptome analysis of Pinus monticola primary needles by RNA-seq provides novel insight into host resistance to Cronartium ribicola. BMC Genomics 14:884. doi:10.1186/1471-2164-14-884 PubMedPubMedCentralCrossRefGoogle Scholar
  77. Liu W, Yu W, Hou L, Wang X, Zheng F, Wang W, Liang D, Yang H, Jin Y, Xie X (2014a) Analysis of miRNAs and their targets during adventitious shoot organogenesis of Acacia crassicarpa. PLoS One 9(4):e93438. doi:10.1371/journal.pone.0093438 PubMedPubMedCentralCrossRefGoogle Scholar
  78. Liu Y, Zhou J, White KP (2014b) RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30(3):301–4. doi:10.1093/bioinformatics/btt688 PubMedPubMedCentralCrossRefGoogle Scholar
  79. Lorenz WW, Ayyampalayam S, Bordeaux JM, Howe GT, Jermstad KD, Neale DB, Rogers DL, Dean JFD (2012) Conifer DBMagic: a database housing multiple de novo transcriptome assemblies for 12 diverse conifer species. Tree Genet Genomes 8:1477–1485. doi:10.1007/s11295-012-0547-y CrossRefGoogle Scholar
  80. Lu B, Zeng Z, Shi T (2013) Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq. Sci China Life Sci 56(2):143–55. doi:10.1007/s11427-013-4442-z PubMedCrossRefGoogle Scholar
  81. Lund S, Nettleton D, McCarthy D, Smyth G (2012) Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat Appl Genet Mol Biol 11(5):1826. doi:10.1515/1544-6115 Google Scholar
  82. Luo X, Gao Z, Shi T, Cheng Z, Zhang Z, Ni Z (2013) Identification of miRNAs and their target genes in peach (Prunus persica L.) using high-throughput sequencing and degradome analysis. PLoS One 8(11):e79090. doi:10.1371/journal.pone.0079090 PubMedPubMedCentralCrossRefGoogle Scholar
  83. Marçais G, Kingsford C (2011) A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27(6):764–70. doi:10.1093/bioinformatics/btr011 PubMedPubMedCentralCrossRefGoogle Scholar
  84. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, Lohman KL, Lu H, Makhijani VB, McDade KE, McKenna MP, Myers EW, Nickerson E, Nobile JR, Plant R, Puc BP, Ronan MT, Roth GT, Sarkis GJ, Simons JF, Simpson JW, Srinivasan M, Tartaro KR, Tomasz A, Vogt KA, Volkmer GA, Wang SH, Wang Y, Weiner MP, Yu P, Begley RF, Rothberg JM (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057):376–80. doi:10.1038/nature03959 PubMedPubMedCentralGoogle Scholar
  85. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18(9):1509–17. doi:10.1101/gr.079558.108 PubMedPubMedCentralCrossRefGoogle Scholar
  86. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao X, Carrington JC, Chen X, Green PJ, Griffiths-Jones S, Jacobsen SE, Mallory AC, Martienssen RA, Poethig RS, Qi Y, Vaucheret H, Voinnet O, Watanabe Y, Weigel D, Zhu JK (2008) Criteria for annotation of plant microRNAs. Plant Cell 20(12):3186–90. doi:10.1105/tpc.108.064311 PubMedPubMedCentralCrossRefGoogle Scholar
  87. Morgan M, Anders S, Lawrence M, Aboyoun P, Pagès H, Gentleman R (2009) ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25(19):2607–8. doi:10.1093/bioinformatics/btp450 PubMedPubMedCentralCrossRefGoogle Scholar
  88. Morse AM, Whetten RW, Dubos C, Campbell MM (2009) Post-translational modification of an R2R3-MYB transcription factor by a MAP kinase during xylem development. New Phytol 183(4):1001–13. doi:10.1111/j.1469-8137.2009.02900.x PubMedCrossRefGoogle Scholar
