Advertisement

POPE: Pipeline of Parentally-Biased Expression

  • Victor Missirian
  • Isabelle Henry
  • Luca Comai
  • Vladimir Filkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7292)

Abstract

While one might expect the phenotypes of progeny to be an additive combination of the parents, Mendelian analysis reveals that this is not always the case. Deviations from additive expectation are observable even at the level of gene expression, and identifying such instances is a prerequisite to the understanding of gene regulation and networks. Many biological studies employ mRNA-seq to identify instances where the overall and allelic expression in hybrids deviates from expectation. We describe a pipeline, POPE (Pipeline of Parentally-biased Expression), that is capable of detecting these instances, building off of a linear model of gene expression in terms of regulatory sequence strength and concentration of synergistic transcriptional regulators. We illustrate the performance of POPE on an existing mRNA-seq data set. POPE is implemented entirely in shell, python, and R, and it is designed for unix-based platforms. The code can be found at http://www.cs.ucdavis. edu/~filkov/POPE/ .

Keywords

Computational biology mRNA-seq pipeline additive non-additive trans effect cis effect 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anders, S., Huber, W.: Differential expression analysis for sequence count data. Genome Biol. 11(10), R106 (2010)CrossRefGoogle Scholar
  2. 2.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. 57(1), 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Bullard, J.H., Purdom, E., Hansen, K.D., Dudoit, S.: Evaluation of statistical methods for normalization and differential expression in mrna-seq experiments. BMC Bioinformatics 11, 94 (2010)CrossRefGoogle Scholar
  4. 4.
    Carey, M., Lin, Y.S., Green, M.R., Ptashne, M.: A mechanism for synergistic activation of a mammalian gene by gal4 derivatives. Letters to Nature 345, 361–364 (1990)CrossRefGoogle Scholar
  5. 5.
    Cumbie, J.S., et al.: Gene-counter: A computational pipeline for the analysis of rna-seq data for gene expression differences. PLoS ONE 6(10), e25279 (2011)CrossRefGoogle Scholar
  6. 6.
    Emerson, J.J., Li, W.H.: The genetic basis of evolutionary change in gene expression levels. Phil. Trans. R. Soc. B 365(1552), 2581–2590 (2010)CrossRefGoogle Scholar
  7. 7.
    Filichkin, S.A., et al.: Global profiling of rice and poplar transcriptomes highlights key conserved circadian-controlled pathways and cis-regulatory modules. PLoS ONE 6(6), e16907 (2011)CrossRefGoogle Scholar
  8. 8.
    Groszmann, M., et al.: Changes in 24-nt sirna levels in arabidopsis hybrids suggest an epigenetic contribution to hybrid vigor. Proc. Natl. Acad. Sci. USA 108(6), 2617–2622 (2011)CrossRefGoogle Scholar
  9. 9.
    Hardcastle, T.J., Kelly, K.A.: bayseq: Empirical bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11, 422 (2010)CrossRefGoogle Scholar
  10. 10.
    He, G., et al.: Global epigenetic and transcriptional trends among two rice subspecies and their reciprocal hybrids. Plant Cell 22(1), 17–33 (2010)CrossRefGoogle Scholar
  11. 11.
    Langmead, B., Hansen, K.D., Leek, J.T.: Cloud-scale rna-sequencing differential expression analysis with myrna. Genome Biol. 11(8), R83 (2010)CrossRefGoogle Scholar
  12. 12.
    Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short dna sequences to the human genome. Genome Biol. 10(3), R25 (2009)CrossRefGoogle Scholar
  13. 13.
    Li, H.: Improving snp discovery by base alignment quality. Bioinformatics 27(8), 1157–1158 (2011)CrossRefGoogle Scholar
  14. 14.
    Li, H., Durbin, R.: Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics 25(14), 1754–1760 (2009)CrossRefGoogle Scholar
  15. 15.
    Li, R., et al.: Soap2: an improved ultrafast tool for short read alignment. Bioinformatics 25(15), 1966–1967 (2009)CrossRefGoogle Scholar
  16. 16.
    Lin, Y.S., Carey, M., Ptashne, M., Green, M.R.: How different eukaryotic transcriptional activators can cooperate promiscuously. Letters to Nature 345, 359–361 (1990)CrossRefGoogle Scholar
  17. 17.
    McManus, C.J., et al.: Regulatory divergence in drosophila revealed by mrna-seq. Genome Res. 20(6), 816–825 (2010)CrossRefGoogle Scholar
  18. 18.
    Ni, Z., et al.: Altered circadian rhythms regulate growth vigour in hybrids and allopolyploids. Nature 457(7227), 327–331 (2009)CrossRefGoogle Scholar
  19. 19.
    Oshlack, A., Robinson, M.D., Young, M.D.: From rna-seq reads to differential expression results. Genome Biol. 11(12), 220 (2010)CrossRefGoogle Scholar
  20. 20.
    Ouyang, S., et al.: The tigr rice genome annotation resource: improvements and new features. Nucleic Acids Res. 35(suppl.1), D883–D887 (2007)CrossRefGoogle Scholar
  21. 21.
    Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edger: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1), 139–140 (2010)CrossRefGoogle Scholar
  22. 22.
    Tirosh, I., Reikhav, S., Levy, A.A., Barkai, N.: A yeast hybrid provides insight into the evolution of gene expression regulation. Science 324(5927), 659–662 (2009)CrossRefGoogle Scholar
  23. 23.
    Trapnell, C., Pachter, L., Salzberg, S.L.: Tophat: discovering splice junctions with rna-seq. Bioinformatics 25(9), 1105–1111 (2009)CrossRefGoogle Scholar
  24. 24.
    Turro, E., et al.: Haplotype and isoform specific expression estimation using multi-mapping rna-seq reads. Genome Biology 12(2), R13 (2011)CrossRefGoogle Scholar
  25. 25.
    Zhang, H.Y., et al.: A genome-wide transcription analysis reveals a close correlation of promoter indel polymorphism and heterotic gene expression in rice hybrids. Molecular Plant 1(5), 720–731 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Victor Missirian
    • 1
  • Isabelle Henry
    • 2
  • Luca Comai
    • 2
  • Vladimir Filkov
    • 1
  1. 1.Department of Computer ScienceUniversity of California at DavisDavisUSA
  2. 2.Department of Plant Biology and Genome CenterUniversity of California at DavisDavisUSA

Personalised recommendations