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BioMed Research International
Volume 2016 (2016), Article ID 7972351, 11 pages
http://dx.doi.org/10.1155/2016/7972351
Research Article

Advancements in RNASeqGUI towards a Reproducible Analysis of RNA-Seq Experiments

Istituto per le Applicazioni del Calcolo, CNR, 80131 Napoli, Italy

Received 2 July 2015; Revised 11 December 2015; Accepted 3 January 2016

Academic Editor: Sílvia A. Sousa

Copyright © 2016 Francesco Russo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Z. Wang, M. Gerstein, and M. Snyder, “RNA-Seq: a revolutionary tool for transcriptomics,” Nature Reviews Genetics, vol. 10, no. 1, pp. 57–63, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. V. Costa, C. Angelini, I. De Feis, and A. Ciccodicola, “Uncovering the complexity of transcriptomes with RNA-Seq,” Journal of Biomedicine and Biotechnology, vol. 2010, Article ID 853916, 19 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Ozsolak and P. M. Milos, “RNA sequencing: advances, challenges and opportunities,” Nature Reviews Genetics, vol. 12, no. 2, pp. 87–98, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. E. L. van Dijk, H. Auger, Y. Jaszczyszyn, and C. Thermes, “Ten years of next-generation sequencing technology,” Trends in Genetics, vol. 30, no. 9, pp. 418–426, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. V. Costa, M. Aprile, R. Esposito, and A. Ciccodicola, “RNA-Seq and human complex diseases: recent accomplishments and future perspectives,” European Journal of Human Genetics, vol. 21, no. 2, pp. 134–142, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Pepke, B. Wold, and A. Mortazavi, “Computation for ChIP-seq and RNA-seq studies,” Nature Methods, vol. 6, no. 11, pp. S22–S32, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Oshlack, M. D. Robinson, and M. D. Young, “From RNA-seq reads to differential expression results,” Genome Biology, vol. 11, no. 12, article 220, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Kim, G. Pertea, C. Trapnell, H. Pimentel, R. Kelley, and S. L. Salzberg, “TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions,” Genome Biology, vol. 14, no. 4, article R36, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Trapnell, D. G. Hendrickson, M. Sauvageau, L. Goff, J. L. Rinn, and L. Pachter, “Differential analysis of gene regulation at transcript resolution with RNA-seq,” Nature Biotechnology, vol. 31, no. 1, pp. 46–53, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. F. Finotello and B. Di Camillo, “Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis,” Briefings in Functional Genomics, vol. 14, no. 2, pp. 130–142, 2015. View at Publisher · View at Google Scholar
  11. C. Trapnell, A. Roberts, L. Goff et al., “Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks,” Nature Protocols, vol. 7, no. 3, pp. 562–578, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Goecks, A. Nekrutenko, J. Taylor et al., “Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences,” Genome Biology, vol. 11, no. 8, article R86, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Sanges, F. Cordero, and R. A. Calogero, “oneChannelGUI: a graphical interface to Bioconductor tools, designed for life scientists who are not familiar with R language,” Bioinformatics, vol. 23, no. 24, pp. 3406–3408, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Lohse, A. M. Bolger, A. Nagel et al., “RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics,” Nucleic Acids Research, vol. 40, no. 1, pp. W622–W627, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. F. Russo and C. Angelini, “RNASeqGUI: a GUI for analysing RNA-Seq data,” Bioinformatics, vol. 30, no. 17, pp. 2514–2516, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. M. D'Antonio, P. D'Onorio De Meo, M. Pallocca et al., “RAP: RNA-Seq analysis pipeline, a new cloud-based NGS web application,” BMC Genomics, vol. 16, supplement 6, article S3, 2015. View at Publisher · View at Google Scholar
  17. A. Poplawski, F. Marini, M. Hess, T. Zeller, J. Mazur, and H. Binder, “Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective,” Briefings in Bioinformatics, 2015. View at Publisher · View at Google Scholar
  18. R. Gentleman, “Reproducible research: a bioinformatics case study,” Statistical Applications in Genetics and Molecular Biology, vol. 4, no. 1, article 2, 25 pages, 2005. View at Publisher · View at Google Scholar · View at MathSciNet
