Einzeltreffer — DigiBib

RNA-Sequencing is widely used to investigate changes in gene expression at the transcription level in plants. Most plant RNA-Seq analysis pipelines base the normalization approaches on the assumption that total transcript levels do not vary between samples. However, this assumption has not been demonstrated. In fact, many common experimental treatments and genetic alterations affect transcription efficiency or RNA stability, resulting in unequal transcript abundance. The addition of synthetic RNA controls is a simple correction that controls for variation in total mRNA levels. However, adding spike-ins appropriately is challenging with complex plant tissue, and carefully considering how they are added is essential to their successful use. We demonstrate that adding external RNA spike-ins as a normalization control produces differences in RNA-Seq analysis compared to traditional normalization methods, even between two times of day in untreated plants. We illustrate the use of RNA spike-ins with 3' RNA-Seq and present a normalization pipeline that accounts for differences in total transcriptional levels. We evaluate the effect of normalization methods on identifying differentially expressed genes in the context of identifying the effect of the time of day on gene expression and response to chilling stress in sorghum.

Titel:	A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning-expressed responses.
Autor/in / Beteiligte Person:	Laosuntisuk, K ; Vennapusa, A ; Somayanda, IM ; Leman, AR ; Jagadish, SK ; Doherty, CJ
Zeitschrift:	The Plant journal : for cell and molecular biology, Jg. 118 (2024-06-01), Heft 5, S. 1241-1257
Veröffentlichung:	Oxford : Blackwell Scientific Publishers and BIOS Scientific Publishers in association with the Society for Experimental Biology, c1991-, 2024
Medientyp:	academicJournal
ISSN:	1365-313X (electronic)
DOI:	10.1111/tpj.16654
Schlagwort:	Sequence Analysis, RNA methods Gene Expression Profiling methods RNA, Messenger genetics RNA, Messenger metabolism Arabidopsis genetics RNA, Plant genetics Gene Expression Regulation, Plant
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Plant J] 2024 Jun; Vol. 118 (5), pp. 1241-1257. <i>Date of Electronic Publication: </i>2024 Jan 30. MeSH Terms: RNA, Plant* / genetics ; Gene Expression Regulation, Plant* ; Sequence Analysis, RNA / methods ; Gene Expression Profiling / methods ; RNA, Messenger / genetics ; RNA, Messenger / metabolism ; Arabidopsis / genetics References: Alexa, A. & Rahnenfuhrer, J. (2022) TopGO: enrichment analysis for gene ontology. (version R package version 2.38.1). ; Anders, S. & Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biology, 11(10), R106. ; Anders, S., Pyl, P.T. & Huber, W. (2014) HTSeq—a python framework to work with high‐throughput sequencing data. Bioinformatics, 31(2), 166–169. ; Andrews, S. (2010) FastQC: a quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. ; Ashraf, M.A. & Rahman, A. (2019) Cold stress response in Arabidopsis thaliana is mediated by GNOM ARF‐GEF. The Plant Journal: For Cell and Molecular Biology, 97(3), 500–516. ; Athanasiadou, R., Neymotin, B., Brandt, N., Miller, D., Tranchina, D. & Gresham, D. (2016) Growth rate‐dependent global amplification of gene expression. BioRxiv, 044735. Available from: https://doi.org/10.1101/044735. ; Athanasiadou, R., Neymotin, B., Brandt, N., Wang, W., Christiaen, L., Gresham, D. et al. (2019) A