  89. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–8. doi:10.1038/nmeth.1226 PubMedCrossRefGoogle Scholar
  90. Myburg AA, Grattapaglia D, Tuskan GA, Hellsten U, Hayes RD, Grimwood J, Jenkins J, Lindquist E, Tice H, Bauer D, Goodstein DM, Dubchak I, Poliakov A, Mizrachi E, Kullan AR, Hussey SG, Pinard D, van der Merwe K, Singh P, van Jaarsveld I, Silva-Junior OB, Togawa RC, Pappas MR, Faria DA, Sansaloni CP, Petroli CD, Yang X, Ranjan P, Tschaplinski TJ, Ye CY, Li T, Sterck L, Vanneste K, Murat F, Soler M, Clemente HS, Saidi N, Cassan-Wang H, Dunand C, Hefer CA, Bornberg-Bauer E, Kersting AR, Vining K, Amarasinghe V, Ranik M, Naithani S, Elser J, Boyd AE, Liston A, Spatafora JW, Dharmwardhana P, Raja R, Sullivan C, Romanel E, Alves-Ferreira M, Külheim C, Foley W, Carocha V, Paiva J, Kudrna D, Brommonschenkel SH, Pasquali G, Byrne M, Rigault P, Tibbits J, Spokevicius A, Jones RC, Steane DA, Vaillancourt RE, Potts BM, Joubert F, Barry K, Pappas GJ, Strauss SH, Jaiswal P, Grima-Pettenati J, Salse J, Van de Peer Y, Rokhsar DS, Schmutz J (2014) The genome of Eucalyptus grandis. Nature 510(7505):356–62. doi:10.1038/nature13308 PubMedGoogle Scholar
  91. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320(5881):1344–9. doi:10.1126/science.1158441 PubMedPubMedCentralCrossRefGoogle Scholar
  92. Nagarajan N, Pop M (2013) Sequence assembly demystified. Nat Rev Genet 14:157–167. doi:10.1038/nrg3367 PubMedCrossRefGoogle Scholar
  93. Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. Nat Rev Genet 12(2):111–22. doi:10.1038/nrg2931 PubMedCrossRefGoogle Scholar
  94. Neale DB, Langley CH, Salzberg SL, Wegrzyn JL (2013) Open access to tree genomes: the path to a better forest. Genome Biol 14(6):120. doi:10.1186/gb-2013-14-6-120 PubMedPubMedCentralGoogle Scholar
  95. Neale DB, Wegrzyn JL, Stevens KA, Zimin AV, Puiu D, Crepeau MW, Cardeno C, Koriabine M, Holtz-Morris AE, Liechty JD, Martínez-García PJ, Vasquez-Gross HA, Lin BY, Zieve JJ, Dougherty WM, Fuentes-Soriano S, Wu LS, Gilbert D, Marçais G, Roberts M, Holt C, Yandell M, Davis JM, Smith KE, Dean JF, Lorenz WW, Whetten RW, Sederoff R, Wheeler N, McGuire PE, Main D, Loopstra CA, Mockaitis K, deJong PJ, Yorke JA, Salzberg SL, Langley CH (2014) Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies. Genome Biol 15(3):R59. doi:10.1186/gb-2014-15-3-r59 PubMedPubMedCentralCrossRefGoogle Scholar
  96. Niu S, Fan G, Xu E, Deng M, Zhao Z, Dong Y (2014) Transcriptome/degradome-wide discovery of microRNAs and transcript targets in two Paulownia australis genotypes. PLoS One 9(9):e106736. doi:10.1371/journal.pone.0106736 PubMedPubMedCentralCrossRefGoogle Scholar
  97. Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin YC, Scofield DG, Vezzi F, Delhomme N, Giacomello S, Alexeyenko A, Vicedomini R, Sahlin K, Sherwood E, Elfstrand M, Gramzow L, Holmberg K, Hällman J, Keech O, Klasson L, Koriabine M, Kucukoglu M, Käller M, Luthman J, Lysholm F, Niittylä T, Olson A, Rilakovic N, Ritland C, Rosselló JA, Sena J, Svensson T, Talavera-López C, Theißen G, Tuominen H, Vanneste K, Wu ZQ, Zhang B, Zerbe P, Arvestad L, Bhalerao R, Bohlmann J, Bousquet J, Garcia Gil R, Hvidsten TR, de Jong P, MacKay J, Morgante M, Ritland K, Sundberg B, Thompson SL, Van de Peer Y, Andersson B, Nilsson O, Ingvarsson PK, Lundeberg J, Jansson S (2013) The Norway spruce genome sequence and conifer genome evolution. Nature 497(7451):579–84. doi:10.1038/nature12211 PubMedCrossRefGoogle Scholar
