How to Design a good RNA-Seq experiment in an interdisciplinary context? Pôle Planification Expérimentale, PEPI IBIS 1 RNA-seq technology is a powerful tool for characterizing and quantifying transcriptome. Upstream careful experimental planning is necessary to pull the maximum of relevant information and to make the best use of these experiments. 1 , INRA, France Be aware of different types of bias Why increasing the number of biological replicates? • To generalize to the population level • To estimate to a higher degree of accuracy variation in individual transcript (Hart, 2013) • To improve detection of DE transcripts and control of false positive rate: TRUE with at least 3 (Sonenson 2013, Robles 2012) An RNA-seq experimental design using Fisher’s principles More biological replicates or increasing sequencing depth? Rule 1: Share a minimal common language Keep in mind the influence of effects on results: lane ≤ run ≤ RNA library preparation ≤ biological (Marioni, 2008), (Bullard, 2010) RNA-seq experiment analysis: from A to Z It depends! (Haas, 2012), (Liu, 2014) • DE transcript detection: (+) biological replicates • Construction and annotation of transcriptome: (+) depth and (+) sampling conditions • Transcriptomic variants search: (+) biological replicates and (+) depth A solution: multiplexing. Decision tools available: Scotty (Busby, 2013), RNAseqPower (Hart, 2013) Some definitions Biological and technical replicates: Rule 2: Well define the biological question Sequencing depth: Average number of a given position in a genome or a transcriptome covered by reads in a sequencing run Multiplexing: Tag or bar coded with specific sequences added during library construction and that allow multiple samples to be included in the same sequencing reaction (lane) Blocking: Isolating variation attributable to a nuisance variable (e.g. lane) From Alon, 2009 • Choose scientific problems on feasibility and interest • Order your objectives (primary and secondary) • Ask yourself if RNA-seq is better than microarray regarding the biological question Adapted from Mutz, 2013 Conclusions Make a choice • Identify differentially expressed (DE) genes? Rule 4: Make good choices • Detect and estimate isoforms? • All skills are needed to discussions right from project • Construct a de Novo transcriptome? How many reads? Rule 3: Anticipate difficulties with a well designed experiment Prepare a checklist with all the needed elements to be collected, 2 Collect data and determine all factors of variation, 3 Choose bioinformatics and statistical models, 4 Draw conclusions on results. 1 • Clarify the biological question • 100M to detect 90% of the transcripts of 81% of human genes (Toung, 2011) • 20M reads of 75bp can detect transcripts of medium and low abundance in chicken (Wand, 2011) • 10M to cover by at least 10 reads 90% of all (human and zebrafish) genes (Hart, 2013)... construction • Prefer biological replicates instead of technical replicates • Use multiplexing • Optimum compromise between replication number and sequencing depth depends on the question • Wherever possible apply the three Fisher’s principles of randomization, replication and local control (blocking) And do not forget: budget also includes cost of biological data acquisition, sequencing data backup, bioinformatics and statistical analysis. 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How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? BMC genomics, 13(1):734. Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A., and Kocher, J.-P. (2013). Calculating Sample Size Estimates for RNA Sequencing Data. Journal of computational biology : a journal of computational molecular cell biology. Liu, Y., Zhou, J., and White, K. P. (2014). RNA-seq differential expression studies: more sequence or more replication? Bioinformatics (Oxford, England), 30(3):301–4. Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., and Gilad, Y. (2008). Rna-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research, 18(9):1509–17. Mutz, K.-O., Heilkenbrinker, A., Lanne, M., Walter, J.-G., and Stahl, F. (2013). Transcriptome analysis using next-generation sequencing. Current Opinion in Biotechnology, 24(1). Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., and Taylor, J. M. (2012). 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