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iSeq: Web-Based RNA-seq Data Analysis and Visualization

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Book cover Computational Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1754))

Abstract

Transcriptome sequencing (RNA-seq) is becoming a standard experimental methodology for genome-wide characterization and quantification of transcripts at single base-pair resolution. However, downstream analysis of massive amount of sequencing data can be prohibitively technical for wet-lab researchers. A functionally integrated and user-friendly platform is required to meet this demand. Here, we present iSeq, an R-based Web server, for RNA-seq data analysis and visualization. iSeq is a streamlined Web-based R application under the Shiny framework, featuring a simple user interface and multiple data analysis modules. Users without programming and statistical skills can analyze their RNA-seq data and construct publication-level graphs through a standardized yet customizable analytical pipeline. iSeq is accessible via Web browsers on any operating system at http://iseq.cbi.pku.edu.cn.

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References

  1. Schuster SC (2008) Next-generation sequencing transforms today’s biology. Nat Methods 5(1):16–18. https://doi.org/10.1038/nmeth1156

    Article  CAS  PubMed  Google Scholar 

  2. Yan L, Yang M, Guo H, Yang L, Wu J, Li R, Liu P, Lian Y, Zheng X, Yan J, Huang J, Li M, Wu X, Wen L, Lao K, Li R, Qiao J, Tang F (2013) Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 20(9):1131–1139. https://doi.org/10.1038/nsmb.2660

    Article  CAS  PubMed  Google Scholar 

  3. Edgren H, Murumagi A, Kangaspeska S, Nicorici D, Hongisto V, Kleivi K, Rye IH, Nyberg S, Wolf M, Borresen-Dale AL, Kallioniemi O (2011) Identification of fusion genes in breast cancer by paired-end RNA-sequencing. Genome Biol 12(1):R6. https://doi.org/10.1186/gb-2011-12-1-r6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, Desai TJ, Krasnow MA, Quake SR (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509(7500):371–375. https://doi.org/10.1038/nature13173

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40(12):1413–1415. https://doi.org/10.1038/ng.259

    Article  CAS  PubMed  Google Scholar 

  6. Yang L, Duff MO, Graveley BR, Carmichael GG, Chen LL (2011) Genomewide characterization of non-polyadenylated RNAs. Genome Biol 12(2):R16. https://doi.org/10.1186/gb-2011-12-2-r16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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):1344–1349. https://doi.org/10.1126/science.1158441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, Robertson AJ, Perkins AC, Bruce SJ, Lee CC, Ranade SS, Peckham HE, Manning JM, McKernan KJ, Grimmond SM (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5(7):613–619. https://doi.org/10.1038/nmeth.1223

    Article  CAS  PubMed  Google Scholar 

  9. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–1111. https://doi.org/10.1093/bioinformatics/btp120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Li R, Li Y, Kristiansen K, Wang J (2008) SOAP: short oligonucleotide alignment program. Bioinformatics 24(5):713–714. https://doi.org/10.1093/bioinformatics/btn025

    Article  CAS  PubMed  Google Scholar 

  11. Wu TD, Nacu S (2010) Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26(7):873–881. https://doi.org/10.1093/bioinformatics/btq057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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  PubMed  PubMed Central  Google Scholar 

  13. 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  PubMed  Google Scholar 

  14. Zyprych-Walczak J, Szabelska A, Handschuh L, Gorczak K, Klamecka K, Figlerowicz M, Siatkowski I (2015) The impact of normalization methods on RNA-Seq data analysis. Biomed Res Int 2015:621690. https://doi.org/10.1155/2015/621690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ringner M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304

    Article  CAS  Google Scholar 

  16. van der Maaten L (2014) Accelerating t-SNE using Tree-Based Algorithms. J Mach Learn Res 15:3221–3245

    Google Scholar 

  17. Goecks J, Nekrutenko A, Taylor J, Galaxy T (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11(8):R86. https://doi.org/10.1186/gb-2010-11-8-r86