  98. Oh S, Song S, Grabowski G, Zhao H, Noonan JP (2013) Time series expression analyses using RNA-seq: a statistical approach. Biomed Res Int 2013:203681. doi:10.1155/2013/203681 PubMedPubMedCentralGoogle Scholar
  99. O’Neil ST, Emrich SJ (2013) Assessing de novo transcriptome assembly metrics for consistency and utility. BMC Genomics 14:465. doi:10.1186/1471-2164-14-465 PubMedPubMedCentralCrossRefGoogle Scholar
  100. Parra G, Bradnam K, Korf I (2007) CEGMA: a pipeline to accurately annotate core genes in eukaryotic genornes. Bioinformatics 23:1061–1067. doi:10.1093/bioinformatics/btm071 PubMedCrossRefGoogle Scholar
  101. Pauli A, Valen E, Lin MF, Garber M, Vastenhouw NL, Levin JZ, Fan L, Sandelin A, Rinn JL, Regev A, Schier AF (2012) Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis. Genome Res 22(3):577–91. doi:10.1101/gr.133009.111 PubMedPubMedCentralCrossRefGoogle Scholar
  102. Pavy N, Johnson JJ, Crow JA, Paule C, Kunau T, MacKay J, Retzel EF (2007) ForestTreeDB: a database dedicated to the mining of tree transcriptomes. Nucleic Acids Res 35(Database issue):D888–94PubMedPubMedCentralCrossRefGoogle Scholar
  103. Peng Y, Leung HC, Yiu SM, Lv MJ, Zhu XG, Chin FY (2013) IDBA-tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven expression levels. Bioinformatics 29(13):i326–34. doi:10.1093/bioinformatics/btt219 PubMedPubMedCentralCrossRefGoogle Scholar
  104. Pertea G, Huang X, Liang F, Antonescu V, Sultana R, Karamycheva S, Lee Y, White J, Cheung F, Parvizi B, Tsai J, Quackenbush J (2003) TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets. Bioinformatics 19(5):651–2. doi:10.1093/bioinformatics/btg034 PubMedCrossRefGoogle Scholar
  105. Pinosio S, González-Martínez SC, Bagnoli F, Cattonaro F, Grivet D, Marroni F, Lorenzo Z, Pausas JG, Verdú M, Vendramin GG (2014) First insights into the transcriptome and development of new genomic tools of a widespread circum-Mediterranean tree species, Pinus halepensis Mill. Mol Ecol Resour 14(4):846–56. doi:10.1111/1755-0998.12232 PubMedCrossRefGoogle Scholar
  106. Planet E, Attolini CS, Reina O, Flores O, Rossell D (2012) htSeqTools: high-throughput sequencing quality control, processing and visualization in R. Bioinformatics 28(4):589–90. doi:10.1093/bioinformatics/btr700 PubMedCrossRefGoogle Scholar
  107. Plomion C, Aury JM, Amselem J, Alaeitabar T, Barbe V, Belser C, Bergès H, Bodénès C, Boudet N, Boury C, Canaguier A, Couloux A, Da Silva C, Duplessis S, Ehrenmann F, Estrada-Mairey B, Fouteau S, Francillonne N, Gaspin C, Guichard C, Klopp C, Labadie K, Lalanne C, Le Clainche I, Leplé JC, Le Provost G, Leroy T, Lesur I, Martin F, Mercier J, Michotey C, Murat F, Salin F, Steinbach D, Faivre-Rampant P, Wincker P, Salse J, Quesneville H, Kremer A (2015) Decoding the oak genome: public release of sequence data, assembly, annotation and publication strategies. Mol Ecol Res doi. doi:10.1111/1755-0998.12425 Google Scholar
