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Strategy for RNA-Seq Experimental Design and Data Analysis

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Oral Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2588))

Abstract

Ribonucleic acids (RNAs) are fundamental molecules that control regulation and expression of the genome and therefore the function of a cell. Robust analysis and quantification of RNA transcripts hold critical importance in understanding cell function, altered phenotypes in different biological context, for understanding and targeting diseases. The development of RNA-sequencing (RNA-Seq) now provides opportunities to analyze the expression and function of RNA molecules at an unprecedented scale. However, the strategy for RNA-Seq experimental design and data analysis can substantially differ depending on the biological application. The design choice could also have significant impact for downstream results and interpretation of data. Here we describe key critical considerations required for RNA-Seq experimental design and also describe a step-by-step bioinformatics workflow detailing the different steps required for RNA-Seq data analysis. We believe this article will be a valuable guide for designing and analyzing RNA-Seq data to address a wide range of different biological questions.

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References

  1. Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12(2):87–98. https://doi.org/10.1038/nrg2934

    Article  CAS  Google Scholar 

  2. Crick F (1970) Central dogma of molecular biology. Nature 227(5258):561–563

    Article  CAS  Google Scholar 

  3. Crick FH (1958) On protein synthesis. Symp Soc Exp Biol 12:138–163

    CAS  Google Scholar 

  4. Statello L, Guo CJ, Chen LL, Huarte M (2021) Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol 22(2):96–118. https://doi.org/10.1038/s41580-020-00315-9

    Article  CAS  Google Scholar 

  5. Gebert LFR, MacRae IJ (2019) Regulation of microRNA function in animals. Nat Rev Mol Cell Biol 20(1):21–37. https://doi.org/10.1038/s41580-018-0045-7

    Article  CAS  Google Scholar 

  6. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470

    Article  CAS  Google Scholar 

  7. Murphy D (2002) Gene expression studies using microarrays: principles, problems, and prospects. Adv Physiol Educ 26(1-4):256–270

    Article  Google Scholar 

  8. Abdullah-Sayani A, Bueno-de-Mesquita JM, van de Vijver MJ (2006) Technology Insight: tuning into the genetic orchestra using microarrays–limitations of DNA microarrays in clinical practice. Nat Clin Pract Oncol 3(9):501–516. https://doi.org/10.1038/ncponc0587

    Article  CAS  Google Scholar 

  9. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. https://doi.org/10.1038/nrg2484

    Article  CAS  Google Scholar 

  10. Wilhelm BT, Landry JR (2009) RNA-Seq-quantitative measurement of expression through massively parallel RNA-sequencing. Methods 48(3):249–257. https://doi.org/10.1016/j.ymeth.2009.03.016

    Article  CAS  Google Scholar 

  11. Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X (2014) Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One 9(1):e78644. https://doi.org/10.1371/journal.pone.0078644

    Article  CAS  Google Scholar 

  12. Leichter AL, Purcell RV, Sullivan MJ, Eccles MR, Chatterjee A (2015) Multi-platform microRNA profiling of hepatoblastoma patients using formalin fixed paraffin embedded archival samples. GigaScience 4:54. https://doi.org/10.1186/s13742-015-0099-9

    Article  CAS  Google Scholar 

  13. Chatterjee A, Leichter AL, Fan V, Tsai P, Purcell RV, Sullivan MJ, Eccles MR (2015) A cross comparison of technologies for the detection of microRNAs in clinical FFPE samples of hepatoblastoma patients. Sci Rep 5:10438. https://doi.org/10.1038/srep10438

    Article  CAS  Google Scholar 

  14. Petrova OE, Garcia-Alcalde F, Zampaloni C, Sauer K (2017) Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Sci Rep 7:41114. https://doi.org/10.1038/srep41114

    Article  CAS  Google Scholar 

  15. Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Labaj PP, Kreil DP, Megherbi D, Gaj S, Caiment F, van Delft J, Kleinjans J, Scherer A, Devanarayan V, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hsieh JH, Svoboda DL, Fuscoe JC, Deng Y, Shi L, Paules RS, Auerbach SS, Tong W (2014) The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol 32(9):926–932. https://doi.org/10.1038/nbt.3001

    Article  CAS  Google Scholar 

  16. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–382. https://doi.org/10.1038/nmeth.1315

    Article  CAS  Google Scholar 

  17. Chatterjee A, Ahn A, Rodger EJ, Stockwell PA, Eccles MR (2018) A guide for designing and analyzing RNA-Seq data. Methods Mol Biol 1783:35–80. https://doi.org/10.1007/978-1-4939-7834-2_3

    Article  Google Scholar 

  18. Chen C-H, Pan C-Y, Lin W-c (2019) Overlapping protein-coding genes in human genome and their coincidental expression in tissues. Sci Rep 9(1):13377. https://doi.org/10.1038/s41598-019-49802-w

