Startseite A novel method to prioritize RNAseq data for post-hoc analysis based on absolute changes in transcript abundance
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

A novel method to prioritize RNAseq data for post-hoc analysis based on absolute changes in transcript abundance

  • Patrick McNutt EMAIL logo , Ian Gut , Kyle Hubbard und Phil Beske
Veröffentlicht/Copyright: 11. März 2015
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Statistical Applications in Genetics and Molecular Biology
Aus der Zeitschrift Statistical Applications in Genetics and Molecular Biology Band 14 Heft 3

Abstract

The use of fold-change (FC) to prioritize differentially expressed genes (DEGs) for post-hoc characterization is a common technique in the analysis of RNA sequencing datasets. However, the use of FC can overlook certain population of DEGs, such as high copy number transcripts which undergo metabolically expensive changes in expression yet fail to exceed the ratiometric FC cut-off, thereby missing potential important biological information. Here we evaluate an alternative approach to prioritizing RNAseq data based on absolute changes in normalized transcript counts (ΔT) between control and treatment conditions. In five pairwise comparisons with a wide range of effect sizes, rank-ordering of DEGs based on the magnitude of ΔT produced a power curve-like distribution, in which 4.7–5.0% of transcripts were responsible for 36–50% of the cumulative change. Thus, differential gene expression is characterized by the high production-cost expression of a small number of genes (large ΔT genes), while the differential expression of the majority of genes involves a much smaller metabolic investment by the cell. To determine whether the large ΔT datasets are representative of coordinated changes in the transcriptional program, we evaluated large ΔT genes for enrichment of gene ontologies (GOs) and predicted protein interactions. In comparison to randomly selected DEGs, the large ΔT transcripts were significantly enriched for both GOs and predicted protein interactions. Furthermore, enrichments were were consistent with the biological context of each comparison yet distinct from those produced using equal-sized populations of large FC genes, indicating that the large ΔT genes represent an orthagonal transcriptional response. Finally, the composition of the large ΔT gene sets were unique to each pairwise comparison, indicating that they represent coherent and context-specific responses to biological conditions rather than the non-specific upregulation of a family of genes. These findings suggest that the large ΔT genes are not a product of random or stochastic phenomenon, but rather represent biologically meaningful changes in the transcriptional program. They furthermore imply that high abundance transcripts are associated with particularly cellular states, and as cells change in response to internal or external conditions, the relative distribution of the abundant transcripts changes accordingly. Thus, prioritization of DEGs based on the concept of metabolic cost is a simple yet powerful method to identify biologically important transcriptional changes and provide novel insights into cellular behaviors.

Keywords: bioinformatics; botulinum neurotoxin; differential gene expression; excitotoxicity; fold-change; functional annotation; gene ontologies; RNA sequencing; neurogenesis; neurotoxicity
Corresponding author: Patrick McNutt, US Army Medical Research Institute of Chemical Defense, 3100 Ricketts Point Road, Gunpowder, MD 21010, USA, e-mail:

Acknowledgments

This work was funded by the National Institutes of Health National Institute of Allergy and Infectious Diseases (IAA number AOD12058-0001-0000) and the Defense Threat Reduction Agency – Joint Science and Technology Office, Medical S&T Division (grant number CBM.THRTOX.01.10.RC.021). This research was performed while I.G. and P.B. held Defense Threat Reduction Agency-National Research Council Research Associateship Awards and K.H. held a National Research Council Research Associateship Award. We thank Angela Adkins, Megan Lyman, and Marian Nelson (USAMRICD, MD) for technical assistance and Cindy Kronman and Riannon Hazell (USAMRICD, MD) for editorial and administrative assistance. The views expressed in this article are those of the authors and do not reflect the official policy of the Department of Army, Department of Defense, or the U.S. Government.

