What method could we use to sequence the mRNA of the plant and quantify it at the same time

A real time PCR assay for the peanut allergen Ara h 2 has been developed with a sensitivity of 10 copies per reaction of the target gene [51];

From: Food Toxicants Analysis, 2007

  1. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5:1–8.

    Article  CAS  Google Scholar 

  2. Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N, et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods. 2010;7:709–15.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Parkhomchuk D, Borodina T, Amstislavskiy V, Banaru M, Hallen L, Krobitsch S, et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 2009;37:e123.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. Katz Y, Wang ET, Airoldi EM, Burge CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods. 2010;7:1009–15.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods. 2011;8:469–77.

    CAS  PubMed  Article  Google Scholar 

  6. Łabaj PP, Leparc GG, Linggi BE, Markillie LM, Wiley HS, Kreil DP. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics. 2011;27:i383–91.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  7. Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014;15:121–32.

    CAS  PubMed  Article  Google Scholar 

  8. Pollen AA, Nowakowski TJ, Shuga J, Wang X, Leyrat AA, Lui JH, et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol. 2014;32:1053–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343:776–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome Res. 2011;21:2213–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Andrews S. FASTQC. A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 29 September 2014.

  12. Dai M, Thompson RC, Maher C, Contreras-Galindo R, Kaplan MH, Markovitz DM, et al. NGSQC: cross-platform quality analysis pipeline for deep sequencing data. BMC Genomics. 2010;11 Suppl 4:S7.

    PubMed  PubMed Central  Article  Google Scholar 

  13. FASTX-Toolkit. http://hannonlab.cshl.edu/fastx_toolkit/. Accessed 12 January 2016.

  14. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    CAS  PubMed  Article  Google Scholar 

  16. Picard. http://picard.sourceforge.net/. Accessed 12 January 2016.

  17. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28:2184–5.

    CAS  PubMed  Article  Google Scholar 

  18. García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28:2678–9.

    PubMed  Article  CAS  Google Scholar 

  19. Tarazona S, Furió-Tarí P, Turrà D, Pietro AD, Nueda MJ, Ferrer A, et al. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. 2015;43:e140.

    PubMed  PubMed Central  Google Scholar 

  20. Risso D, Schwartz K, Sherlock G, Dudoit S. GC-content normalization for RNA-seq data. BMC Bioinformatics. 2011;12:480.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Steijger T, Abril JF, Engström PG, Kokocinski F, Hubbard TJ, Guigó R, et al. Assessment of transcript reconstruction methods for RNA-seq. Nat Methods. 2013;10:1177–84.

    CAS  PubMed  Article  Google Scholar 

  22. Boley N, Stoiber MH, Booth BW, Wan KH, Hoskins RA, Bickel PJ, et al. Genome-guided transcript assembly by integrative analysis of RNA sequence data. Nat Biotechnol. 2014;32:341–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Roberts A, Pimentel H, Trapnell C, Pachter L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics. 2011;27:2325–9.

    CAS  PubMed  Article  Google Scholar 

  24. Mezlini AM, Smith EJ, Fiume M, Buske O, Savich GL, Shah S, et al. iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data. Genome Res. 2013;23:519–29.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Li JJ, Jiang CR, Brown JB, Huang H, Bickel PJ. Sparse linear modeling of next-generation mRNA sequencing (RNA-Seq) data for isoform discovery and abundance estimation. Proc Natl Acad Sci U S A. 2011;108:19867–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Hiller D, Wong WH. Simultaneous isoform discovery and quantification from RNA-Seq. Stat Biosci. 2013;5:100–18.

    PubMed  Article  Google Scholar 

  28. Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34:W435–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Engström PG, Steijger T, Sipos B, Grant GR, Kahles A, Rätsch G, et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods. 2013;10:1185–91.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30:1660–6.

    CAS  PubMed  Article  Google Scholar 

  31. Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28:1086–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novotranscript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.