    Article  CAS  Google Scholar 

  19. Zhao S, Zhang Y, Gordon W, Quan J, Xi H, Du S, von Schack D, Zhang B (2015) Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap. BMC Genomics 16(1):675. https://doi.org/10.1186/s12864-015-1876-7

    Article  CAS  Google Scholar 

  20. Krzywinski M, Altman N (2013) Power and sample size. Nat Methods 10(12):1139–1140. https://doi.org/10.1038/nmeth.2738

    Article  CAS  Google Scholar 

  21. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, Munafò MR (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14(5):365–376. https://doi.org/10.1038/nrn3475

    Article  CAS  Google Scholar 

  22. Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher J-P (2013) Calculating sample size estimates for RNA sequencing data. J Comput Biol 20(12):970–978. https://doi.org/10.1089/cmb.2012.0283

    Article  CAS  Google Scholar 

  23. Busby MA, Stewart C, Miller CA, Grzeda KR, Marth GT (2013) Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics 29(5):656–657. https://doi.org/10.1093/bioinformatics/btt015

    Article  CAS  Google Scholar 

  24. 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):2213–2223. https://doi.org/10.1101/gr.124321.111

    Article  CAS  Google Scholar 

  25. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628. https://doi.org/10.1038/nmeth.1226

    Article  CAS  Google Scholar 

  26. Chhangawala S, Rudy G, Mason CE, Rosenfeld JA (2015) The impact of read length on quantification of differentially expressed genes and splice junction detection. Genome Biol 16(1):131. https://doi.org/10.1186/s13059-015-0697-y

    Article  CAS  Google Scholar 

  27. Uszczynska-Ratajczak B, Lagarde J, Frankish A, Guigó R, Johnson R (2018) Towards a complete map of the human long non-coding RNA transcriptome. Nat Rev Genet 19(9):535–548. https://doi.org/10.1038/s41576-018-0017-y

    Article  CAS  Google Scholar 

  28. Weirather JL, de Cesare M, Wang Y, Piazza P, Sebastiano V, Wang X-J, Buck D, Au KF (2017) Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Research 6:100. https://doi.org/10.12688/f1000research.10571.2

    Article  Google Scholar 

  29. O’Neil D, Glowatz H, Schlumpberger M (2013) Ribosomal RNA depletion for efficient use of RNA-Seq capacity. Curr Protoc Mol Biol 103(1):4.19.11–4.19.18. https://doi.org/10.1002/0471142727.mb0419s103

    Article  Google Scholar 

  30. Zaghlool A, Ameur A, Nyberg L, Halvardson J, Grabherr M, Cavelier L, Feuk L (2013) Efficient cellular fractionation improves RNA sequencing analysis of mature and nascent transcripts from human tissues. BMC Biotechnol 13(1):99. https://doi.org/10.1186/1472-6750-13-99

    Article  CAS  Google Scholar 

  31. Kim SH, Das A, Chai JC, Binas B, Choi MR, Park KS, Lee YS, Jung KH, Chai YG (2016) Transcriptome sequencing wide functional analysis of human mesenchymal stem cells in response to TLR4 ligand. Sci Rep 6:30311–30311. https://doi.org/10.1038/srep30311

    Article  CAS  Google Scholar 

  32. Ewels P, Magnusson M, Lundin S, Käller M (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32(19):3047–3048. https://doi.org/10.1093/bioinformatics/btw354

    Article  CAS  Google Scholar 

  33. Liao Y, Shi W (2020) Read trimming is not required for mapping and quantification of RNA-seq reads at the gene level. NAR Genomics Bioinformatics 2(3):lqaa068. https://doi.org/10.1093/nargab/lqaa068

    Article  CAS  Google Scholar 

  34. Meyers BC, Scalabrin S, Morgante M (2004) Mapping and sequencing complex genomes: let’s get physical! Nat Rev Genet 5(8):578–588. https://doi.org/10.1038/nrg1404

    Article  CAS  Google Scholar 

  35. Hatem A, Bozdağ D, Toland AE, Çatalyürek ÜV (2013) Benchmarking short sequence mapping tools. BMC Bioinformatics 14(1):184. https://doi.org/10.1186/1471-2105-14-184

    Article  Google Scholar 

  36. 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. https://doi.org/10.1093/bioinformatics/bts635

    Article  CAS  Google Scholar 

  37. Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12(4):357–360. https://doi.org/10.1038/nmeth.3317

    Article  CAS  Google Scholar 

  38. Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34(5):525–527. https://doi.org/10.1038/nbt.3519

    Article  CAS  Google Scholar 

  39. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14(4):417–419. https://doi.org/10.1038/nmeth.4197