  108. Rahman AY, Usharraj AO, Misra BB, Thottathil GP, Jayasekaran K, Feng Y, Hou S, Ong SY, Ng FL, Lee LS, Tan HS, Sakaff MK, Teh BS, Khoo BF, Badai SS, Aziz NA, Yuryev A, Knudsen B, Dionne-Laporte A, Mchunu NP, Yu Q, Langston BJ, Freitas TA, Young AG, Chen R, Wang L, Najimudin N, Saito JA, Alam M (2013) Draft genome sequence of the rubber tree Hevea brasiliensis. BMC Genomics 14:75. doi:10.1186/1471-2164-14-75 PubMedPubMedCentralCrossRefGoogle Scholar
  109. Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D (2013) Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol 14(9):R95. doi:10.1186/gb-2013-14-9-r95 PubMedPubMedCentralCrossRefGoogle Scholar
  110. Robasky K, Lewis NE, Church GM (2014) The role of replicates for error mitigation in next-generation sequencing. Nat Rev Genet 15(1):56–62. doi:10.1038/nrg3655 PubMedPubMedCentralCrossRefGoogle Scholar
  111. Robertson G, Schein J, Chiu R, Corbett R, Field M, Jackman SD, Mungall K, Lee S, Okada HM, Qian JQ, Griffith M, Raymond A, Thiessen N, Cezard T, Butterfield YS, Newsome R, Chan SK, She R, Varhol R, Kamoh B, Prabhu AL, Tam A, Zhao Y, Moore RA, Hirst M, Marra MA, Jones SJ, Hoodless PA, Birol I (2010) De novo assembly and analysis of RNA-seq data. Nat Methods 7(11):909–12. doi:10.1038/nmeth.1517 PubMedCrossRefGoogle Scholar
  112. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11(3):R25. doi:10.1186/gb-2010-11-3-r25 PubMedPubMedCentralCrossRefGoogle Scholar
  113. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–40. doi:10.1093/bioinformatics/btp616 PubMedPubMedCentralCrossRefGoogle Scholar
  114. Ruttink T, Sterck L, Rohde A, Bendixen C, Rouzé P, Asp T, Van de Peer Y, Roldan-Ruiz I (2013) Orthology Guided Assembly in highly heterozygous crops: creating a reference transcriptome to uncover genetic diversity in Lolium perenne. Plant Biotechnol J 11(5):605–17. doi:10.1111/pbi.12051 PubMedCrossRefGoogle Scholar
  115. Salzberg SL, Phillippy AM, Zimin A, Puiu D, Magoc T, Koren S, Treangen TJ, Schatz MC, Delcher AL, Roberts M, Marçais G, Pop M, Yorke JA (2012) GAGE: a critical evaluation of genome assemblies and assembly algorithms. Genome Res 22(3):557–67. doi:10.1101/gr.131383.111 PubMedPubMedCentralCrossRefGoogle Scholar
  116. 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(8):1086–92. doi:10.1093/bioinformatics/bts094 PubMedPubMedCentralCrossRefGoogle Scholar
  117. Sebastiana M, Vieira B, Lino-Neto T, Monteiro F, Figueiredo A, Sousa L, Pais MS, Tavares R, Paulo OS (2014) Oak root response to ectomycorrhizal symbiosis establishment: RNA-Seq derived transcript identification and expression profiling. PLoS One 9(5):e98376. doi:10.1371/journal.pone.0098376 PubMedPubMedCentralCrossRefGoogle Scholar
  118. Shuai P, Liang D, Tang S, Zhang Z, Ye CY, Su Y, Xia X, Yin W (2014) Genome-wide identification and functional prediction of novel and drought-responsive lincRNAs in Populus trichocarpa. J Exp Bot 65(17):4975–83. doi:10.1093/jxb/eru256 PubMedPubMedCentralCrossRefGoogle Scholar
  119. Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJ, Birol I (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19(6):1117–23. doi:10.1101/gr.089532.108 PubMedPubMedCentralCrossRefGoogle Scholar
  120. Skidmore E, Kim SJ, Kuchimanchi S, Singaram S, Merchant N, Stanzione D (2011) iPlant atmosphere: a gateway to cloud infrastructure for the plant sciences. Presented at the Proceedings of the 2011 ACM workshop on Gateway computing environments, Seattle, Washington, USAGoogle Scholar