complete statistical model for calibration of RNA‐Seq counts using external spike‐ins and maximum likelihood theory. PLoS Computational Biology, 15(3), e1006794. ; BBMap Guide. (2016) September 1, 2016. https://jgi.doe.gov/data‐and‐tools/bbtools/bb‐tools‐user‐guide/bbmap‐guide/. ; Blair, E.J., Bonnot, T., Hummel, M., Hay, E., Marzolino, J.M., Quijada, I.A. et al. (2019) Contribution of time of day and the circadian clock to the heat stress responsive transcriptome in Arabidopsis. Scientific Reports, 9(1), 4814. ; Blevins, W.R., Carey, L.B. & Mar Albà, M. (2019) Transcriptomics data of 11 species of yeast identically grown in rich media and oxidative stress conditions. BMC Research Notes, 12(1), 250. ; Blighe, K., Rana, S., Turkes, E., Ostendorf, B., Grioni, A. & Lewis, M. (2022) EnhancedVolcano: publication‐ready volcano plots with enhanced Colouring and labeling (version R package version 1.14.0). https://github.com/kevinblighe/EnhancedVolcano. ; Bonnot, T., Impa, S., Krishna Jagadish, S.V. & Nagel, D.H. (2023) Time of day and genotype sensitivity adjust molecular responses to temperature stress in sorghum. The Plant Journal: For Cell and Molecular Biology, 116, 1081–1096. Available from: https://doi.org/10.1111/tpj.16467. ; Borsani, O., Zhu, J., Verslues, P.E., Sunkar, R. & Zhu, J.‐K. (2005) Endogenous SiRNAs derived from a pair of natural cis‐antisense transcripts regulate salt tolerance in Arabidopsis. Cell, 123(7), 1279–1291. ; Branco‐Price, C., Kawaguchi, R., Ferreira, R.B. & Bailey‐Serres, J. (2005) Genome‐wide analysis of transcript abundance and translation in Arabidopsis seedlings subjected to oxygen deprivation. Annals of Botany, 96(4), 647–660. ; Brauer, M.J., Huttenhower, C., Airoldi, E.M., Rosenstein, R., Matese, J.C., Gresham, D. et al. (2008) Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Molecular Biology of the Cell, 19(1), 352–367. ; Brennecke, P., Anders, S., Kim, J.K., Kołodziejczyk, A.A., Zhang, X., Proserpio, V. et al. (2013) Accounting for technical noise in single‐cell RNA‐Seq experiments. Nature Methods, 10(11), 1093–1095. ; Bushnell, B. (2014) BBMap: A Fast, Accurate, Splice‐Aware Aligner.” LBNL‐7065E. Berkeley, CA (United States): Lawrence Berkeley National Lab. Available from: https://www.osti.gov/biblio/1241166. ; Byrne, A., Beaudin, A.E., Olsen, H.E., Jain, M., Cole, C., Palmer, T. et al. (2017) Nanopore long‐read RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nature Communications, 8(July), 16027. ; Chen, H. & Boutros, P.C. (2011) VennDiagram: a package for the generation of highly‐customizable Venn and Euler diagrams in R. BMC Bioinformatics, 12(January), 35. ; Coate, J.E. & Doyle, J.J. (2010) Quantifying whole transcriptome size, a prerequisite for understanding transcriptome evolution across species: an example from a plant allopolyploid. Genome Biology and Evolution, 2(July), 534–546. ; Coate, J.E. & Doyle, J.J. (2015) Variation in transcriptome size: are we getting the message? Chromosoma, 124(1), 27–43. ; Czechowski, T., Stitt, M., Altmann, T., Udvardi, M.K. & Scheible, W.