    CAS  PubMed  Article  Google Scholar 

  34. Patro R, Mount SM, Kingsford C. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol. 2014;32:462–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Anders S, Pyl PT, Huber W. HTSeq - a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

    CAS  PubMed  Article  Google Scholar 

  36. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

    CAS  PubMed  Article  Google Scholar 

  37. UCSC Genome Bioinformatics: Frequently Asked Questions: Data File Formats. https://genome.ucsc.edu/FAQ/FAQformat.html#format4. Accessed on 12 January 2016.

  38. Pachter L. Models for transcript quantification from RNA-seq. arXiv.org. 2011. http://arxiv.org/abs/1104.3889. Accessed 6 January 2016.

  39. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Roberts A, Pachter L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nat Methods. 2013;10:71–3.

    CAS  PubMed  Article  Google Scholar 

  42. Bray N, Pimentel H, Melsted P, Pachter L. Near-optimal RNA-Seq quantification with kallisto. https://liorpachter.wordpress.com/2015/05/10/near-optimal-rna-seq-quantification-with-kallisto/. Accessed 6 January 2016.

  43. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L. Improving RNA-seq expression estimates by correcting for fragment bias. Genome Biol. 2011;12:R22.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Ma X, Zhang X. NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data. BMC Bioinformatics. 2013;14:220.

    PubMed  PubMed Central  Article  Google Scholar 

  45. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010;11:94.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. Hansen K, Brenner S, Dudoit S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 2010;38:e131.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11:R25.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Li J, Witten DM, Johnstone IM, Tibshirani R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics. 2012;13:523–38.

    PubMed  Article  Google Scholar 

  50. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2012;31:46–53.

    PubMed  Article  CAS  Google Scholar 

  51. Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics. 2010;185:405–16.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Johnson WE, Rabinovic A, Li C. Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics. 2007;8:118–27.

    PubMed  Article  Google Scholar 

  53. Nueda MJ, Ferrer A, Conesa A. ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics. 2012;13:553–66.

    PubMed  Article  Google Scholar 

  54. Robinson MD, Smyth GK. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics. 2007;23:2881–7.

    CAS  PubMed  Article  Google Scholar 

  55. Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:R29.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:91.

    PubMed  Article  PubMed Central  Google Scholar 

  57. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    CAS  PubMed  Article  Google Scholar 

  58. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  59. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics. 2010;11:422.

    PubMed  PubMed Central  Article  Google Scholar 

  60. Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013;29:1035–43.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Li J, Tibshirani R. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res. 2013;22:519–36.

    PubMed  Article  Google Scholar 

  62. Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010;26:136–8.

    PubMed  Article  CAS  Google Scholar 

  63. Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 2013;14:R95.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  64. Seyednasrollah F, Laiho A, Elo LL. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform. 2015;16:59–70.

    Article  PubMed  Google Scholar 

  65. Zheng S, Chen L. A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level. Nucleic Acids Res. 2009;37:e75.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  66. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–78.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Singh D, Orellana CF, Hu Y, Jones CD, Liu Y, Chiang DY, et al. FDM: a graph-based statistical method to detect differential transcription using RNA-seq data. Bioinformatics. 2011;27:2633–40.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Shi Y, Jiang H. rSeqDiff: detecting differential isoform expression from RNA-Seq data using hierarchical likelihood ratio test. PLoS One. 2013;8:e79448.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  69. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data. Genome Res. 2012;22:2008–17.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Wang W, Qin Z, Feng Z, Wang X, Zhang X. Identifying differentially spliced genes from two groups of RNA-seq samples. Gene. 2013;518:164–70.

    CAS  PubMed  Article  Google Scholar 

  71. Shen S, Park JW, Lu ZX, Lin L, Henry MD, Wu YN, et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc Natl Acad Sci U S A. 2014;111:E5593–601.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. Drewe P, Stegle O, Hartmann L, Kahles A, Bohnert R, Wachter A, et al. Accurate detection of differential RNA processing. Nucleic Acids Res. 2013;41:5189–98.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. Hu Y, Huang Y, Du Y, Orellana CF, Singh D, Johnson AR, et al. DissSplice: the genome-wide detection of differential splicing events with RNA-seq. Nucleic Acids Res. 2013;41:e39.