    Article  CAS  Google Scholar 

  40. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323–323. https://doi.org/10.1186/1471-2105-12-323

    Article  CAS  Google Scholar 

  41. 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):281–285. https://doi.org/10.1007/s12064-012-0162-3

    Article  CAS  Google Scholar 

  42. Pachter L (2011) Models for transcript quantification from RNA-Seq. https://doi.org/10.48550/arXiv.1104.3889

  43. Zhao S, Ye Z, Stanton R (2020) Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. RNA 26(8):903–909. https://doi.org/10.1261/rna.074922.120

    Article  CAS  Google Scholar 

  44. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17(1):13. https://doi.org/10.1186/s13059-016-0881-8

    Article  CAS  Google Scholar 

  45. Costa-Silva J, Domingues D, Lopes FM (2017) RNA-Seq differential expression analysis: an extended review and a software tool. PLoS One 12(12):e0190152. https://doi.org/10.1371/journal.pone.0190152

    Article  CAS  Google Scholar 

  46. Quinn TP, Crowley TM, Richardson MF (2018) Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods. BMC Bioinformatics 19(1):274. https://doi.org/10.1186/s12859-018-2261-8

    Article  CAS  Google Scholar 

  47. Everaert C, Luypaert M, Maag JLV, Cheng QX, Dinger ME, Hellemans J, Mestdagh P (2017) Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. Sci Rep 7(1):1559. https://doi.org/10.1038/s41598-017-01617-3

    Article  CAS  Google Scholar 

  48. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079. https://doi.org/10.1093/bioinformatics/btp352

    Article  CAS  Google Scholar 

  49. Anders S, Pyl PT, Huber W (2015) HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. https://doi.org/10.1093/bioinformatics/btu638

    Article  CAS  Google Scholar 

  50. Liao Y, Smyth GK, Shi W (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 41(10):e108–e108. https://doi.org/10.1093/nar/gkt214

    Article  CAS  Google Scholar 

  51. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O’Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD (2016) Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44(D1):D733–D745. https://doi.org/10.1093/nar/gkv1189

    Article  CAS  Google Scholar 

  52. Aken BL, Ayling S, Barrell D, Clarke L, Curwen V, Fairley S, Fernandez Banet J, Billis K, García Girón C, Hourlier T, Howe K, Kähäri A, Kokocinski F, Martin FJ, Murphy DN, Nag R, Ruffier M, Schuster M, Tang YA, Vogel J-H, White S, Zadissa A, Flicek P, Searle SMJ (2016) The ensembl gene annotation system. Database (Oxford) 2016:baw093. https://doi.org/10.1093/database/baw093

    Article  CAS  Google Scholar 

  53. Frankish A, Diekhans M, Ferreira A-M, Johnson R, Jungreis I, Loveland J, Mudge JM, Sisu C, Wright J, Armstrong J, Barnes I, Berry A, Bignell A, Carbonell Sala S, Chrast J, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, García Girón C, Gonzalez JM, Grego T, Hardy M, Hourlier T, Hunt T, Izuogu OG, Lagarde J, Martin FJ, Martínez L, Mohanan S, Muir P, Navarro FCP, Parker A, Pei B, Pozo F, Ruffier M, Schmitt BM, Stapleton E, Suner M-M, Sycheva I, Uszczynska-Ratajczak B, Xu J, Yates A, Zerbino D, Zhang Y, Aken B, Choudhary JS, Gerstein M, Guigó R, Hubbard TJP, Kellis M, Paten B, Reymond A, Tress ML, Flicek P (2019) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47(D1):D766–D773. https://doi.org/10.1093/nar/gky955

    Article  CAS  Google Scholar 

  54. Hamaguchi Y, Zeng C, Hamada M (2021) Impact of human gene annotations on RNA-seq differential expression analysis. BMC Genomics 22(1):730. https://doi.org/10.1186/s12864-021-08038-7

    Article  CAS  Google Scholar 

  55. Wu PY, Phan JH, Wang MD (2012) The effect of human genome annotation complexity on RNA-Seq gene expression quantification. In: 2012 IEEE international conference on bioinformatics and biomedicine workshops, 4–7 October 2012, pp 712–717. https://doi.org/10.1109/BIBMW.2012.6470224

    Chapter  Google Scholar 

  56. Wang L, Wang S, Li W (2012) RSeQC: quality control of RNA-seq experiments. Bioinformatics 28(16):2184–2185. https://doi.org/10.1093/bioinformatics/bts356

    Article  CAS  Google Scholar 

  57. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  Google Scholar 

  58. Soneson C, Love MI, Robinson MD (2105) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4:1521

    Article  Google Scholar 

  59. Patro R, Mount SM, Kingsford C (2014) Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol 32(5):462–464. https://doi.org/10.1038/nbt.2862