  121. Smyth G (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using R and bioconductor. Springer, NY, pp 397–420CrossRefGoogle Scholar
  122. Sreedharan VT, Schultheiss SJ, Jean G, Kahles A, Bohnert R, Drewe P, Mudrakarta P, Görnitz N, Zeller G, Rätsch G (2014) Oqtans: the RNA-seq workbench in the cloud for complete and reproducible quantitative transcriptome analysis. Bioinformatics 30(9):1300–1. doi:10.1093/bioinformatics/btt731 PubMedPubMedCentralCrossRefGoogle Scholar
  123. Stegle O, Denby KJ, Cooke EJ, Wild DL, Ghahramani Z, Bordkwart KM (2010) A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. J Comput Biol 17:355–367. doi:10.1089/cmb.2009.0175 PubMedPubMedCentralCrossRefGoogle Scholar
  124. Sun YH, Shi R, Zhang XH, Chiang VL, Sederoff RR (2012) MicroRNAs in trees. Plant Mol Biol 80(1):37–53. doi:10.1007/s11103-011-9864-z PubMedCrossRefGoogle Scholar
  125. Sun X, Dong B, Yin L, Zhang R, Du W, Liu D, Shi N, Li A, Liang Y, Mao L (2013) PMTED: a plant microRNA target expression database. BMC Bioinformatics 14:174. doi:10.1186/1471-2105-14-174 PubMedPubMedCentralCrossRefGoogle Scholar
  126. Tarazona S, García-Alcalde F, Dopazo J, Ferrer A, Conesa A (2011) Differential expression in RNA-seq: a matter of depth. Genome Res 21(12):2213–23. doi:10.1101/gr.124321.111 PubMedPubMedCentralCrossRefGoogle Scholar
  127. Tarkka MT, Herrmann S, Wubet T, Feldhahn L, Recht S, Kurth F, Mailänder S, Bönn M, Neef M, Angay O, Bacht M, Graf M, Maboreke H, Fleischmann F, Grams TE, Ruess L, Schädler M, Brandl R, Scheu S, Schrey SD, Grosse I, Buscot F (2013) OakContigDF159.1, a reference library for studying differential gene expression in Quercus robur during controlled biotic interactions: use for quantitative transcriptomic profiling of oak roots in ectomycorrhizal symbiosis. New Phytol 199(2):529–40. doi:10.1111/nph.12317 PubMedCrossRefGoogle Scholar
  128. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–11. doi:10.1093/bioinformatics/btp120 PubMedPubMedCentralCrossRefGoogle Scholar
  129. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28(5):511–5. doi:10.1038/nbt.1621 PubMedPubMedCentralCrossRefGoogle Scholar
  130. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (2013) Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31(1):46–53. doi:10.1038/nbt.2450 PubMedCrossRefGoogle Scholar
  131. Tuskan GA, Difazio S, Jansson S, Bohlmann J, Grigoriev I, Hellsten U, Putnam N, Ralph S, Rombauts S, Salamov A, Schein J, Sterck L, Aerts A, Bhalerao RR, Bhalerao RP, Blaudez D, Boerjan W, Brun A, Brunner A, Busov V, Campbell M, Carlson J, Chalot M, Chapman J, Chen GL, Cooper D, Coutinho PM, Couturier J, Covert S, Cronk Q, Cunningham R, Davis J, Degroeve S, Déjardin A, Depamphilis C, Detter J, Dirks B, Dubchak I, Duplessis S, Ehlting J, Ellis B, Gendler K, Goodstein D, Gribskov M, Grimwood J, Groover A, Gunter L, Hamberger B, Heinze B, Helariutta Y, Henrissat B, Holligan D, Holt R, Huang W, Islam-Faridi N, Jones S, Jones-Rhoades M, Jorgensen R, Joshi C, Kangasjärvi J, Karlsson J, Kelleher C, Kirkpatrick R, Kirst M, Kohler A, Kalluri U, Larimer F, Leebens-Mack J, Leplé JC, Locascio P, Lou Y, Lucas S, Martin F, Montanini B, Napoli C, Nelson DR, Nelson