    CAS  PubMed  Article  Google Scholar 

  74. Hilker R, Stadermann KB, Doppmeier D, Kalinowski J, Stoye J, Straube J, et al. ReadXplorer - visualization and analysis of mapped sequences. Bioinformatics. 2014;30:2247–54.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The Human Genome Browser at UCSC. Genome Res. 2002;12:996–1006.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high- performance genomics data visualization and exploration. Brief Bioinformatics. 2012;14:178–92.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  77. Medina I, Salavert F, Sanchez R, de Maria A, Alonso R, Escobar P, et al. Genome Maps, a new generation genome browser. Nucleic Acids Res. 2013;41(Web Server issue):W41–6.

    PubMed  PubMed Central  Article  Google Scholar 

  78. Fiume M, Williams V, Brook A, Brudno M. Savant: genome browser for high-throughput sequencing data. Bioinformatics. 2010;26:1938–44.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. Rogé X, Zhang X. RNAseqViewer: visualization tool for RNA-Seq data. Bioinformatics. 2013;30:891–2.

    PubMed  Article  CAS  Google Scholar 

  80. Katz Y, Wang ET, Silterra J, Schwartz S, Wong B, Thorvaldsdóttir H, et al. Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics. 2015;31:2400–2.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. Wu E, Nance T, Montgomery SB. SplicePlot: a utility for visualizing splicing quantitative trait loci. Bioinformatics. 2014;30:1025–6.

    CAS  PubMed  Article  Google Scholar 

  82. Ryan MC, Cleland J, Kim R, Wong WC, Weinstein JN. SpliceSeq: a resource for analysis and visualization of RNA-Seq data on alternative splicing and its functional impacts. Bioinformatics. 2012;28:2385–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. Liu Q, Chen C, Shen E, Zhao F, Sun Z, Wu J. Detection, annotation and visualization of alternative splicing from RNA-Seq data with SplicingViewer. Genomics. 2012;99:178–82.

    CAS  PubMed  Article  Google Scholar 

  84. Dietrich S, Wiegand S, Liesegang H. TraV: a genome context sensitive transcriptome browser. PLoS One. 2014;9:e93677.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  85. Carrara M, Beccuti M, Lazzarato F, Cavallo F, Cordero F, Donatelli S, et al. State-of-the-art fusion-finder algorithms sensitivity and specificity. BioMed Res Int. 2013;15:340620.

    Google Scholar 

  86. Maher CA, Palanisamy N, Brenner JC, Cao X, Kalyana-Sundaram S, Luo S, et al. Chimeric transcript discovery by paired-end transcriptome sequencing. Proc Natl Acad Sci U S A. 2009;106:12353–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. Yoshihara K, Wang Q, Torres-Garcia W, Zheng S, Vegesna R, Kim H, et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene. 2015;34:4845–54.

    CAS  PubMed  Article  Google Scholar 

  88. McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MG, et al. deFuse: an algorithm for gene fusion discovery in tumor RNA-seq data. PLoS Comput Biol. 2011;7:e1001138.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. Wu C, Wyatt AW, McPherson A, Lin D, McConeghy BJ, Mo F, et al. Poly-gene fusion transcripts and chromothripsis in prostate cancer. Gene Chromosomes Cancer. 2012;51:1144–53.

    CAS  Article  Google Scholar 

  90. Wyatt AW, Mo F, Wang K, McConeghy B, Brahmbhatt S, Jong L, et al. Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer. Genome Biol. 2014;15:426.

    PubMed  PubMed Central  Article  Google Scholar 

  91. Stransky N, Cerami E, Schalm S, Kim JL, Lengauer C. The landscape of kinase fusions in cancer. Nat Commun. 2014;5:4846.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. Rabbitts TH. Commonality but diversity in cancer gene fusions. Cell. 2009;137:391–5.

    CAS  PubMed  Article  Google Scholar 

  93. McPherson A, Wu C, Hajirasouliha I, Hormozdiari F, Hach F, Lapuk A, et al. Comrad: detection of expressed rearrangements by integrated analysis of RNA-Seq and low coverage genome sequence data. Bioinformatics. 2011;27:1481–8.

