Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays

Gillespie, Colin S.; Lei, Guiyuan; Boys, Richard J.; Greenall, Amanda; Wilkinson, Darren J.

doi:10.1186/1756-0500-3-81

Cited by 16 publications

(15 citation statements)

References 15 publications

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“…A surprising finding of this former study was that all miRNA expression changes occurred with a delay only after 24 h. To find an explanation for this result and to identify dynamic regulatory networks, we performed a series of mRNA microarrays using the same RNA extracts. In addition to the identification of significantly regulated miRNAs and mRNAs over time, this approach allows for building and testing contrasts using the same linear model (45). Based on our benchmarking results of three methods (‘betr’, ‘timecourse’ and ‘limma’), ‘limma’ proved to be superior in terms of FDR for permutated data sets and synthetic data.…”

Section: Discussionmentioning

confidence: 99%

“…Different methods [‘betr’ (43), ‘timecourse’ (44) and ‘limma’ (33)] to identify significantly differentially expressed (SDE) miRNAs and mRNAs were tested (Supplementary Data and Supplementary Table S2). In this study, linear models and the empirical Bayes method from the ‘limma’ package of R/Bioconductor were used for differential analysis of the miRNA and mRNA time-series data sets as described before (45). In addition, to obtain a list of features significantly regulated at each time point, we used the same ‘limma’ model and a set of contrasts, comparing expression in IFN-γ–treated samples versus untreated controls.…”

Section: Methodsmentioning

confidence: 99%

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Interplay of microRNAs, transcription factors and target genes: linking dynamic expression changes to function

Nazarov

Reinsbach

Muller

et al. 2013

Nucleic Acids Research

130

109

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MicroRNAs (miRNAs) are ubiquitously expressed small non-coding RNAs that, in most cases, negatively regulate gene expression at the post-transcriptional level. miRNAs are involved in fine-tuning fundamental cellular processes such as proliferation, cell death and cell cycle control and are believed to confer robustness to biological responses. Here, we investigated simultaneously the transcriptional changes of miRNA and mRNA expression levels over time after activation of the Janus kinase/Signal transducer and activator of transcription (Jak/STAT) pathway by interferon-γ stimulation of melanoma cells. To examine global miRNA and mRNA expression patterns, time-series microarray data were analysed. We observed delayed responses of miRNAs (after 24–48 h) with respect to mRNAs (12–24 h) and identified biological functions involved at each step of the cellular response. Inference of the upstream regulators allowed for identification of transcriptional regulators involved in cellular reactions to interferon-γ stimulation. Linking expression profiles of transcriptional regulators and miRNAs with their annotated functions, we demonstrate the dynamic interplay of miRNAs and upstream regulators with biological functions. Finally, our data revealed network motifs in the form of feed-forward loops involving transcriptional regulators, mRNAs and miRNAs. Additional information obtained from integrating time-series mRNA and miRNA data may represent an important step towards understanding the regulatory principles of gene expression.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Interplay of microRNAs, transcription factors and target genes: linking dynamic expression changes to function

Nazarov

Reinsbach

Muller

et al. 2013

Nucleic Acids Research

130

109

View full text Add to dashboard Cite

show abstract

“…Microarray data were analyzed using R/Bioconductor (37), the '.CEL' data files imported using the affy package (1.52.0) (38). Scripts from Gillespie et al (39) were used to mask the S. pombe probes, to extract the data for the S. cerevisiae probes. The intensity values from the S. cerevisiae probes were normalized using the Robust Multi-array Average (RMA) (40) function from the affy package.…”

Section: Methodsmentioning

confidence: 99%

Knockout of the Hmt1p Arginine Methyltransferase in Saccharomyces cerevisiae Leads to the Dysregulation of Phosphate-associated Genes and Processes

Chia

Lai

Yagoub

et al. 2018

Molecular & Cellular Proteomics

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Hmt1p is the predominant arginine methyltransferase in Its substrate proteins are involved in transcription, transcriptional regulation, nucleocytoplasmic transport and RNA splicing. Hmt1p-catalyzed methylation can also modulate protein-protein interactions. Hmt1p is conserved from unicellular eukaryotes through to mammals where its ortholog, PRMT1, is lethal upon knockout. In yeast, however, the effect of knockout on the transcriptome and proteome has not been described. Transcriptome analysis revealed downregulation of phosphate-responsive genes inΔ, including acid phosphatases ,, and , phosphate transporters and and the vacuolar transporter chaperone Analysis of the Δ proteome revealed decreased abundance of phosphate-associated proteins including phosphate transporter Pho84p, vacuolar alkaline phosphatase Pho8p, acid phosphatase Pho3p and subunits of the vacuolar transporter chaperone complex Vtc1p, Vtc3p and Vtc4p. Consistent with this, phosphate homeostasis was dysregulated inΔ cells, showing decreased extracellular phosphatase levels and decreased total P in phosphate-depleted medium. , we showed that transcription factor Pho4p can be methylated at Arg-241, which could explain phosphate dysregulation inΔ if interplay exists with phosphorylation at Ser-242 or Ser-243, or if Arg-241 methylation affects the capacity of Pho4p to homodimerize or interact with Pho2p. However, the Arg-241 methylation site was not validated and the localization of a Pho4p-GFP fusion inΔ was not different from wild type. To our knowledge, this is the first study to reveal an association between Hmt1p and phosphate homeostasis and one which suggests a regulatory link between S-adenosyl methionine and intracellular phosphate.

