MRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data

Nature Communications

Volumn 6, Issue , 2015, Pages

  Feng, Huijuan ^a,b   Zhang, Xuegong ^a   Zhang, Chaolin ^b  

^a TSINGHUA UNIVERSITY   (China)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA;

ASSESSMENT METHOD; BRAIN; CELL ORGANELLE; DATA SET; FRAGMENTATION; GENE EXPRESSION; GENOME; RNA; STABILIZATION;

3' UNTRANSLATED REGION; 5' UNTRANSLATED REGION; ARTICLE; BASAL GANGLION; BRAIN REGION; CONTROLLED STUDY; DNA BASE COMPOSITION; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENETIC ASSOCIATION; HUMAN; RIBOSOME SUBUNIT; RNA DEGRADATION; RNA SEQUENCE; AUTOPSY; BRAIN; ELECTROPHORESIS; GENE EXPRESSION PROFILING; GENETICS; MACHINE LEARNING; METABOLISM; NUCLEIC ACID DATABASE; PROCEDURES; RNA STABILITY; SEQUENCE ANALYSIS;

AUTOPSY; BRAIN; DATABASES, NUCLEIC ACID; ELECTROPHORESIS; GENE EXPRESSION PROFILING; HUMANS; MACHINE LEARNING; RNA STABILITY; RNA, MESSENGER; SEQUENCE ANALYSIS, RNA;

EID: 84938546430     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms8816     Document Type: Article

References (47)

1. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57-63 (2009).
2. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621-628 (2008).
3. Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623-629 (2013).
4. Romero, I. G., Pai, A. A., Tung, J. & Gilad, Y. RNA-seq: impact of RNA degradation on transcript quantification. BMC Biol. 12, 42 (2014).
5. Lee, J., Hever, A., Willhite, D., Zlotnik, A. & Hevezi, P. Effects of RNA degradation on gene expression analysis of human postmortem tissues. FASEB J. 19, 1356-1358 (2005).
6. GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648-660 (2015).
7. Durrenberger, P. F. et al. Effects of antemortem and postmortem variables on human brain mRNA quality: a BrainNet Europe study. J. Neuropathol. Exp. Neurol. 69, 70-81 (2010).
8. Tomita, H. et al. Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain. Biol. Psychiatry 55, 346-352 (2004).
9. Schroeder, A. et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol. Biol. 7, 3 (2006).
10. Imbeaud, S. et al. Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces. Nucleic Acids Res. 33, e56 (2005).
11. Sigurgeirsson, B., Emanuelsson, O. & Lundeberg, J. Sequencing degraded RNA addressed by 3' tag counting. PLoS ONE 9, e91851 (2014).
12. DeLuca, D. S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530-1532 (2012).
13. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184-2185 (2012).
14. Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207-210 (2002).
15. Garneau, N. L., Wilusz, J. & Wilusz, C. J. The highways and byways of mRNA decay. Nat. Rev. Mol. Cell Biol. 8, 113-126 (2007).
16. Houseley, J. & Tollervey, D. The many pathways of RNA degradation. Cell 136, 763-776 (2009).
17. Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483-489 (2011).
18. Clark, T. et al. Discovery of tissue-specific exons using comprehensive human exon microarrays. Genome Biol. 8, R64 (2007).
19. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863-14868 (1998).
20. Smirnov, N. Table for estimating the goodness of fit of empirical distributions. Ann. Math. Statist. 19, 279-279 (1948).
21. Li, J., Jiang, H. & Wong, W. H. Modeling non-uniformity in short-read rates in RNA-Seq data. Genome Biol. 11, R50 (2010).
22. Dennis, G. et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 4, R60 (2003).
23. Stoecklin, G., Mayo, T. & Anderson, P. ARE-mRNA degradation requires the 5'-3' decay pathway. EMBO Rep. 7, 72-77 (2006).
24. Gruber, A. R., Fallmann, J., Kratochvill, F., Kovarik, P. & Hofacker, I. L. AREsite: a database for the comprehensive investigation of AU-rich elements. Nucleic Acids Res. 39, D66-D69 (2011).
25. Barreau, C., Paillard, L. & Osborne, H. B. AU-rich elements and associated factors: are there unifying principles? Nucleic Acids Res. 33, 7138-7150 (2005).
26. White, E. K., Moore-Jarrett, T. & Ruley, H. E. PUM2, a novel murine puf protein, and its consensus RNA-binding site. RNA 7, 1855-1866 (2001).
27. Li, S. et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32, 888-895 (2014).
28. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in highthroughput experiments. Bioinformatics 28, 882-883 (2012).
29. Stegle, O., Parts, L., Durbin, R. &Winn, J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput. Biol. 6, e1000770 (2010).
30. Wu, Z. P., Wang, X. & Zhang, X. G. Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq. Bioinformatics 27, 502-508 (2011).
31. Ma, X. & Zhang, X. NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data. BMC Bioinformatics 14, 220 (2013).
32. Houseley, J., LaCava, J. & Tollervey, D. RNA-quality control by the exosome. Nat. Rev. Mol. Cell Biol. 7, 529-539 (2006).
33. Sheth, U. & Parker, R. Decapping and decay of messenger RNA occur in cytoplasmic processing bodies. Science 300, 805-808 (2003).
34. Sharova, L. V. et al. Database for mRNA half-life of 19 977 genes obtained by DNA microarray analysis of pluripotent and differentiating mouse embryonic stem cells. DNA Res. 16, 45-58 (2009).
35. Yang, E. et al. Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes. Genome Res. 13, 1863-1872 (2003).
36. Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342 (2011).
37. Karginov, F. V. et al. Diverse endonucleolytic cleavage sites in the mammalian transcriptome depend upon microRNAs, Drosha, and additional nucleases. Mol. Cell 38, 781-788 (2010).
38. Shin, C. et al. Expanding the microRNA targeting code: functional sites with centered pairing. Mol. Cell 38, 789-802 (2010).
39. Mercer, T. R. et al. Regulated post-transcriptional RNA cleavage diversifies the eukaryotic transcriptome. Genome Res. 20, 1639-1650 (2010).
40. Sobell, H. M. Actinomycin and DNA transcription. Proc. Natl Acad. Sci. USA 82, 5328-5331 (1985).
41. Friedel, C. C., Dolken, L., Ruzsics, Z., Koszinowski, U. H. & Zimmer, R. Conserved principles of mammalian transcriptional regulation revealed by RNA half-life. Nucleic Acids Res. 37, e115 (2009).
42. Johnson, N. L., Kotz, S. & Balakrishnan, N. Continuous Univariate Distributions (Wiley, 1994).
43. Habegger, L. et al. RSEQtools: a modular framework to analyze RNA-Seq data using compact, anonymized data summaries. Bioinformatics 27, 281-283 (2011).
44. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105-1111 (2009).
45. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842 (2010).
46. Halees, A. S., El-Badrawi, R. & Khabar, K. S. A. ARED Organism: expansion of ARED reveals AU-rich element cluster variations between human and mouse. Nucleic Acids Res. 36, D137-D140 (2008).
47. Brawand, D. et al. The evolution of gene expression levels in mammalian organs. Nature 478, 343-348 (2011).