C, Nieminen K, Nilsson O, Pereda V, Peter G, Philippe R, Pilate G, Poliakov A, Razumovskaya J, Richardson P, Rinaldi C, Ritland K, Rouzé P, Ryaboy D, Schmutz J, Schrader J, Segerman B, Shin H, Siddiqui A, Sterky F, Terry A, Tsai CJ, Uberbacher E, Unneberg P, Vahala J, Wall K, Wessler S, Yang G, Yin T, Douglas C, Marra M, Sandberg G, Van de Peer Y, Rokhsar D (2006) The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313(5793):1596–604. doi:10.1126/science.1128691 PubMedCrossRefGoogle Scholar
  132. Ulitsky I, Bartel DP (2013) lincRNAs: genomics, evolution, and mechanisms. Cell 154(1):26–46. doi:10.1016/j.cell.2013.06.020 PubMedPubMedCentralCrossRefGoogle Scholar
  133. Van Bel M, Proost S, Wischnitzki E, Movahedi S, Scheerlinck C, Van de Peer Y, Vandepoele K (2012) Dissecting plant genomes with the PLAZA comparative genomics platform. Plant Physiol 158(2):590–600. doi:10.1104/pp.111.189514 PubMedPubMedCentralCrossRefGoogle Scholar
  134. Van de Wiel MA, Neerincx M, Buffart TE, Sie D, Verheul HMW (2014) ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs. BMC Bioinformatics 15(1):116. doi:10.1186/1471-2105-15-116 PubMedPubMedCentralCrossRefGoogle Scholar
  135. Wagner GP, Kin K, Lynch VJ (2012) Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131(4):281–5. doi:10.1007/s12064-012-0162-3 PubMedCrossRefGoogle Scholar
  136. Wang Z, Gerstein M, Snyder M (2009) RNA-seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. doi:10.1038/nrg2484 PubMedPubMedCentralCrossRefGoogle Scholar
  137. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, He X, Mieczkowski P, Grimm SA, Perou CM, MacLeod JN, Chiang DY, Prins JF, Liu J (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38(18):e178. doi:10.1093/nar/gkq622 PubMedPubMedCentralCrossRefGoogle Scholar
  138. Wang L, Wang S, Li W (2012) RSeQC: quality control of RNA-seq experiments. Bioinformatics 28(16):2184–5. doi:10.1093/bioinformatics/bts356 PubMedCrossRefGoogle Scholar
  139. Wang X, Halakivi-Clarke L, Clarke R, Xuan J (2014) BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data. Bioinformatics 15(Suppl 9):S6. doi:10.1186/1471-2105-15-S9-S6 PubMedPubMedCentralGoogle Scholar
  140. Ward JA, Ponnala L, Weber CA (2012) Strategies for transcriptome analysis in nonmodel plants. Am J Bot 99(2):267–276. doi:10.3732/ajb.1100334 PubMedCrossRefGoogle Scholar
  141. Wegrzyn JL, Lee JM, Tearse BR, Neale DB (2008) TreeGenes: a forest tree genome database. Int J Plant Genomics 2008: Article ID 412875. doi:10.1155/2008/412875
  142. Wegrzyn JL, Lin BY, Zieve JJ, Dougherty WM, Martínez-García PJ, Koriabine M, Holtz-Morris A, deJong P, Crepeau M, Langley CH, Puiu D, Salzberg SL, Neale DB, Stevens KA (2013) Insights into the loblolly pine genome: characterization of BAC and fosmid sequences. PLoS One 8(9):e72439. doi:10.1371/journal.pone.0072439 PubMedPubMedCentralCrossRefGoogle Scholar
  143. Wegrzyn JL, Liechty JD, Stevens KA, Wu LS, Loopstra CA, Vasquez-Gross HA, Dougherty WM, Lin BY, Zieve JJ, Martínez-García PJ, Holt C, Yandell M, Zimin AV, Yorke JA, Crepeau MW, Puiu D, Salzberg SL, Dejong PJ, Mockaitis K, Main D, Langley CH, Neale DB (2014) Unique features of the loblolly pine (Pinus taeda L.) megagenome revealed through sequence annotation. Genetics 196(3):891–909. doi:10.1534/genetics.113.159996 PubMedPubMedCentralCrossRefGoogle Scholar
