Inferential considerations for low-count RNA-seq transcripts: A case study on the dominant prairie grass Andropogon gerardii

BMC Genomics

Volumn 17, Issue 1, 2016, Pages

  Raithel, Seth ^a   Johnson, Loretta ^a   Galliart, Matthew ^a   Brown, Sue ^a   Shelton, Jennifer ^a   Herndon, Nicolae ^a   Bello, Nora M ^a  

^a KANSAS STATE UNIVERSITY   (United States)

Author keywords

Andropogon gerardii; DESeq2; EdgeR robust; Gene filtering; Low count transcripts; Plasmode; RNA seq

Indexed keywords

ANDROPOGON; BEHAVIOR; CASE REPORT; CONTROLLED STUDY; DNA TRANSCRIPTION; DROUGHT TOLERANCE; FAMILY; FILTRATION; GENE EXPRESSION; GENETIC TRANSCRIPTION; GRASS; HUMAN; HUMAN TISSUE; LEAF TISSUE; PIPELINE; PRAIRIE; STATISTICAL ANALYSIS; SUBSPECIES; ADAPTATION; GENETICS; GENOMICS; PHENOTYPE; PROCEDURES; SEQUENCE ANALYSIS;

PLANT RNA; TRANSCRIPTOME;

ADAPTATION, PHYSIOLOGICAL; ANDROPOGON; GENOMICS; PHENOTYPE; RNA, PLANT; SEQUENCE ANALYSIS, RNA; TRANSCRIPTOME;

EID: 84959222823     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-016-2442-7     Document Type: Article

References (42)

