Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 248090, 8 pages
http://dx.doi.org/10.1155/2014/248090
Research Article

MultiRankSeq: Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control

Center for Quantitative Sciences, Vanderbilt University, Nashville, TN 37027, USA

Received 23 November 2013; Revised 1 February 2014; Accepted 15 March 2014; Published 27 May 2014

Academic Editor: Jason E. Mcdermott

Copyright © 2014 Yan Guo 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. J. Shendure, “The beginning of the end for microarrays?” Nature Methods, vol. 5, no. 7, pp. 585–587, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. W. Asmann, E. W. Klee, E. A. Thompson et al., “3′ tag digital gene expression profiling of human brain and universal reference RNA using illumina genome analyzer,” BMC Genomics, vol. 10, article 531, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Cloonan, A. R. Forrest, G. Kolle et al., “Stem cell transcriptome profiling via massive-scale mRNA sequencing,” Nature Methods, vol. 5, no. 7, pp. 613–619, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Guo, Q. Sheng, J. Li, F. Ye, D. C. Samuels, and Y. Shyr, “Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data,” PLoS ONE, vol. 8, no. 8, Article ID e71462, 2013. View at Publisher · View at Google Scholar
  5. J. C. Marioni, C. E. Mason, S. M. Mane, M. Stephens, and Y. Gilad, “RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays,” Genome Research, vol. 18, no. 9, pp. 1509–1517, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. 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
  7. A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, and B. Wold, “Mapping and quantifying mammalian transcriptomes by RNA-seq,” Nature Methods, vol. 5, no. 7, pp. 621–628, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Trapnell, B. A. Williams, G. Pertea et al., “Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation,” Nature Biotechnology, vol. 28, no. 5, pp. 511–515, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Li and C. N. Dewey, “RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome,” BMC Bioinformatics, vol. 12, article 323, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Anders and W. Huber, “Differential expression analysis for sequence count data,” Genome Biology, vol. 11, no. 10, article R106, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Wang, Z. Feng, X. Wang, X. Wang, and X. Zhang, “DEGseq: an R package for identifying differentially expressed genes from RNA-seq data,” Bioinformatics, vol. 26, no. 1, pp. 136–138, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. M. D. Robinson, D. J. McCarthy, and G. K. Smyth, “edgeR: a bioconductor package for differential expression analysis of digital gene expression data,” Bioinformatics, vol. 26, no. 1, pp. 139–140, 2010. View at Google Scholar · View at Scopus
  13. T. J. Hardcastle and K. A. Kelly, “BaySeq: empirical Bayesian methods for identifying differential expression in sequence count data,” BMC Bioinformatics, vol. 11, article 422, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. P. L. Auer and R. W. Doerge, “A two-stage poisson model for testing RNA-seq data,” Statistical Applications in Genetics and Molecular Biology, vol. 10, no. 1, article 26, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Di, D. W. Schafer, J. S. Cumbie, and J. H. Chang, “The NBP negative binomial model for assessing differential gene expression from RNA-seq,” Statistical Applications in Genetics and Molecular Biology, vol. 10, no. 1, article 24, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Li and R. Tibshirani, “Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-seq data,” Statistical Methods in Medical Research, vol. 22, no. 5, pp. 519–536, 2011. View at Publisher · View at Google Scholar
  17. S. Tarazona, F. García-Alcalde, J. Dopazo, A. Ferrer, and A. Conesa, “Differential expression in RNA-seq: a matter of depth,” Genome Research, vol. 21, no. 12, pp. 2213–2223, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. M. A. Dillies, A. Rau, J. Aubert et al., “A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis,” Brief Bioinform, vol. 14, no. 6, pp. 671–683, 2012. View at Publisher · View at Google Scholar
  19. V. M. Kvam, P. Liu, and S. Yaqing, “A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data,” The American Journal of Botany, vol. 99, no. 2, pp. 248–256, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. J. A. Robles, S. E. Qureshi, S. J. Stephen, S. R. Wilson, C. J. Burden, and J. M. Taylor, “Efficient experimental design and analysis strategies for the detection of differential expression using RNA-sequencing,” BMC Genomics, vol. 13, article 484, 2012. View at Publisher · View at Google Scholar
  21. C. Soneson and M. Delorenzi, “A comparison of methods for differential expression analysis of RNA-seq data,” BMC Bioinformatics, vol. 14, article 91, 2013. View at Publisher · View at Google Scholar
  22. Y. Guo, F. Ye, Q. Sheng, T. Clark, and D. C. Samuels, “Three-stage quality control strategies for DNA re-sequencing data,” Briefings in Bioinformatics, 2014. View at Publisher · View at Google Scholar
  23. D. S. DeLuca, J. Z. Levin, A. Sivachenko et al., “RNA-SeQC: RNA-seq metrics for quality control and process optimization,” Bioinformatics, vol. 28, no. 11, pp. 1530–1532, 2012. View at Publisher · View at Google Scholar
  24. L. Wang, S. Wang, and W. Li, “RSeQC: quality control of RNA-seq experiments,” Bioinformatics, vol. 28, no. 16, pp. 2184–2185, 2012. View at Publisher · View at Google Scholar
  25. I. Diboun, L. Wernisch, C. A. Orengo, and M. Koltzenburg, “Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma,” BMC Genomics, vol. 7, article 252, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. K. D. Hansen, R. A. Irizarry, and Z. Wu, “Removing technical variability in RNA-seq data using conditional quantile normalization,” Biostatistics, vol. 13, no. 2, pp. 204–216, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. W. H. Kruskal and W. A. Wallis, “Use of ranks in one-criterion variance analysis,” Journal of the American Statistical Association, vol. 47, no. 260, pp. 583–621, 1952. View at Publisher · View at Google Scholar
  28. W. J. Conover, M. E. Johnson, and M. M. Johnson, “A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data,” Technometrics, vol. 23, no. 4, pp. 351–361, 1981. View at Google Scholar · View at Scopus
  29. Y. Shyr and K. Kim, “Weighted flexible compound covariate method for classifying microarray data,” in A Practical Approach to Microarray Data Analysis, D. Berrar, W. Dubitzky, and M. Granzow, Eds., pp. 186–200, Springer, New York, NY, USA, 2003. View at Google Scholar
  30. Y. Guo, C. I. Li, F. Ye, and Y. Shyr, “Evaluation of read count based RNAseq analysis methods,” BMC Genomics, vol. 14, supplement 8, article S2, 2013. View at Publisher · View at Google Scholar
  31. 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, pp. 562–578, 2012. View at Publisher · View at Google Scholar