‐R. (2005) Genome‐wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiology, 139(1), 5–17. ; Del Pozo, J.C. & Ramirez‐Parra, E. (2015) Whole genome duplications in plants: an overview from Arabidopsis. Journal of Experimental Botany, 66(22), 6991–7003. ; Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S. et al. (2013) STAR: ultrafast universal RNA‐Seq aligner. Bioinformatics, 29(1), 15–21. ; Evans, C., Hardin, J. & Stoebel, D.M. (2018) Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions. Briefings in Bioinformatics, 19(5), 776–792. ; Eveland, A.L., McCarty, D.R. & Koch, K.E. (2008) Transcript profiling by 3′‐untranslated region sequencing resolves expression of gene families. Plant Physiology, 146, 32–44. Available from: https://doi.org/10.1104/pp.107.108597. ; Fernández‐Parras, I., Ramírez‐Tejero, J.A., Luque, F. & Navarro, F. (2021) Several isoforms for each subunit shared by RNA polymerases are differentially expressed in the cultivated olive tree (Olea Europaea L.). Frontiers in Molecular Biosciences, 8(December), 679292. ; Filichkin, S.A., Breton, G., Priest, H.D., Dharmawardhana, P., Jaiswal, P., Fox, S.E. et al. (2011) Global profiling of rice and poplar transcriptomes highlights key conserved circadian‐controlled pathways and cis‐regulatory modules. PLoS One, 6(6), e16907. ; Fowler, S.G., Cook, D. & Thomashow, M.F. (2005) Low temperature induction of Arabidopsis CBF1, 2, and 3 is gated by the circadian clock. Plant Physiology, 137(3), 961–968. ; Fowler, S. & Thomashow, M.F. (2002) Arabidopsis transcriptome profiling indicates that multiple regulatory pathways are activated during cold acclimation in addition to the CBF cold response pathway. The Plant Cell, 14(8), 1675–1690. ; Gandolfo, L.C. & Speed, T.P. (2018) RLE plots: visualizing unwanted variation in high dimensional data. PLoS One, 13(2), e0191629. ; Girke, T., Todd, J., Ruuska, S., White, J., Benning, C. & Ohlrogge, J. (2000) Microarray analysis of developing Arabidopsis seeds. Plant Physiology, 124(4), 1570–1581. ; Grinevich, D.O., Desai, J.S., Stroup, K.P., Duan, J., Slabaugh, E. & Doherty, C.J. (2019) Novel transcriptional responses to heat revealed by turning up the heat at night. Plant Molecular Biology, 101(1–2), 1–19. ; Grün, D., Kester, L. & van Oudenaarden, A. (2014) Validation of noise models for single‐cell Transcriptomics. Nature Methods, 11(6), 637–640. ; Hilson, P., Allemeersch, J., Altmann, T., Aubourg, S., Avon, A., Beynon, J. et al. (2004) Versatile gene‐specific sequence tags for Arabidopsis functional genomics: transcript profiling and reverse genetics applications. Genome Research, 14(10B), 2176–2189. ; Hou, J., Shi, X., Chen Chen, M., Islam, S., Johnson, A.F., Kanno, T. et al. (2018) Global impacts of chromosomal imbalance on gene expression in Arabidopsis and other taxa. Proceedings of the National Academy of Sciences of the United States of America, 115(48), E11321–E11330. ; Israeli, A., Capua, Y., Shwartz, I., Tal, L., Meir, Z., Levy, M. et al. (2019) Multiple Auxin‐response regulators enable stability and variability in leaf development. Current Biology, 29(11), 1746–1759.e5. ; Jiang, L., Schlesinger, F., Davis, C.A., Zhang, Y., Li, R., Salit, M. et al. (2011) Synthetic spike‐in standards for RNA‐Seq experiments. Genome Research, 21(9), 1543–1551. ; Kellenberger, R.T., Byers, K.J.R., De Brito, R.M., Francisco, Y.M., Staedler, A.M., LaFountain, J.S. et al. (2019) Emergence of a floral colour polymorphism by pollinator‐mediated overdominance. Nature Communications, 10, 63. Available from: https://doi.org/10.1038/s41467‐018‐07936‐x. ; Kim, Y.‐K., Cho, B., Cook, D.P., Trcka, D., Wrana, J.L. & Ramalho‐Santos, M. (2023) Absolute scaling of single‐cell transcriptomes identifies pervasive Hypertranscription in adult stem and progenitor cells. Cell Reports, 42(1), 111978. ; Koike, N., Yoo, S.