  19. R. D. Peng, “Reproducible research in computational science,” Science, vol. 334, no. 6060, pp. 1226–1227, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. D. C. Ince, L. Hatton, and J. Graham-Cumming, “The case for open computer programs,” Nature, vol. 482, no. 7386, pp. 485–488, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. “Enhancing reproducibility,” Nature Methods, vol. 10, no. 5, article 367, 2013. View at Publisher · View at Google Scholar
  22. R. D. Peng, “Reproducible research and Biostatistics,” Biostatistics, vol. 10, no. 3, pp. 405–408, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Nekrutenko and J. Taylor, “Next-generation sequencing data interpretation: enhancing reproducibility and accessibility,” Nature Reviews Genetics, vol. 13, no. 9, pp. 667–672, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. V. Stodden, F. Leisch, and R. D. Peng, Eds., Implementing Reproducible Research, CRC Press, 2014.
  25. C. Fresno and E. A. Fernández, “RDAVIDWebService: a versatile R interface to DAVID,” Bioinformatics, vol. 29, no. 21, pp. 2810–2811, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. A. L. Tarca, S. Draghici, P. Khatri et al., “A novel signaling pathway impact analysis,” Bioinformatics, vol. 25, no. 1, pp. 75–82, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. W. Luo, M. S. Friedman, K. Shedden, K. D. Hankenson, and P. J. Woolf, “GAGE: generally applicable gene set enrichment for pathway analysis,” BMC Bioinformatics, vol. 10, no. 1, article 161, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. R. D. Peng, “Caching and distributing statistical analyses in R,” Journal of Statistical Software, vol. 26, no. 7, pp. 1–24, 2008. View at Google Scholar · View at Scopus
  29. M. Lawrence and T. L. Duncan, “RGtk2: a graphical user interface toolkit for R,” Journal of Statistical Software, vol. 37, no. 8, pp. 1–52, 2010. View at Google Scholar
  30. Y. Liao, G. K. Smyth, and W. Shi, “The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote,” Nucleic Acids Research, vol. 41, no. 10, article e108, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Lawrence, W. Huber, H. Pags, P. Aboyoun, and M. Carlson, “Software for computing and annotating genomic ranges,” PLoS Computational Biology, vol. 9, no. 8, Article ID e1003118, 2013. View at Publisher · View at Google Scholar
  32. M. A. Huntley, J. L. Larson, C. Chaivorapol et al., “ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses,” Bioinformatics, vol. 29, no. 24, pp. 3220–3221, 2013. View at Publisher · View at Google Scholar · View at Scopus
  33. Z. Liu and S. Pounds, “An R package that automatically collects and archives details for reproducible computing,” BMC Bioinformatics, vol. 15, article 138, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Falcon, weaver: Tools and extensions for processing Sweave documents. R package version, 1(0), 2007.
  35. Y. Xie, Dynamic Documents with R and Knitr, CRC Press, New York, NY, USA, 2nd edition, 2015.
  36. R. Peng, “Interacting with data using the filehash package for R,” Working Paper 108, Department of Biostatistics Working Papers, Johns Hopkins University, Baltimore, Md, USA, 2006. View at Google Scholar
  37. Revolution Analytics and S. Weston, “DoParallel: foreach parallel adaptor for the parallel package,” R Package Version, vol. 1, no. 8, 2014. View at Google Scholar
  38. S. Weston, “Using The foreach Package,” 2014.
  39. L. Tierney, A. J. Rossini, and N. Li, “Snow: a parallel computing framework for the R system,” International Journal of Parallel Programming, vol. 37, no. 1, pp. 78–90, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. M. Morgan, V. Carey, and M. Lawrence, “BiocParallel: Bioconductor Facilities for Parallel Evaluation,” R Package Version 0.4.1, 2014.
  41. R. C. Gentleman, V. J. Carey, D. M. Bates et al., “Bioconductor: open software development for computational biology and bioinformatics,” Genome Biology, vol. 5, no. 10, article R80, 2004. View at Publisher · View at Google Scholar · View at Scopus
  42. A. N. Brooks, L. Yang, M. O. Duff et al., “Conservation of an RNA regulatory map between Drosophila and mammals,” Genome Research, vol. 21, no. 2, pp. 193–202, 2011. View at Publisher · View at Google Scholar · View at Scopus