References

Alstott, J., E. Bullmore and D. Plenz (2014): “Powerlaw: A python package for analysis of heavy-tailed distributions,” PLoS ONE 9(1): e85777.10.1371/journal.pone.0085777Suche in Google Scholar PubMed PubMed Central

Anders, S. and W. Huber (2010): “Differential expression analysis for sequence count data,” Genome Biol., 11(10), R106.Suche in Google Scholar

Anders, S., A. Reyes and W. Huber (2012): “Detecting differential usage of exons from RNA-seq data,” Genome Res., 22(10), 2008–2017.Suche in Google Scholar

Ardizzone, T. D., A. Lu, K. R. Wagner, Y. Tang, R. Ran and F. R. Sharp (2004): “Glutamate receptor blockade attenuates glucose hypermetabolism in perihematomal brain after experimental intracerebral hemorrhage in rat,” Stroke, 35(11), 2587–2591.10.1161/01.STR.0000143451.14228.ffSuche in Google Scholar PubMed

Baker, M. (2012): “Digital PCR hits its stride,” Nat. Methods, 9, 541–544.Suche in Google Scholar

Benjamini, Y. and Y. Hochberg (1995): “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” J. Roy. Stat. Soc. B (Met.) 57(1), 289–300.10.1111/j.2517-6161.1995.tb02031.xSuche in Google Scholar

Bergmann, S., J. Ihmels and N. Barkai (2004): “Similarities and differences in genome-wide expression data of six organisms,” PLoS Biol., 2(1), E9.10.1371/journal.pbio.0020009Suche in Google Scholar PubMed PubMed Central

Bi, Y. and R. V. Davuluri (2013): “NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data,” BMC Bioinformatics, 14, 262.10.1186/1471-2105-14-262Suche in Google Scholar PubMed PubMed Central

Bindea, G., B. Mlecnik, H. Hackl, P. Charoentong, M. Tosolini, A. Kirilovsky, W. H. Fridman, F. Pages, Z. Trajanoski and J. Galon (2009): “ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks,” Bioinformatics, 25(8), 1091–1093.10.1093/bioinformatics/btp101Suche in Google Scholar PubMed PubMed Central

Blaybel, R., O. Theoleyre, A. Douablin and F. Baklouti (2008): “Downregulation of the Spi-1/PU.1 oncogene induces the expression of TRIM10/HERF1, a key factor required for terminal erythroid cell differentiation and survival,” Cell Res, 18(8), 834–845.Suche in Google Scholar

Bullard, J. H., E. Purdom, K. D. Hansen and S. Dudoit (2010): “Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments,” BMC Bioinformatics, 11, 94.10.1186/1471-2105-11-94Suche in Google Scholar PubMed PubMed Central

Cline, M. S., M. Smoot, E. Cerami, A. Kuchinsky, N. Landys, C. Workman, R. Christmas, I. Avila-Campilo, M. Creech, B. Gross, K. Hanspers, R. Isserlin, R. Kelley, S. Killcoyne, S. Lotia, S. Maere, J. Morris, K. Ono, V. Pavlovic, A. R. Pico, A. Vailaya, P. L. Wang, A. Adler, B. R. Conklin, L. Hood, M. Kuiper, C. Sander, I. Schmulevich, B. Schwikowski, G. J. Warner, T. Ideker and G. D. Bader (2007): “Integration of biological networks and gene expression data using Cytoscape,” Nat. Protoc., 2(10), 2366–2382.10.1038/nprot.2007.324Suche in Google Scholar PubMed PubMed Central

Coffield, J. A. and X. Yan (2009): “Neuritogenic actions of botulinum neurotoxin A on cultured motor neurons,” J. Pharmacol. Exp. Ther., 330(1), 352–358.10.1124/jpet.108.147744Suche in Google Scholar PubMed PubMed Central

de Paiva, A., F. A. Meunier, J. Molgo, K. R. Aoki and J. O. Dolly (1999): “Functional repair of motor endplates after botulinum neurotoxin type A poisoning: biphasic switch of synaptic activity between nerve sprouts and their parent terminals,” Proc. Natl. Acad. Sci. USA, 96(6), 3200–3205.10.1073/pnas.96.6.3200Suche in Google Scholar PubMed PubMed Central

Dillies, M. A., A. Rau, J. Aubert, C. Hennequet-Antier, M. Jeanmougin, N. Servant, C. Keime, G. Marot, D. Castel, J. Estelle, G. Guernec, B. Jagla, L. Jouneau, D. Laloe, C. Le Gall, B. Schaeffer, S. Le Crom, M. Guedj, F. Jaffrezic and C. French StatOmique (2013): “A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis,” Brief Bioinform., 14(6), 671–683.Suche in Google Scholar