‐H., Huang, H.‐C., Kumar, V., Lee, C., Kim, T.‐K. et al. (2012) Transcriptional architecture and chromatin landscape of the core circadian clock in mammals. Science, 338(6105), 349–354. ; Koiwa, H. (2006) Phosphorylation of RNA polymerase II C‐terminal domain and plant osmotic‐stress responses. In: Rai, A.K. & Takabe, T. (Eds.) Abiotic Stress Tolerance in Plants. Dordrecht: Springer Netherlands, pp. 47–57. ; Kremling, K.A.G., Chen, S.‐Y., Mei‐Hsiu, S., Lepak, N.K., Cinta Romay, M., Swarts, K.L. et al. (2018) Dysregulation of expression correlates with rare‐allele burden and fitness loss in maize. Nature, 555(7697), 520–523. ; Kroustallaki, P., Lirussi, L., Carracedo, S., Panpan You, Q., Esbensen, Y., Götz, A. et al. (2019) SMUG1 promotes telomere maintenance through telomerase RNA processing. Cell Reports, 28(7), 1690–1702.e10. ; Kwapisz, M., Beckouët, F. & Thuriaux, P. (2008) Early evolution of eukaryotic DNA‐dependent RNA polymerases. Trends in Genetics, 24(5), 211–215. ; Lai, X., Bendix, C., Yan, L., Zhang, Y., Schnable, J.C. & Harmon, F.G. (2020) Interspecific analysis of diurnal gene regulation in Panicoid grasses identifies known and novel regulatory motifs. BMC Genomics, 21(1), 428. ; Lemire, A., Lea, K., Batten, D., Jian Gu, S., Whitley, P., Bramlett, K. et al. (2011) Development of ERCC RNA spike‐in control mixes. Journal of Biomolecular Techniques, 22(Suppl), S46. ; Lin, C.Y., Lovén, J., Rahl, P.B., Paranal, R.M., Burge, C.B., Bradner, J.E. et al. (2012) Transcriptional amplification in tumor cells with elevated C‐Myc. Cell, 151(1), 56–67. ; Lin, L., Song, M., Jiang, Y., Zhao, X., Wang, H. & Zhang, L. (2020) Normalizing single‐cell RNA sequencing data with internal spike‐in‐like genes. NAR Genomics and Bioinformatics, 2(3), lqaa059. ; Lippman, S.I. & Broach, J.R. (2009) Protein kinase a and TORC1 activate genes for ribosomal biogenesis by inactivating repressors encoded by Dot6 and its homolog Tod6. Proceedings of the National Academy of Sciences of the United States of America, 106(47), 19928–19933. ; Liu, Y., Wang, Z., Xiaoyuan, W., Zhu, J., Luo, H., Tian, D. et al. (2021) SorGSD: updating and expanding the sorghum genome science database with new contents and tools. Biotechnology for Biofuels, 14(1), 165. ; Louarn, G., Andrieu, B. & Giauffret, C. (2010) A size‐mediated effect can compensate for transient chilling stress affecting maize (Zea Mays) leaf extension. The New Phytologist, 187(1), 106–118. ; Love, M.I., Huber, W. & Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA‐Seq data with DESeq2. Genome Biology, 15(12), 550. ; Lovén, J., Orlando, D.A., Sigova, A.A., Lin, C.Y., Rahl, P.B., Burge, C.B. et al. (2012) Revisiting global gene expression analysis. Cell, 151(3), 476–482. ; Lun, A.T.L., Calero‐Nieto, F.J., Haim‐Vilmovsky, L., Göttgens, B. & Marioni, J.C. (2017) Assessing the reliability of spike‐in normalization for analyses of single‐cell RNA sequencing data. Genome Research, 27(11), 1795–1806. ; Ma, F., Fuqua, B.K., Hasin, Y., Yukhtman, C., Vulpe, C.D., Lusis, A.J. et al. (2019) A comparison between whole transcript and 3’ RNA sequencing methods using Kapa and Lexogen library preparation methods. BMC Genomics, 20, 9. Available from: https://doi.org/10.1186/s12864‐018‐5393‐3. ; McCormick, R.F., Truong, S.K., Sreedasyam, A., Jenkins, J., Shu, S., Sims, D. et al. (2018) The sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization. The