1. Kvam VM, Lu P, Si YQ. A comparison of statistical methods for detecting differentially expressed genes from RNA-Seq DATA. Am J Bot. 2012;99(2):248-56.
2. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2013;14(6):671-83.
3. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.
4. Anders S, McCarthy DJ, Chen YS, Okoniewski M, Smyth GK, Huber W, et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc. 2013;8(9):1765-86.
5. Spitz F, Furlong EEM. Transcription factors: from enhancer binding to developmental control. Nat Rev Genet. 2012;13(9):613-26.
6. Bourgon R, Gentleman R, Huber W. Independent filtering increases detection power for high-throughput experiments. Proc Natl Acad Sci U S A. 2010;107(21):9546-51.
7. Reeb PD, Steibel JP. Evaluating statistical analysis models for RNA sequencing experiments. Frontiers in Genetics. 2013;4:1-9.
8. Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics. 2013;14:91.
9. McCarthy DJ, Chen YS, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40(10):4288-97.
10. Zhou XB, Lindsay H, Robinson MD. Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res. 2014;42(11):e91.
11. Owsley M. Plant fact sheet for Big Bluestem (Andropogon gerardii Vitman). USDA-Natural Resources Conservation Service, Jimmy Carter PMC Americus, Georgia 31719. 2011. http://plants.usda.gov/factsheet/pdf/fs_ange.pdf.
12. Knapp AK, Carter GA. Variability in leaf optical properties among 26 species from a broad range of habitats. Am J Bot. 1998;85(7):940-6.
13. Bleed AS, Flowerday CA. An atlas of the Sand Hills. 3rd ed. Lincoln: University of Nebraska-Lincoln; 1998.
14. Barnes PW. Variation in the big bluestem (Andropogon-Gerardii)-sand bluestem (Andropogon-Hallii) complex along a local dune meadow gradient in the Nebraska Sandhills. Am J Bot. 1986;73(2):172-84.
15. Shelton J. Epicuticular wax chemistry, morphology, and physiology in sand bluestem, andropogon gerardii ssp. hallii, and big bluestem, andropogon gerardii spp gerardii. Manhattan: Kansas State University; 2012.
16. IPCC. Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 2013. p. 4-5.
17. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010;11:94.
18. Mehta T, Tanik M, Allison DB. Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nat Genet. 2004;36(9):943-7.
19. Gadbury GL, Xiang QF, Yang L, Barnes S, Page GP, Allison DB. Evaluating statistical methods using plasmode data sets in the age of massive public databases: an illustration using false discovery rates. Plos Genet. 2008;4(6):e1000098.
20. Steibel JP, Poletto R, Coussens PM, Rosa GJM. A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data. Genomics. 2009;94(2):146-52.
21. Robinson MD, Smyth GK. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics. 2008;9(2):321-32.
22. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106.
23. Hogg R, McKean J, Craig A. Introduction to Mathematical Statistics. Essex, England: Pearsons; 2013.
24. Gianola D. Priors in whole-genome regression: The Bayesian alphabet returns. Genetics. 2013;194(3):573-96.
25. Lehermeier C, Wimmer V, Albrecht T, Auinger HJ, Gianola D, Schmid VJ, et al. Sensitivity to prior specification in Bayesian genome-based prediction models. Stat Appl Genet Mol. 2013;12(3):375-91.
26. Agresti A. An Introduction to Categorical Data Analysis, 2nd edn. Hoboken, New Jersey: John Wiley & Sons; 2007.
27. Barnes P: Divergence and adaptation in adjacent plant populations: Studies on the ecology and physiology of the big bluestem (Andropogon gerardii vitman) - sand bluestem (Andropogon hallii hack.) complex in Nebraska. PhD dissertation. University of Nebraska - Lincoln; 1984. http://digitalcommons.unl.edu/dissertations/AAI8423759/.
28. Johnson L, Shelton J, Bello NM, Raithel S, Galliart M, Brown SJ, Herndon N, Welti R, Nikolau B, Samarakoon T, Song Z, Jeannotte R. Divergent epicuticular wax and transcriptome of edaphically differentiated Andropogon grasses in the US Midwest prairies. Abstract. XXIII International Plant & Animal Genome Conference. San Diego, California. January 10-14, 2015. https://pag.confex.com/pag/xxiii/webprogram/Paper16889.html.
29. Schmieder R, Lim YW, Rohwer F, Edwards R. TagCleaner: Identification and removal of tag sequences from genomic and metagenomic datasets. BMC Bioinformatics. 2010;11:341.
30. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27(6):863-4.
31. Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WEG, Wetter T, et al. Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 2004;14(6):1147-59.
32. Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28(8):1086-92.
33. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403-10.
34. Zeng V, Villanueva KE, Ewen-Campen BS, Alwes F, Browne WE, Extavour CG. De novo assembly and characterization of a maternal and developmental transcriptome for the emerging model crustacean Parhyale hawaiensis. BMC Genomics. 2011;12:581.
35. Duan JL, Xia C, Zhao GY, Jia JZ, Kong XY. Optimizing de novo common wheat transcriptome assembly using short-read RNA-Seq data. BMC Genomics. 2012;13:392.
36. Meyer E, Logan TL, Juenger TE. Transcriptome analysis and gene expression atlas for Panicum hallii var. filipes, a diploid model for biofuel research. Plant J. 2012;70(5):879-90.
37. Liu MY, Qiao GR, Jiang J, Yang HQ, Xie LH, Xie JZ, et al. Transcriptome sequencing and De Novo analysis for Ma Bamboo (Dendrocalamus latiflorus Munro) using the Illumina platform. Plos One. 2012;7(10):e46766.
38. Chouvarine P, Cooksey AM, McCarthy FM, Ray DA, Baldwin BS, Burgess SC, et al. Transcriptome-based differentiation of closely-related Miscanthus lines. Plos One. 2012;7(1):168-75.
39. Van Belleghem SM, Roelofs D, Van Houdt J, Hendrickx F. De novo Transcriptome assembly and SNP Discovery in the Wing Polymorphic Salt Marsh Beetle Pogonus chalceus (Coleoptera, Carabidae). Plos One. 2012;7(8):e42605.
40. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357-U354.
41. Chen Y, McCarthy D, Robinson M, Smyth GK. edgeR: Differential expression analysis of digital gene expression data - User's Guide. 2014.
42. Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J Roy Stat Soc B Met. 1995;57(1):289-300.