Endersby, R., I. J. Majewski, L. Winteringham, J. G. Beaumont, A. Samuels, R. Scaife, E. Lim, M. Crossley, S. P. Klinken and J. P. Lalonde (2008): “Hls5 regulated erythroid differentiation by modulating GATA-1 activity,” Blood, 111(4), 1946–1950.10.1182/blood-2007-04-085746Suche in Google Scholar PubMed

Furusawa, C. and K. Kaneko (2003): “Zipf’s law in gene expression,” Phys. Rev. Lett., 90(8), 088102.Suche in Google Scholar

Greenbaum, D., C. Colangelo, K. Williams and M. Gerstein (2003): “Comparing protein abundance and mRNA expression levels on a genomic scale,” Genome Biol., 4(9), 117.Suche in Google Scholar

Guo, Y., P. Xiao, S. Lei, F. Deng, G. G. Xiao, Y. Liu, X. Chen, L. Li, S. Wu, Y. Chen, H. Jiang, L. Tan, J. Xie, X. Zhu, S. Liang and H. Deng (2008): “How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes,” Acta Biochim. Biophys. Sin (Shanghai), 40(5), 426–436.10.1111/j.1745-7270.2008.00418.xSuche in Google Scholar PubMed

Gut, I. M., P. H. Beske, K. S. Hubbard, M. E. Lyman, T. A. Hamilton and P. M. McNutt (2013): “Novel application of stem cell-derived neurons to evaluate the time- and dose-dependent progression of excitotoxic injury,” PLoS One, 8(5), e64423.10.1371/journal.pone.0064423Suche in Google Scholar PubMed PubMed Central

Huang da, W., B. T. Sherman, Q. Tan, J. R. Collins, W. G. Alvord, J. Roayaei, R. Stephens, M. W. Baseler, H. C. Lane and R. A. Lempicki (2007): “The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists,” Genome Biol., 8(9), R183.Suche in Google Scholar

Huang da, W., B. T. Sherman and R. A. Lempicki (2009a): “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Res., 37(1), 1–13.10.1093/nar/gkn923Suche in Google Scholar PubMed PubMed Central

Huang, D. W., Sherman, B. T., Zheng, X., Yang, J., Imamichi, T., Stephens, R. and Lempicki, R. A. (2009): “Extracting biological meaning from large gene lists with DAVID,” Current Protocols in Bioinformatics. (27)13.11, 13.11.1–13.11.13.Suche in Google Scholar

Hubbard, K. S., I. M. Gut, M. E. Lyman and P. M. McNutt (2013): “Longitudinal RNA sequencing of the deep transcriptome during neurogenesis of glutamatergic neurons from murine ESCs,” F1000 Research, 2(35).10.12688/f1000research.2-35.v1Suche in Google Scholar PubMed PubMed Central

Hubbard, K. S., I. M. Gut, M. E. Lyman, K. M. Tuznik, M. T. Mesngon and P. M. McNutt (2012): “High yield derivation of enriched glutamatergic neurons from suspension-cultured mouse ESCs for neurotoxicology research,” BMC Neuroscience, 13(127).10.1186/1471-2202-13-127Suche in Google Scholar PubMed PubMed Central

Iyer-Biswas, S., F. Hayot and C. Jayaprakash (2009): “Stochasticity of gene products from transcriptional pulsing,” Phys. Rev. E Stat. Nonlin. Soft. Matter Phys., 79(3 Pt 1), 031911.Suche in Google Scholar

Jensen, L. J., M. Kuhn, M. Stark, S. Chaffron, C. Creevey, J. Muller, T. Doerks, P. Julien, A. Roth, M. Simonovic, P. Bork and C. von Mering (2009): “STRING 8–a global view on proteins and their functional interactions in 630 organisms,” Nucleic Acids Res., 37(Database issue), D412–416.Suche in Google Scholar

Jiang, L., F. Schlesinger, C. A. Davis, Y. Zhang, R. Li, M. Salit, T. R. Gingeras and B. Oliver (2011): “Synthetic spike-in standards for RNA-seq experiments,” Genome Res., 21(9), 1543–1551.Suche in Google Scholar