Plant Journal: For Cell and Molecular Biology, 93(2), 338–354. ; Michael, T.P., Mockler, T.C., Breton, G., McEntee, C., Byer, A., Trout, J.D. et al. (2008) Network discovery pipeline elucidates conserved time‐of‐day–specific cis‐regulatory modules. PLoS Genetics, 4(2), e14. ; Moll, P., Ante, M., Seitz, A. & Reda, T. (2014) QuantSeq 3′ MRNA sequencing for RNA quantification. Nature Methods, 11(12), i–iii. ; Nadal‐Ribelles, M., Saiful Islam, W., Wei, P.L., Nguyen, M., de Nadal, E., Posas, F. et al. (2019) Sensitive high‐throughput single‐cell RNA‐Seq reveals within‐clonal transcript correlations in yeast populations. Nature Microbiology, 4, 683–692. Available from: https://doi.org/10.1038/s41564‐018‐0346‐9. ; O'Neil, D., Glowatz, H. & Schlumpberger, M. (2013) Ribosomal RNA depletion for efficient use of RNA‐Seq capacity. Current Protocols in Molecular Biology / Edited by Frederick M. Ausubel … [et Al.] Chapter 4 (July): Unit 4.19. ; Palmer, N.A., Basu, S., Heng‐Moss, T., Bradshaw, J.D., Sarath, G. & Louis, J. (2019) Fall armyworm (Spodoptera Frugiperda smith) feeding elicits differential defense responses in upland and lowland Switchgrass. PLoS One, 14(6), e0218352. ; Paul, L., Kubala, P., Horner, G., Ante, M., Holländer, I., Alexander, S. et al. (2016) SIRVs: spike‐in RNA variants as external isoform controls in RNA‐sequencing. BioRxiv, 080747. Available from: https://doi.org/10.1101/080747. ; Percharde, M., Bulut‐Karslioglu, A. & Ramalho‐Santos, M. (2017) Hypertranscription in development, stem cells, and regeneration. Developmental Cell, 40(1), 9–21. ; Pirrello, J., Deluche, C., Frangne, N., Gévaudant, F., Maza, E., Djari, A. et al. (2018) Transcriptome profiling of sorted Endoreduplicated nuclei from tomato fruits: how the global shift in expression ascribed to DNA ploidy influences RNA‐Seq data normalization and interpretation. The Plant Journal: For Cell and Molecular Biology, 93(2), 387–398. ; Popova, O.V., Dinh, H.Q., Aufsatz, W. & Jonak, C. (2013) The RdDM pathway is required for basal heat tolerance in Arabidopsis. Molecular Plant, 6(2), 396–410. ; Raj, A., Peskin, C.S., Tranchina, D., Vargas, D.Y. & Tyagi, S. (2006) Stochastic MRNA synthesis in mammalian cells. PLoS Biology, 4(10), e309. ; Ream, T.S., Haag, J.R. & Pikaard, C.S. (2014) Plant multisubunit RNA polymerases IV and V. In: Murakami, K. & Trakselis, M. (Eds.) Nucleic acid polymerases. Nucleic acids and molecular biology, Vol 30. Berlin, Heidelberg: Springer. Available from: https://doi.org/10.1007/978‐3‐642‐39796‐7_13. ; Risso, D., Ngai, J., Speed, T.P. & Dudoit, S. (2014a) The Role of spike‐in standards in the normalization of RNA‐Seq. In: Datta, S. & Nettleton, D. (Eds.) Statistical analysis of next generation sequencing data. Cham: Springer International Publishing, pp. 169–190. ; Risso, D., Ngai, J., Speed, T.P. & Dudoit, S. (2014b) Normalization of RNA‐Seq data using factor analysis of control genes or samples. Nature Biotechnology, 32(9), 896–902. ; Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. (2011) GC‐content normalization for RNA‐Seq data. BMC Bioinformatics, 12(December), 480. ; Robinson, D.O., Coate, J.E., Singh, A., Hong, L., Bush, M., Doyle, J.J. et al. (2018) Ploidy and size at multiple scales in the Arabidopsis sepal. The Plant Cell, 30(10), 2308–2329. ; Robinson, M.D., McCarthy, D.J. & Smyth, G.K. (2010) EdgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. ; Robinson, M.D. & Oshlack, A. (2010) A