Homology cluster differential expression analysis for interspecies mRNA-Seq experiments
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Jonathan A. Gelfond
,
Joseph G. Ibrahim
Abstract
There is an increasing demand for exploration of the transcriptomes of multiple species with extraordinary traits such as the naked-mole rat (NMR). The NMR is remarkable because of its longevity and resistance to developing cancer. It is of scientific interest to understand the molecular mechanisms that impart these traits, and RNA-sequencing experiments with comparator species can correlate transcriptome dynamics with these phenotypes. Comparing transcriptome differences requires a homology mapping of each transcript in one species to transcript(s) within the other. Such mappings are necessary, especially if one species does not have well-annotated genome available. Current approaches for this type of analysis typically identify the best match for each transcript, but the best match analysis ignores the inherent risks of mismatch when there are multiple candidate transcripts with similar homology scores. We present a method that treats the set of homologs from a novel species as a cluster corresponding to a single gene in the reference species, and we compare the cluster-based approach to a conventional best-match analysis in both simulated data and a case study with NMR and mouse tissues. We demonstrate that the cluster-based approach has superior power to detect differential expression.
References
Anders, S., D. J. McCarthy, Y. Chen, M. Okoniewski, G. K. Smyth, W. Huber and M. D. Robinson (2013): “Count-based differential expression analysis of rna sequencing data using r and bioconductor,” Nat. Protoc., 8, 1765–1786.Suche in Google Scholar
Buffenstein, R. (2005): “The naked mole-rat: a new long-living model for human aging research,” J. Gerontol. A–Biol., 60, 1369–1377.Suche in Google Scholar
Chen, E. Y., C. M. Tan, Y. Kou, Q. Duan, Z. Wang, G. V. Meirelles, N. R. Clark and A. Maayan (2013): “Enrichr: interactive and collaborative html5 gene list enrichment analysis tool,” BMC Bioinformatics, 14, 128.10.1186/1471-2105-14-128Suche in Google Scholar
de Magalhães, J. P. (2013): “How ageing processes influence cancer,” Nat. Rev. Cancer, 13, 357–365.Suche in Google Scholar
Ebbesen, P. (1974): “Aging increases susceptibility of mouse skin to dmba carcinogenesis independent of general immune status,” Science, 183, 217–218.10.1126/science.183.4121.217Suche in Google Scholar
Fischer, W. H. and W. K. Lutz (1998): “Influence of diet restriction and tumor promoter dose on cell proliferation, oxidative dna damage and rate of papilloma appearance in the mouse skin after initiation with dmba and promotion with tpa,” Toxicol. Lett., 98, 59–69.10.1016/S0378-4274(98)00105-2Suche in Google Scholar
Grabherr, M. G., B. J. Haas, M. Yassour, J. Z. Levin, D. A. Thompson, I. Amit, X. Adiconis, L. Fan, R. Raychowdhury and Q. Zeng (2011): “Full-length transcriptome assembly from rna-seq data without a reference genome,” Nat. biotechnol., 29, 644–652.Suche in Google Scholar
Hawkins, R. D., G. C. Hon and B. Ren (2010): “Next-generation genomics: an integrative approach,” Nat. Rev. Genet., 11, 476–486.Suche in Google Scholar
Keane, M., T. Craig, J. Alfoldi, A. M. Berlin, J. Johnson, A. Seluanov, V. Gorbunova, F. Di Palma, K. Lindblad-Toh, G. M. Church, et al. (2014): “The naked mole rat genome resource: facilitating analyses of cancer and longevity-related adaptations,” Bioinformatics, 30, 3558–3560.10.1093/bioinformatics/btu579Suche in Google Scholar PubMed PubMed Central
Kerr, K. F. (2009): “Comments on the analysis of unbalanced microarray data,” Bioinformatics, 25, 2035–2041.10.1093/bioinformatics/btp363Suche in Google Scholar PubMed PubMed Central
Kim, E. B., X. Fang, A. A. Fushan, Z. Huang, A. V. Lobanov, L. Han, S. M. Marino, X. Sun, A. A. Turanov, P. Yang, et al. (2011): “Genome sequencing reveals insights into physiology and longevity of the naked mole rat,” Nature, 479, 223–227.10.1038/nature10533Suche in Google Scholar PubMed PubMed Central
Li, B. and C. N. Dewey (2011): “Rsem: accurate transcript quantification from rna-seq data with or without a reference genome,” BMC Bioinformatics, 12, 323.10.1186/1471-2105-12-323Suche in Google Scholar PubMed PubMed Central
Liang, S., J. Mele, Y. Wu, R. Buffenstein and P. J. Hornsby (2010): “Resistance to experimental tumorigenesis in cells of a long-lived mammal, the naked mole-rat (heterocephalus glaber),” Aging Cell, 9, 626–635.10.1111/j.1474-9726.2010.00588.xSuche in Google Scholar PubMed PubMed Central
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, 1509–1517.Suche in Google Scholar
Preisser, J. S., K. K. Lohman and P. J. Rathouz (2002): “Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random,” Stat. Med., 21, 3035–3054.Suche in Google Scholar
Seluanov, A., C. Hine, J. Azpurua, M. Feigenson, M. Bozzella, Z. Mao, K. C. Catania and V. Gorbunova (2009): “Hypersensitivity to contact inhibition provides a clue to cancer resistance of naked mole-rat,” Proc. Natl. Acad. Sci., 106, 19352–19357.Suche in Google Scholar
Tatusova, T. A. and T. L. Madden (1999): “Blast 2 sequences, a new tool for comparing protein and nucleotide sequences,” FEMS Microbiol. Lett., 174, 247–250.Suche in Google Scholar
Wang, Z., M. Gerstein and M. Snyder (2009): “Rna-seq: a revolutionary tool for transcriptomics,” Nat. Rev. Genet., 10, 57–63.Suche in Google Scholar
Wilbur, W. J. and D. J. Lipman (1983): “Rapid similarity searches of nucleic acid and protein data banks,” Proc. Natl. Acad. Sci., 80, 726–730.Suche in Google Scholar
Yu, C., Y. Li, A. Holmes, K. Szafranski, C. G. Faulkes, C. W. Coen, R. Buffenstein, M. Platzer, J. P. de Magalhaes and G. M. Church (2011): “Rna sequencing reveals differential expression of mitochondrial and oxidation reduction genes in the long-lived naked mole-rat when compared to mice,” PloS One, 6, e26729.10.1371/journal.pone.0026729Suche in Google Scholar PubMed PubMed Central
Supplemental Material
The online version of this article (DOI: 10.1515/sagmb-2014-0056) offers supplementary material, available to authorized users.
©2015 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Homology cluster differential expression analysis for interspecies mRNA-Seq experiments
- Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling
- On the validity of within-nuclear-family genetic association analysis in samples of extended families
- An Empirical Bayes risk prediction model using multiple traits for sequencing data
- Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Homology cluster differential expression analysis for interspecies mRNA-Seq experiments
- Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling
- On the validity of within-nuclear-family genetic association analysis in samples of extended families
- An Empirical Bayes risk prediction model using multiple traits for sequencing data
- Empirical likelihood tests for nonparametric detection of differential expression from RNA-seq data