Krewski, D., D. Acosta Jr., M. Andersen, H. Anderson, J. C. Bailar, 3rd, K. Boekelheide, R. Brent, G. Charnley, V. G. Cheung, S. Green Jr., K. T. Kelsey, N. I. Kerkvliet, A. A. Li, L. McCray, O. Meyer, R. D. Patterson, W. Pennie, R. A. Scala, G. M. Solomon, M. Stephens, J. Yager and L. Zeise (2010): “Toxicity testing in the 21st century: a vision and a strategy,” J. Toxicol. Environ. Health B Crit. Rev., 13(2–4): 51–138.10.1080/10937404.2010.483176Suche in Google Scholar PubMed PubMed Central

Leng, N., J. A. Dawson, J. A. Thomson, V. Ruotti, A. I. Rissman, B. M. Smits, J. D. Haag, M. N. Gould, R. M. Stewart and C. Kendziorski (2013): “EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments,” Bioinformatics, 29(8), 1035–1043.10.1093/bioinformatics/btt087Suche in Google Scholar PubMed PubMed Central

Love, M. I., W. Huber and S. Anders (2014): “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2,” Genome Biol., 15(12), 550.Suche in Google Scholar

Maere, S., K. Heymans and M. Kuiper (2005): “BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks,” Bioinformatics, 21(16), 3448–3449.10.1093/bioinformatics/bti551Suche in Google Scholar PubMed

Marioni, J. C., C. E. Mason, S. M. Mane, M. Stephens and Y. Gilad (2008): “RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays,” Genome Res., 18(9), 1509–1517.Suche in Google Scholar

McNutt, P., J. Celver, T. Hamilton and M. Mesngon (2011): “Embryonic stem cell-derived neurons are a novel, highly sensitive tissue culture platform for botulinum research,” Biochem. Biophys. Res. Commun., 405(1), 85–90.10.1016/j.bbrc.2010.12.132Suche in Google Scholar PubMed

Montroll, E. W. and M. F. Shlesinger (1982): “On 1/f noise and other distributions with long tails,” Proc. Natl. Acad. Sci. USA, 79(10), 3380–3383.10.1073/pnas.79.10.3380Suche in Google Scholar PubMed PubMed Central

Mutch, D. M., A. Berger, R. Mansourian, A. Rytz and M. A. Roberts (2002): “The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data,” BMC Bioinformatics, 3, 17.10.1186/1471-2105-3-17Suche in Google Scholar PubMed PubMed Central

Nagalakshmi, U., Z. Wang, K. Waern, C. Shou, D. Raha, M. Gerstein and M. Snyder (2008): “The transcriptional landscape of the yeast genome defined by RNA sequencing,” Science, 320(5881), 1344–1349.10.1126/science.1158441Suche in Google Scholar PubMed PubMed Central

Novelli, A., J. A. Reilly, P. G. Lysko and R. C. Henneberry (1988): “Glutamate becomes neurotoxic via the N-methyl-D-aspartate receptor when intracellular energy levels are reduced,” Brain Res., 451(1–2), 205–212.Suche in Google Scholar

Rapaport, F., R. Khanin, Y. Liang, M. Pirun, A. Krek, P. Zumbo, C. E. Mason, N. D. Socci and D. Betel (2013): “Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data,” Genome Biol., 14(9), R95.Suche in Google Scholar

Redmond, L. C., C. I. Dumur, K. J. Archer, J. L. Haar and J. A. Lloyd (2008): “Identification of erythroid-enriched gene expression in the mouse embryonic yolk sac using microdissected cells,” Dev. Dyn., 237(2), 436–446.Suche in Google Scholar

Robinson, M. D., D. J. McCarthy and G. K. Smyth (2010): “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data,” Bioinformatics, 26(1), 139–140.10.1093/bioinformatics/btp616Suche in Google Scholar PubMed PubMed Central

Robinson, M. D. and G. K. Smyth (2007): “Moderated statistical tests for assessing differences in tag abundance,” Bioinformatics, 23(21), 2881–2887.10.1093/bioinformatics/btm453Suche in Google Scholar PubMed

Salari, R., D. Wojtowicz, J. Zheng, D. Levens, Y. Pilpel and T. M. Przytycka (2012): “Teasing apart translational and transcriptional components of stochastic variations in eukaryotic gene expression,” PLoS Comput. Biol., 8(8), e1002644.Suche in Google Scholar

Schwanhausser, B., D. Busse, N. Li, G. Dittmar, J. Schuchhardt, J. Wolf, W. Chen and M. Selbach (2011): “Global quantification of mammalian gene expression control,” Nature, 473(7347), 337–342.10.1038/nature10098Suche in Google Scholar PubMed