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nelson JW, Sklenar J, Barnes AP, Minnier J (2017) The START App: a web-based RNAseq analysis and visualization resource. Bioinformatics 33(3):447–449. https://doi.org/10.1093/bioinformatics/btw624

    Article  PubMed  Google Scholar 

  19. D’Antonio M, D’Onorio De Meo P, Pallocca M, Picardi E, D’Erchia AM, Calogero RA, Castrignano T, Pesole G (2015) RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application. BMC Genomics 16:S3. https://doi.org/10.1186/1471-2164-16-S6-S3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Velmeshev D, Lally P, Magistri M, Faghihi MA (2016) CANEapp: a user-friendly application for automated next generation transcriptomic data analysis. BMC Genomics 17:49. https://doi.org/10.1186/s12864-015-2346-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106. https://doi.org/10.1186/gb-2010-11-10-r106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 4(5):P3

    Article  Google Scholar 

  23. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  24. Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11(2):R14. https://doi.org/10.1186/gb-2010-11-2-r14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ginestet C (2011) ggplot2: elegant graphics for data analysis. J R Stat Soc a Stat 174:245. https://doi.org/10.1111/j.1467-985X.2010.00676_9.x

    Article  Google Scholar 

  26. Tang Y, Horikoshi M, Li WX (2016) ggfortify: unified interface to visualize statistical results of popular R packages. R J 8(2):474–485

    Google Scholar 

  27. Dillman AA, Hauser DN, Gibbs JR, Nalls MA, McCoy MK, Rudenko IN, Galter D, Cookson MR (2013) mRNA expression, splicing and editing in the embryonic and adult mouse cerebral cortex. Nat Neurosci 16(4):499–506. https://doi.org/10.1038/nn.3332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloe D, Le Gall C, Schaeffer B, Le Crom S, Guedj M, Jaffrezic F, French StatOmique C (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14(6):671–683. https://doi.org/10.1093/bib/bbs046

    Article  CAS  PubMed  Google Scholar 

  29. Seyednasrollah F, Laiho A, Elo LL (2015) Comparison of software packages for detecting differential expression in RNA-seq studies. Brief Bioinform 16(1):59–70. https://doi.org/10.1093/bib/bbt086

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We thank Yifang Liu for advice on Web server construction and the PKU Bioinformatics Core Discussion Group (Yangchen Zheng, Yong Peng) for testing and suggestions. This work was supported by funding from Peking-Tsinghua Center for Life Sciences and School of Life Sciences of Peking University, Natural Science Foundation of China (Key Research Grant 71532001), and Chinese National Key Projects of Research and Development (2016YFA0100103).

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Author notes

  1. Chao Zhang and Caoqi Fan contributed equally to this work.

Authors and Affiliations

  1. PKU-Tsinghua-NIBS Graduate Program, School of Life Sciences, Peking University, Beijing, 100871, China

    Chao Zhang

  2. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, 100871, China

    Caoqi Fan, Jingbo Gan, Ping Zhu, Lei Kong & Cheng Li

  3. Center for Statistical Science, Peking University, Beijing, 100871, China

    Cheng Li

Authors
  1. Chao Zhang
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  2. Caoqi Fan
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  3. Jingbo Gan
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  4. Ping Zhu
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  5. Lei Kong
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  6. Cheng Li
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Corresponding author

Correspondence to Cheng Li .

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Editors and Affiliations

  1. Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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Zhang, C., Fan, C., Gan, J., Zhu, P., Kong, L., Li, C. (2018). iSeq: Web-Based RNA-seq Data Analysis and Visualization. In: Huang, T. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 1754. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7717-8_10

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  • DOI: https://doi.org/10.1007/978-1-4939-7717-8_10

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7716-1

  • Online ISBN: 978-1-4939-7717-8

  • eBook Packages: Springer Protocols

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