    Article  CAS  Google Scholar 

  60. Pertea M, Pertea GM, Antonescu CM, Chang T-C, Mendell JT, Salzberg SL (2015) StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33(3):290–295. https://doi.org/10.1038/nbt.3122

    Article  CAS  Google Scholar 

  61. Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Čech M, Chilton J, Clements D, Coraor N, Grüning BA, Guerler A, Hillman-Jackson J, Hiltemann S, Jalili V, Rasche H, Soranzo N, Goecks J, Taylor J, Nekrutenko A, Blankenberg D (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46(W1):W537–W544. https://doi.org/10.1093/nar/gky379

    Article  CAS  Google Scholar 

  62. Köster J, Rahmann S (2012) Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28(19):2520–2522. https://doi.org/10.1093/bioinformatics/bts480

    Article  CAS  Google Scholar 

  63. Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S (2020) The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38(3):276–278. https://doi.org/10.1038/s41587-020-0439-x

    Article  CAS  Google Scholar 

  64. Sharon D, Tilgner H, Grubert F, Snyder M (2013) A single-molecule long-read survey of the human transcriptome. Nat Biotechnol 31(11):1009–1014. https://doi.org/10.1038/nbt.2705

    Article  CAS  Google Scholar 

  65. Byrne A, Beaudin AE, Olsen HE, Jain M, Cole C, Palmer T, DuBois RM, Forsberg EC, Akeson M, Vollmers C (2017) Nanopore long-read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat Commun 8(1):16027. https://doi.org/10.1038/ncomms16027

    Article  CAS  Google Scholar 

  66. Soneson C, Yao Y, Bratus-Neuenschwander A, Patrignani A, Robinson MD, Hussain S (2019) A comprehensive examination of Nanopore native RNA sequencing for characterization of complex transcriptomes. Nat Commun 10(1):3359. https://doi.org/10.1038/s41467-019-11272-z

    Article  CAS  Google Scholar 

  67. Hansen KD, Wu Z, Irizarry RA, Leek JT (2011) Sequencing technology does not eliminate biological variability. Nat Biotechnol 29(7):572–573. https://doi.org/10.1038/nbt.1910

    Article  CAS  Google Scholar 

  68. 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):1509–1517. https://doi.org/10.1101/gr.079558.108

    Article  CAS  Google Scholar 

  69. Takele Assefa A, Vandesompele J, Thas O (2020) On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments. BMC Genomics 21(1):312. https://doi.org/10.1186/s12864-020-6721-y

    Article  CAS  Google Scholar 

  70. Klaus B (2015) Statistical relevance—relevant statistics, part I. EMBO J 34(22):2727–2730. https://doi.org/10.15252/embj.201592958

    Article  CAS  Google Scholar 

  71. Goh WWB, Wang W, Wong L (2017) Why batch effects matter in omics data, and how to avoid them. Trends Biotechnol 35(6):498–507. https://doi.org/10.1016/j.tibtech.2017.02.012

    Article  CAS  Google Scholar 

  72. Zhang Y, Parmigiani G, Johnson WE (2020) ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics Bioinformatics 2(3):lqaa078. https://doi.org/10.1093/nargab/lqaa078

    Article  CAS  Google Scholar 

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Acknowledgments

We would like to thank the Rutherford Discovery Fellowship Program (Royal Society of New Zealand) for supporting AC’s current position and the Dunedin School of Medicine for supporting our work.

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Authors and Affiliations

  1. Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand

    Gregory Gimenez, Peter A. Stockwell, Euan J. Rodger & Aniruddha Chatterjee

  2. UPES University, School of Health Sciences, Dehradun, India

    Aniruddha Chatterjee

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  1. Gregory Gimenez
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  2. Peter A. Stockwell
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  3. Euan J. Rodger
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  4. Aniruddha Chatterjee
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Corresponding authors

Correspondence to Gregory Gimenez or Aniruddha Chatterjee .

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Editors and Affiliations

  1. School of Dentistry, The University of Queensland, Herston, QLD, Australia

    Gregory J. Seymour

  2. School of Dentistry, The University of Queensland, Herston, QLD, Australia

    Mary P. Cullinan

  3. Faculty of Dentistry, University of Otago, Dunedin, New Zealand

    Nicholas C.K. Heng

  4. Faculty of Dentistry, University of Otago, Dunedin, New Zealand

    Paul R. Cooper

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Gimenez, G., Stockwell, P.A., Rodger, E.J., Chatterjee, A. (2023). Strategy for RNA-Seq Experimental Design and Data Analysis. In: Seymour, G.J., Cullinan, M.P., Heng, N.C., Cooper, P.R. (eds) Oral Biology. Methods in Molecular Biology, vol 2588. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2780-8_16

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  • DOI: https://doi.org/10.1007/978-1-0716-2780-8_16

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