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pmid:25152866

Transcriptome profiling of Set5 and Set1 methyltransferases: Tools for visualization of gene expression.

Glòria Mas Martín · Devin A King · Pablo E Garcia-Nieto · Ashby J Morrison

Genomics data · 2014 doi:10.1016/j.gdata.2014.07.001

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Transcriptome profiling of Set5 and Set1 methyltransferases: Tools for visualization of gene expression.
doi
10.1016/j.gdata.2014.07.001
pmid
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Glòria Mas Martín
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Devin A King
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Pablo E Garcia-Nieto
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Ashby J Morrison
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Genomics data
year
2014
abstract
Cells regulate transcription by coordinating the activities of multiple histone modifying complexes. We recently identified the yeast histone H4 methyltransferase Set5 and discovered functional overlap with the histone H3 methyltransferase Set1 in gene expression. Specifically, using next-generation RNA sequencing (RNA-Seq), we found that Set5 and Set1 function synergistically to regulate specific transcriptional programs at subtelomeres and transposable elements [1]. Here we provide a comprehensive description of the methodology and analysis tools corresponding to the data deposited in NCBI's Gene Expression Omnibus (GEO) under the accession number GSE52086. This data complements the experimental methods described in Mas Martín G et al., 2014, and provides the means to explore the cooperative functions of histone H3 and H4 methyltransferases in the regulation of transcription. Furthermore, a fully annotated R code is included to enable researchers to use the following computational tools: comparison of significant differential expression (SDE) profiles; gene ontology enrichment of SDE; and enrichment of SDE relative to chromosomal features, such as centromeres, telomeres, and transposable elements. Overall, we present a bioinformatics platform that can be generally implemented for similar analyses with different datasets and in different organisms.
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Gene expression and differential transcription between WT and mutant cells were then determined using Cuffdiff. The Cuffdiff program, included in Cufflinks, was used to assess biases in read distribution across each transcript and to estimate the statistical significance of gene expression changes between samples.
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Gene expression and differential transcription between WT and mutant cells were then determined using Cuffdiff. The Cuffdiff program, included in Cufflinks, was used to assess biases in read distribution across each transcript and to estimate the statistical significance of gene expression changes between samples.
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The lists of significantly differentially expressed (SDE) genes were obtained using the getSig() function and are included as text files in the supplementary data. FPKM expression data is available through GEO and included in the supplementary files as ‘GSE52086_processed_data.txt’. In addition to the 0.05 q-value threshold from Cufflinks, our criteria for defining SDE genes included a fold-change threshold of > 1.7, to ensure that biologically relevant gene expression changes were considered for downstream analyses. Using these criteria, we identified a total of 42 SDE genes in set5∆ cells, 183 SDE genes in set1∆, and 250 SDE genes in set5∆ set1∆ cells. A very similar gene expression profile to set5∆ was observed for the SET5Y402A catalytic inactive mutant strain. The complete lists of SDE genes are included in the supplementary files ‘set1_sig_genes.txt’, ‘set5_sig_genes.txt’ and ‘set5set1_sig_genes.txt’.
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Transcriptome analysis of yeast methyltransferase mutants set5∆, set1∆ and set5∆set1∆
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Yeast strains used for RNA-Seq. YKO, Yeast Knockout collection from Open Biosystems.
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