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“…Microarray data were processed using the MAS 5.0 algorithm implemented in the Bioconductor R package (Gentleman et al 2004) after removing data from probes for Schizosaccharomyces pombe transcripts using published methods (Gillespie et al 2010). Differentially expressed genes were identified using the limma R package (Smyth 2005), using a Benjamini-Hochberg adjusted P-value threshold of 0.05 and fold-change threshold of 1.7 (up or down), as previously described (Van Wageningen et al 2010;Lenstra et al 2011).…”

Section: Microarray Data and Analysismentioning

confidence: 99%

Histone Sprocket Arginine Residues Are Important for Gene Expression, DNA Repair, and Cell Viability inSaccharomyces cerevisiae

Hodges

Gallegos

Laughery³

et al. 2015

Genetics

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A critical feature of the intermolecular contacts that bind DNA to the histone octamer is the series of histone arginine residues that insert into the DNA minor groove at each superhelical location where the minor groove faces the histone octamer. One of these "sprocket" arginine residues, histone H4 R45, significantly affects chromatin structure in vivo and is lethal when mutated to alanine or cysteine in Saccharomyces cerevisiae (budding yeast). However, the roles of the remaining sprocket arginine residues (H3 R63, H3 R83, H2A R43, H2B R36, H2A R78, H3 R49) in chromatin structure and other cellular processes have not been well characterized. We have genetically characterized mutations in each of these histone residues when introduced either singly or in combination to yeast cells. We find that pairs of arginine residues that bind DNA adjacent to the DNA exit/entry sites in the nucleosome are lethal in yeast when mutated in combination and cause a defect in histone occupancy. Furthermore, mutations in individual residues compromise repair of UV-induced DNA lesions and affect gene expression and cryptic transcription. This study reveals simple rules for how the location and structural mode of DNA binding influence the biological function of each histone sprocket arginine residue.KEYWORDS nucleotide excision repair; cryptic transcription; nucleosome; histone assembly T HE histone octamer, which is composed of two copies each of histones H2A, H2B, H3, and H4, binds with high affinity to $147 bp of DNA to form the nucleosome core particle. Histone-DNA binding is mediated by .100 histone main-chain and side-chain interactions with the DNA sugarphosphate backbone and a similar number of indirect water-mediated interactions (Davey et al. 2002;Muthurajan et al. 2003). These interactions occur primarily at 14 locations in the nucleosome structure where the DNA minor groove faces the histone octamer [superhelical locations (SHL) 26.5 to 6.5] (Luger et al. 1997). At each of these locations, an arginine side chain extends into the DNA minor groove and makes extensive contacts with the DNA backbone ( Figure 1A).We will refer to the arginine residues that insert into the DNA minor groove as "sprocket" arginines, since they engage the DNA "chain" like the teeth of a bicycle sprocket wheel. Sprocket arginine residues are highly conserved (Muthurajan et al. 2003;Sullivan and Landsman 2003), and the insertion of sprocket arginine side chains into the DNA minor groove comprises a significant fraction of the solvent accessible surface area that is buried upon histone-DNA binding (Davey et al. 2002). Studies have suggested that sprocket arginine-DNA contacts may play an important role in the rotational positioning of nucleosomes (Luger et al. 1997;Harp et al. 2000;Rohs et al. 2009;Wang et al. 2010;West et al. 2010). For example, short poly(A) stretches narrow the DNA minor groove, potentially enhancing electrostatic interactions between the DNA phosphate backbone and the sprocket arginine (Rohs et al. 2009;West et al. 201...

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Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays

Cited by 16 publications

References 15 publications

Interplay of microRNAs, transcription factors and target genes: linking dynamic expression changes to function

Interplay of microRNAs, transcription factors and target genes: linking dynamic expression changes to function

Knockout of the Hmt1p Arginine Methyltransferase in Saccharomyces cerevisiae Leads to the Dysregulation of Phosphate-associated Genes and Processes

Histone Sprocket Arginine Residues Are Important for Gene Expression, DNA Repair, and Cell Viability inSaccharomyces cerevisiae

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