scaling normalization method for differential expression analysis of RNA‐Seq data. Genome Biology, 11(3), R25. ; Rymen, B., Fiorani, F., Kartal, F., Vandepoele, K., Inzé, D. & Beemster, G.T.S. (2007) Cold nights impair leaf growth and cell cycle progression in maize through transcriptional changes of cell cycle genes. Plant Physiology, 143(3), 1429–1438. ; Sanchez, A. & Golding, I. (2013) Genetic determinants and cellular constraints in Noisy gene expression. Science, 342(6163), 1188–1193. ; Shao, R., Kumar, B., Lidschreiber, K., Lidschreiber, M., Cramer, P. & Elsässer, S.J. (2022) Distinct transcription kinetics of pluripotent cell states. Molecular Systems Biology, 18(1), e10407. ; Shi, X., Yang, H., Chen, C., Hou, J., Hanson, K.M., Albert, P.S. et al. (2021) Genomic imbalance determines positive and negative modulation of gene expression in diploid maize. The Plant Cell, 33(4), 917–939. ; Silva, K.J., Pereira, J.S., Bednarek, R., Fei, Z. & Khan, A. (2019) Differential gene regulatory pathways and Co‐expression networks associated with fire blight infection in apple (Malus × Domestica). Horticulture Research, 6(April), 35. ; Song, Q., Ando, A., Jiang, N., Ikeda, Y. & Jeffrey Chen, Z. (2020) Single‐cell RNA‐Seq analysis reveals ploidy‐dependent and cell‐specific transcriptome changes in Arabidopsis female gametophytes. Genome Biology, 21(1), 178. ; Stark, R., Grzelak, M. & Hadfield, J. (2019) RNA sequencing: the teenage years. Nature Reviews. Genetics, 20(11), 631–656. ; Szádeczky‐Kardoss, I., Szaker, H.M., Verma, R., Darkó, É., Pettkó‐Szandtner, A., Silhavy, D. et al. (2022) Elongation factor TFIIS is essential for heat stress adaptation in plants. Nucleic Acids Research, 50(4), 1927–1950. ; Tandonnet, S. & Torres, T.T. (2017) Traditional versus 3′ RNA‐Seq in a non‐model species. Genomics Data, 11, 9–16. Available from: https://doi.org/10.1016/j.gdata.2016.11.002. ; Thatcher, L.F., Foley, R., Casarotto, H.J., Gao, L.‐L., Kamphuis, L.G., Melser, S. et al. (2018) The Arabidopsis RNA polymerase II carboxyl terminal domain (CTD) phosphatase‐like1 (CPL1) is a biotic stress susceptibility gene. Scientific Reports, 8(1), 13454. ; Thimm, O., Bläsing, O., Gibon, Y., Nagel, A., Meyer, S., Krüger, P. et al. (2004) Mapman: a user‐driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. The Plant Journal, 37, 914–939. Available from: https://doi.org/10.1111/j.1365‐313x.2004.02016.x. ; Tian, T., Liu, Y., Yan, H., You, Q., Yi, X., Zhou, D. et al. (2017) AgriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Research, 45(W1), W122–W129. ; Topal, S., Vasseur, P., Radman‐Livaja, M. & Peterson, C.L. (2019) Distinct transcriptional roles for histone H3‐K56 acetylation during the cell cycle in yeast. Nature Communications, 10(1), 4372. ; Vannini, A. & Cramer, P. (2012) Conservation between the RNA polymerase I, II, and III transcription initiation machineries. Molecular Cell, 45(4), 439–446. ; Wang, L., Wang, S. & Li, W. (2012) RSeQC: quality control of RNA‐Seq experiments. Bioinformatics, 28(16), 2184–2185. ; Wang, P., Hendron, R.