  144. Westermann AJ, Gorski SA, Vogel J (2012) Dual RNA-seq of pathogen and host. Nat Rev Microbiol 10(9):618–30. doi:10.1038/nrmicro2852 PubMedCrossRefGoogle Scholar
  145. Wu TD, Nacu S (2010) Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26(7):873–81. doi:10.1093/bioinformatics/btq057 PubMedPubMedCentralCrossRefGoogle Scholar
  146. Wu TD, Watanabe CK (2005) GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21(9):1859–75. doi:10.1093/bioinformatics/bti310 PubMedCrossRefGoogle Scholar
  147. Wu H, Wang C, Wu Z (2013) A new shrinkage estimator for dispersion improves differential expression detection in rna-seq data. Biostatistics 14(2):232–243. doi:10.1093/biostatistics/kxs033 PubMedPubMedCentralCrossRefGoogle Scholar
  148. Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, Huang W, He G, Gu S, Li S, Zhou X, Lam TW, Li Y, Xu X, Wong GK, Wang J (2014) SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics 30(12):1660–6. doi:10.1093/bioinformatics/btu077 PubMedCrossRefGoogle Scholar
  149. Yanming D, Schafer DW, Cumbie JS, Chang JH (2011) The NBP negative binomial model for assessing differential gene expression from RNA-Seq. Stat Appl Genet Mol Biol 10(1):1–28Google Scholar
  150. Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18(5):821–9. doi:10.1101/gr.074492.107 PubMedPubMedCentralCrossRefGoogle Scholar
  151. Zhang Z, Yu J, Li D, Zhang Z, Liu F, Zhou X, Wang T, Ling Y, Su Z (2010) PMRD: plant microRNA database. Nucleic Acids Res 38(Database issue):D806–13. doi:10.1093/nar/gkp818 PubMedPubMedCentralCrossRefGoogle Scholar
  152. Zhang J, Zhang S, Han S, Li X, Tong Z, Qi L (2013) Deciphering small noncoding RNAs during the transition from dormant embryo to germinated embryo in larches (Larix leptolepis). PLoS One 8(12):e81452. doi:10.1371/journal.pone.0081452 PubMedPubMedCentralCrossRefGoogle Scholar
  153. Zhang ZH, Jhaveri DJ, Marshall VM, Bauer DC, Edson J, Narayanan RK, Robinson GJ, Lundberg AE, Bartlett PF, Wray NR, Zhao QY (2014) A comparative study of techniques for differential expression analysis on RNA-Seq data. PLoS One 9(8):e103207. doi:10.1371/journal.pone.0103207 PubMedPubMedCentralCrossRefGoogle Scholar
  154. Zhang H, Xu J, Jiang N, Hu X, Luo Z (2015) PLNseq: a multivariate Poisson lognormal distribution for high-throughput matched RNA-sequencing read count data. Stats Med 34(9):1577–1589. doi:10.1002/sim.6449 CrossRefGoogle Scholar
  155. Zhao S, Prenger K, Smith L (2013) Stormbow: a cloud-based tool for reads mapping and expression quantification in large-scale RNA-seq studies. ISRN Bioinformatics Article ID 481545:8 pages. doi: 10.1155/2013/481545
  156. Zhou YH, Xia K, Wright FA (2011) A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 27(19):2672–2678. doi:10.1093/bioinformatics/btr449 PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Unai López de Heredia
    • 1
  • José Luis Vázquez-Poletti
    • 2
  1. 1.Forest Genetics and Ecophysiology Research Group, E.T.S. Forestry EngineeringTechnical University of Madrid (UPM)MadridSpain
  2. 2.Distributed Systems Architecture Research Group, Department of Computer Architecture and AutomationComplutense UniversityMadridSpain

Personalised recommendations