    CAS  PubMed  Article  Google Scholar 

  94. Iyer MK, Chinnaiyan AM, Maher CA. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics. 2011;27:2903–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. Pflueger D, Terry S, Sboner A, Habegger L, Esgueva R, Lin PC, et al. Discovery of non-ETS gene fusions in human prostate cancer using next-generation RNA sequencing. Genome Res. 2011;21:56–67.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. Wu J, Liu Q, Wang X, Zheng J, Wang T, You M, et al. mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing. RNA Biol. 2013;10:1087–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. Prüfer K, Stenzel U, Dannemann M, Green RE, Lachmann M, Kelso J. PatMaN: rapid alignment of short sequences to large databases. Bioinformatics. 2008;24:1530–1.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  100. Emde AK, Grunert M, Weese D, Reinert K, Sperling SR. MicroRazerS: rapid alignment of small RNA reads. Bioinformatics. 2010;26:123–4.

    CAS  PubMed  Article  Google Scholar 

  101. An J, Lai J, Lehman ML, Nelson CC. miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res. 2013;41:727–37.

    CAS  PubMed  Article  Google Scholar 

  102. Yang X, Li L. miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics. 2011;27:2614–5.

    CAS  PubMed  Google Scholar 

  103. Stocks MB, Moxon S, Mapleson D, Woolfenden HC, Mohorianu I, Folkes L, et al. The UEA sRNA workbench: a suite of tools for analysing and visualizing next generation sequencing microRNA and small RNA datasets. Bioinformatics. 2012;28:2059–61.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. Axtell MJ. ShortStack: comprehensive annotation and quantification of small RNA genes. RNA. 2013;19:740–51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. Giurato G, De Filippo MR, Rinaldi A, Hashim A, Nassa G, Ravo M, et al. iMir: an integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq. BMC Bioinformatics. 2013;14:362.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  106. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:1–12.

    Article  CAS  Google Scholar 

  107. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7.

    PubMed  PubMed Central  Article  Google Scholar 

  108. Wang X, Cairns MJ. Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing. BMC Bioinformatics. 2013;14 Suppl 5:S16.

    PubMed  PubMed Central  Article  Google Scholar 

  109. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protocols. 2009;4:44–57.

    CAS  Article  Google Scholar 

  112. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13.

    Article  CAS  Google Scholar 

  113. Medina I, Carbonell J, Pulido L, Madeira SC, Goetz S, Conesa A, et al. Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res. 2010;38 suppl 2:W210–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. Bairoch A, Boeckmann B, Ferro S, Gasteiger E. Swiss-Prot: juggling between evolution and stability. Brief Bioinformatics. 2004;5:39–55.

    CAS  PubMed  Article  Google Scholar 

  115. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. The Pfam protein families database. Nucleic Acids Res. 2014;42(Database issue):D222–30.

    CAS  PubMed  Article  Google Scholar 

  116. Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 2011;40(Database issue):D306–12.

    PubMed  PubMed Central  Google Scholar 

  117. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–6.

    CAS  PubMed  Article  Google Scholar 

  118. Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, et al. Rfam: updates to the RNA families database. Nucleic Acids Res. 2009;37 suppl 1:D136–40.

    CAS  PubMed  Article  Google Scholar 

  119. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42(Database issue):D68–73.

    CAS  PubMed  Article  Google Scholar 

  120. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in Drosophila. Genome Biol. 2003;5:R1.

    PubMed  PubMed Central  Article  Google Scholar 

  121. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10:e1004383.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  122. Moffatt MF, Kabesch M, Liang L, Dixon AL, Strachan D, Heath S, et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature. 2007;448:470–3.

    CAS  PubMed  Article  Google Scholar 

  123. Gilad Y, Rifkin S, Pritchard J. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet. 2008;24:408–15.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. Gaffney D. Global properties and functional complexity of human gene regulatory variation. PLoS Genet. 2013;9:e1003501.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  125. Montgomery S, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature. 2010;464:773–7.