‐W. & Kelly, S. (2017) Transcriptional control of photosynthetic capacity: conservation and divergence from Arabidopsis to rice. The New Phytologist, 216(1), 32–45. ; Wang, X., Frederick, J., Wang, H., Hui, S., Backman, V. & Ji, Z. (2021) Spike‐in normalization for single‐cell RNA‐Seq reveals dynamic global transcriptional activity mediating anticancer drug response. NAR Genomics and Bioinformatics, 3(2), lqab054. ; Wang, Y., Song, L., Liu, M., Ge, R., Zhou, Q., Liu, W. et al. (2018) A proteomics landscape of circadian clock in mouse liver. Nature Communications, 9(1), 1553. ; Wilson, M.R., Reske, J.J., Holladay, J., Wilber, G.E., Rhodes, M., Koeman, J. et al. (2019) ARID1A and PI3‐kinase pathway mutations in the endometrium drive epithelial transdifferentiation and collective invasion. Nature Communications, 10(1), 3554. ; Wos, G., Macková, L., Kubíková, K. & Kolář, F. (2022) Ploidy and local environment drive intraspecific variation in Endoreduplication in Arabidopsis arenosa. American Journal of Botany, 109(2), 259–271. ; Wu, D. (2022) Mouse oocytes, a complex single cell transcriptome. Frontiers in Cell and Developmental Biology, 10(March), 827937. ; Yang, H., Shi, X., Chen, C., Hou, J., Ji, T., Cheng, J. et al. (2021) Predominantly inverse modulation of gene expression in genomically unbalanced disomic haploid maize. The Plant Cell, 33(4), 901–916. ; Yu, R., Campbell, K., Pereira, R., Björkeroth, J., Qi, Q., Vorontsov, E. et al. (2020) Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast. Nature Communications, 11(1), 1881. ; Yu, R., Vorontsov, E., Sihlbom, C. & Nielsen, J. (2021) Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast. eLife, 10(March), e65722. Available from: https://doi.org/10.7554/eLife.65722. ; Yu, Y., Hao, H., Doust, A.N. & Kellogg, E.A. (2020) Divergent gene expression networks underlie morphological diversity of abscission zones in grasses. The New Phytologist, 225(4), 1799–1815. ; Zhang, H., Li, X., Song, R., Zhan, Z., Zhao, F., Li, Z. et al. (2022) Cap‐binding complex assists RNA polymerase II transcription in plant salt stress response. Plant, Cell & Environment, 45(9), 2780–2793. ; Zhang, T., Zhao, X., Wang, W., Pan, Y., Huang, L., Liu, X. et al. (2012) Comparative transcriptome profiling of chilling stress responsiveness in two contrasting rice genotypes. PLoS One, 7(8), e43274. ; Zhao, L., Wang, P., Hou, H., Zhang, H., Wang, Y., Yan, S. et al. (2014) Transcriptional regulation of cell cycle genes in response to abiotic stresses correlates with dynamic changes in histone modifications in maize. PLoS One, 9(8), e106070. ; Zumel, D., Diéguez, X., Werner, O., Moreno‐Ortiz, M.C., Muñoz, J. & Ros, R.M. (2023) High Endoreduplication after drought‐related conditions in haploid but not diploid mosses. Annals of Botany, mcad159. Available from: https://doi.org/10.1093/aob/mcad159. Grant Information: 2210293 Division of Integrative Organismal Systems; Development and Promotion of Science and Technology Talents Project, Bangkok, Thailand; D19AP00026 Biological Technologies Office Contributed Indexing: Keywords: Sorghum bicolor; RNA‐Seq; abiotic stress responses; diel transcriptional changes; gene expression; normalization methods Substance Nomenclature: 0 (RNA, Plant) ; 0 (RNA, Messenger) Entry Date(s): Date Created: 20240130 Date Completed: 20240530 Latest Revision: 20240530 Update Code: 20240530

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

BibTeX Citavi, JabRef, u.a.
(Literaturverwaltung)

PDF kein Volltext!
(Merkzettel, Notizen)

RIS Endnote, Citavi u.a.
(Literaturverwaltung)

MODS
(XML zur Weiterverarbeitung)

oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

Gewünschter Zitations-Stil:

oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning-expressed responses.