Simpson, L. L. (2004): “Identification of the major steps in botulinum toxin action,” Annu. Rev. Pharmacol. Toxicol., 44, 167–193.Suche in Google Scholar

Soneson, C. and M. Delorenzi (2013): “A comparison of methods for differential expression analysis of RNA-seq data,” BMC Bioinformatics, 14, 91.10.1186/1471-2105-14-91Suche in Google Scholar PubMed PubMed Central

Spandidos, A., X. Wang, H. Wang, S. Dragnev, T. Thurber and B. Seed (2008): “A comprehensive collection of experimentally validated primers for Polymerase Chain Reaction quantitation of murine transcript abundance,” BMC Genomics, 9, 633.10.1186/1471-2164-9-633Suche in Google Scholar PubMed PubMed Central

Storey, J. D. (2003): “The positive false discovery rate: A Bayesian interpretation and the q-value,” Ann. Stat., 31(6), 2013–2035.Suche in Google Scholar

Sultan, M., M. H. Schulz, H. Richard, A. Magen, A. Klingenhoff, M. Scherf, M. Seifert, T. Borodina, A. Soldatov, D. Parkhomchuk, D. Schmidt, S. O’Keeffe, S. Haas, M. Vingron, H. Lehrach and M. L. Yaspo (2008): “A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome,” Science, 321(5891), 956–960.10.1126/science.1160342Suche in Google Scholar PubMed

Tallack, M. R., G. W. Magor, B. Dartigues, L. Sun, S. Huang, J. M. Fittock, S. V. Fry, E. A. Glazov, T. L. Bailey and A. C. Perkins (2012): “Novel roles for KLF1 in erythropoiesis revealed by mRNA-seq,” Genome Res., 22(12), 2385–2398.Suche in Google Scholar

Tarazona, S., F. Garcia-Alcalde, J. Dopazo, A. Ferrer and A. Conesa (2011): “Differential expression in RNA-seq: a matter of depth,” Genome Res., 21(12), 2213–2223.Suche in Google Scholar

Vogelstein, B. and K. W. Kinzler (1999): “Digital PCR,” Proc. Natl. Acad. Sci. USA, 96(16), 9236–9241.10.1073/pnas.96.16.9236Suche in Google Scholar PubMed PubMed Central

Washburn, M. P., A. Koller, G. Oshiro, R. R. Ulaszek, D. Plouffe, C. Deciu, E. Winzeler and J. R. Yates, 3rd (2003): “Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae,” Proc. Natl. Acad. Sci. USA, 100(6), 3107–3112.10.1073/pnas.0634629100Suche in Google Scholar PubMed PubMed Central

Supplemental Material

The online version of this article (DOI: 10.1515/sagmb-2014-0018) offers supplementary material, available to authorized users.

Published Online: 2015-3-11
Published in Print: 2015-6-1

©2015 by De Gruyter

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. A novel method to prioritize RNAseq data for post-hoc analysis based on absolute changes in transcript abundance
  4. A mutual information estimator with exponentially decaying bias
  5. Bayes factors based on robust TDT-type tests for family trio design
  6. Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies
  7. Weighted Kolmogorov Smirnov testing: an alternative for Gene Set Enrichment Analysis
  8. Application of the fractional-stable distributions for approximation of the gene expression profiles
  9. Software and Application Notes
  10. CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data
  11. TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists
https://doi.org/10.1515/sagmb-2014-0018
Schlagwörter für diesen Artikel
bioinformatics; botulinum neurotoxin; differential gene expression; excitotoxicity; fold-change; functional annotation; gene ontologies; RNA sequencing; neurogenesis; neurotoxicity

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. A novel method to prioritize RNAseq data for post-hoc analysis based on absolute changes in transcript abundance
  4. A mutual information estimator with exponentially decaying bias
  5. Bayes factors based on robust TDT-type tests for family trio design
  6. Modeling gene-covariate interactions in sparse regression with group structure for genome-wide association studies
  7. Weighted Kolmogorov Smirnov testing: an alternative for Gene Set Enrichment Analysis
  8. Application of the fractional-stable distributions for approximation of the gene expression profiles
  9. Software and Application Notes
  10. CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data
  11. TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 16.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/sagmb-2014-0018/pdf
Button zum nach oben scrollen