    CAS  PubMed  Article  Google Scholar 

  126. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464:768–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. Lappalainen T, Sammeth M, Friedlander M, ‘t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506–11.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  128. Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, Shi J, et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 2014;24:14–24.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. Pastinen T. Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet. 2010;11:533–8.

    CAS  PubMed  Article  Google Scholar 

  130. Sun W. A statistical framework for eQTL mapping using RNA-seq data. Biometrics. 2012;68:1–11.

    PubMed  Article  Google Scholar 

  131. van de Geijn B, McVicker G, Gilad Y, Pritchard JK. WASP: allele-specific for robust molecular quantitative trait locus discovery. Nat Methods. 2015;12:1061–3.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  132. Kumasaka N, Knights AJ, Gaffney DJ. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat Genet. 2015. doi: 10.1038/ng.3467.

  133. Storey JD, Tibshirani R. Statistical significance for genome-wide studies. Proc Natl Acad Sci U S A. 2003;100:9440–5.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  134. Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, et al. Genome-wide associations of gene expression variation in humans. PLoS Genet. 2005;1:e78.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  135. Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, Replogle JM, et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science. 2014;344:519–23.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  136. Shabalin A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28:1353–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. Louhimo R, Lepikhova T, Monni O, Hautaniemi S. Comparative analysis of algorithms for integration of copy number and expression data. Nat Methods. 2012;9:351–5.

    CAS  PubMed  Article  Google Scholar 

  138. Kim JH, Dhanasekaran SM, Prensner JR, Cao X, Robinson D, Kalyana-Sundaram S, et al. Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer. Genome Res. 2011;21:1028–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  139. Li JL, Mazar J, Zhong C, Faulkner GJ, Govindarajan SS, Zhang Z, et al. Genome-wide methylated CpG island profiles of melanoma cells reveal a melanoma coregulation network. Sci Rep. 2013;3:2962.

    PubMed  PubMed Central  Google Scholar 

  140. Xie L, Weichel B, Ohm JE, Zhang K. An integrative analysis of DNA methylation and RNA-Seq data for human heart, kidney and liver. BMC Syst Biol. 2011;5 Suppl 3:S4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  141. Van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics. 2012;13:636.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  142. Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31:142–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  143. Yeang C-H. An integrated analysis of molecular aberrations in NCI-60 cell lines. BMC Bioinformatics. 2010;11:495.

    PubMed  PubMed Central  Google Scholar 

  144. Jeong J, Li L, Liu Y, Nephew KP, Huang YHM, Shen C. An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer. BMC Med Genomics. 2010;3:55.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  145. Jiao Y, Widschwendter M, Teschendorff AE. A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics. 2014;30:2360–6.

    CAS  PubMed  Article  Google Scholar 

  146. Wang S, Sun H, Ma J, Zang C, Wang C, Wang J, et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc. 2013;8:2502–15.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  147. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317–30.

    PubMed Central  Article  CAS  Google Scholar 

  148. Madrigal P, Krajewski P. Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform. BioData Min. 2015;8:20.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  149. Angelini C, Costa V. Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems. Front Cell Dev Biol. 2014;2:51.

    PubMed  PubMed Central  Article  Google Scholar 

  150. Neph S, Stergachis AB, Reynolds A, Sandstrom R, Borenstein E, Stamatoyannopoulos JA. Circuitry and dynamics of human transcription factor regulatory networks. Cell. 2012;150:1274–86.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  151. Dweep H, Sticht C, Pandey P, Gretz N. miRWalk - database: prediction of possible miRNA binding sites by ‘walking’ the genes of 3 genomes. J Biomed Inform. 2011;44:839–47.

    CAS  PubMed  Article  Google Scholar 

  152. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008;36:D154–8.

    CAS  PubMed  Article  Google Scholar 

  153. Wu X, Watson M. CORNA: testing gene lists for regulation by microRNAs. Bioinformatics. 2009;25:832–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  154. Lee H, Yang Y, Chae H, Nam S, Choi D, Tangchaisin P, et al. BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2. IEEE Trans Nanobiosci. 2012;11:266–72.

    Article  Google Scholar 

  155. Nam S, Li M, Choi K, Balch C, Kim S, Nephew KP. MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression. Nucleic Acids Res. 2009;37:W356–62.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  156. Sales G, Coppe A, Bisognin A, Bortoluzzi S, Romualdi C. MAGIA, a web-based tool for miRNA and Genes Integrated Analysis. Nucleic Acids Res. 2010;38:W352–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  157. Icay K, Chen P, Cervera C, Lehtonen R, Hautaniemi S. SePIA: RNA and smallRNA-sequence processing, integration, and analysis. 2015. http://anduril.org/sepia. Accessed 6 Jan 2016.

  158. de Sousa AR, Penalva LO, Marcotte EM, Vogel C. Global signatures of protein and mRNA expression levels. Mol Biosyst. 2009;5:1512–26.

    Google Scholar 

  159. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32.

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Low TY, van Heesch S, van den Toorn H, Giansanti P, Cristobal A, Toonen P. Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis. Cell Rep. 2013;5:1469–78.

    CAS  PubMed  Article  Google Scholar 

  161. Suhre K, Schmitt-Kopplin P. MassTRIX: mass translator into pathways. Nucleic Acids Res. 2008;36(Web Server issue):W481–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  162. García-Alcalde F, García-López F, Dopazo J, Conesa A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics. 2011;27:137–9.

    PubMed  Article  CAS  Google Scholar 

  163. Rohn H, Junker A, Hartmann A, Grafahrend-Belau E, Treutler H, Klapperstück M, et al. VANTED v2: a framework for systems biology applications. BMC Syst Biol. 2012;6:139.

    PubMed  PubMed Central  Article  Google Scholar 

  164. Tuncbag N, McCallum S, Huang SS, Fraenkel E. SteinerNet: a web server for integrating ‘omic’ data to discover hidden components of response pathways. Nucleic Acids Res. 2012;40:W505–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  165. Zhang S, Li Q, Liu J, Zhou XJ. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011;27:i401–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  166. Le H-S, Bar-Joseph Z. Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation. Bioinformatics. 2013;29:i89–97.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. Sass S, Buettner F, Mueller NS, Theis FJ. A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Res. 2013;41:9622–33.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  168. Kuo TC, Tian TF, Tseng YJ. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol. 2013;7:64.

    PubMed  PubMed Central  Article  Google Scholar 

  169. Ovaska K, Laakso M, Haapa-Paananen S, Louhimo R, Chen P, Aittomäki V, et al. Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Med. 2010;2:65.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  170. Goecks J, Nekrutenko A, Taylor J. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11:R86.

    PubMed  PubMed Central  Article  Google Scholar 

  171. Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Gentile M, Scheinin I, et al. Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics. 2011;12:507.

    PubMed  PubMed Central  Article  Google Scholar 

  172. Pang CNI, Tay AP, Aya C. Tools to covisualize and coanalyze proteomic data with genomes and transcriptomes: validation of genes and alternative mRNA splicing. J Proteome Res. 2014;13:84–98.

    CAS  PubMed  Article  Google Scholar 

  173. Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  174. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10:1096–8.

    CAS  PubMed  Article  Google Scholar 

  175. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  176. Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014;24:496–510.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  177. Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2014;11:163–6.

    CAS  PubMed  Article  Google Scholar 

  178. Kivioja T, Vähärautio A, Karlsson K, Bonke M, Enge M, Linnarsson S, et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods. 2011;9:72–4.

    PubMed  Article  CAS  Google Scholar 

  179. Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods. 2013;10:1093–5.

    CAS  PubMed  Article  Google Scholar 

  180. Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet. 2015;16:133–45.

    CAS  PubMed  Article  Google Scholar 

  181. Trapnell C, Cacchiarelli D. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  182. Lorthongpanich C, Cheow LF, Balu S, Quake SR, Knowles BB, Burkholder WF, et al. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science. 2013;341:1110–2.

    CAS  PubMed  Article  Google Scholar 

  183. Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523:486–90.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  184. Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015;348:910–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  185. Tilgner H, Jahanbani F, Blauwkamp T, Moshrefi A, Jaeger E, Chen F, et al. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat Biotechnol. 2015;33:736–42.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  186. Au KF, Sebastiano V, Afshar PT, Durruthy JD, Lee L, Williams BA, et al. Characterization of the human ESC transcriptome by hybrid sequencing. Proc Natl Acad Sci U S A. 2013;110:E4821–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  187. Tilgner H, Grubert F, Sharon D, Snyder MP. Defining a personal, allele-specific, and single-molecule long-read transcriptome. Proc Natl Acad Sci U S A. 2014;111:9869–74.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  188. Au KF, Underwood JG, Lee L, Wong WH. Improving PacBio long read accuracy by short read alignment. PLoS One. 2012;7:e46679.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  189. Hansen KD, Wu Z, Irizarry RA, Leek JT. Sequencing technology does not eliminate biological variability. Nat Biotechnol. 2011;29:572–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  190. Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher JP. Calculating sample size estimates for RNA sequencing data. J Comput Biol. 2013;20:970–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  191. Busby MA, Stewart C, Miller CA, Grzeda KR, Marth GT. Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics. 2013;29:656–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  192. Oshlack A, Wakefield MJ. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct. 2009;4:14.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  193. Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27:1135–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  194. Robinson DG, Storey JD. subSeq: determining appropriate sequencing depth through efficient read subsampling. Bioinformatics. 2014;30:3424–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  195. Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2013;30:301–4.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  196. SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol. 2014;32:903–14.

    Article  CAS  Google Scholar 

  197. Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R, Salit M, et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 2011;21:1543–51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  198. Kouzine F, Wojtowicz D, Yamane A, Resch W, Kieffer-Kwon KR, Bandle R, et al. Global regulation of promoter melting in naive lymphocytes. Cell. 2013;153:988–99.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  199. Van Dijk EL, Jaszczyszyn Y, Thermes C. Library preparation methods for next-generation sequencing: tone down the bias. Exp Cell Res. 2014;322:12–20.

    PubMed  Article  CAS  Google Scholar 

  200. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  201. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  202. Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  203. Jean G, Kahles A, Sreedharan VT, De Bona F, Rätsch G. RNA-Seq read alignments with PALMapper. Curr Protoc Bioinformatics. 2010;11(6).

  204. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38:e178.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  205. Marco-Sola S, Sammeth M, Guigó R, Ribeca P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat Methods. 2012;9:1185–8.

    CAS  PubMed  Article  Google Scholar 

  206. Zhao S, Zhang B. A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification. BMC Genomics. 2015;16:97.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  207. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  208. Kvam VM, Liu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Bot. 2012;99:248–56.

    PubMed  Article  Google Scholar 

  209. Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics. 2012;13:484.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  210. Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlén M, et al. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res. 2012;40:10084–97.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  211. Seyednasrollah F, Rantanen K, Jaakkola P, Elo LL. ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer. Nucleic Acids Res. 2016;44(1):e1. doi:10.1093/nar/gkv806.

    PubMed  Article  CAS  Google Scholar 

  212. Bi Y, Davuluri RV. NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:262.

    PubMed  PubMed Central  Article  Google Scholar 

  213. Nueda MJ, Tarazona S, Conesa A. Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics. 2014;30:2598–602.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  214. GTEx Consortium. The Genotype-Tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Article  CAS  Google Scholar 


Page 2

  Replicates per group
  3 5 10
Effect size (fold change)
 1.25 17 % 25 % 44 %
 1.5 43 % 64 % 91 %
 2 87 % 98 % 100 %
Sequencing depth (millions of reads)
 3 19 % 29 % 52 %
 10 33 % 51 % 80 %
 15 38 % 57 % 85 %

  1. Example of calculations for the probability of detecting differential expression in a single test at a significance level of 5 %, for a two-group comparison using a Negative Binomial model, as computed by the RNASeqPower package of Hart et al. [190]. For a fixed within-group variance (package default value), the statistical power increases with the difference between the two groups (effect size), the sequencing depth, and the number of replicates per group. This table shows the statistical power for a gene with 70 aligned reads, which was the median coverage for a protein-coding gene for one whole-blood RNA-seq sample with 30 million aligned reads from the GTEx Project [214]