Computational protocol: Essential Nonredundant Function of the Catalytic Activity of Histone Deacetylase 2 in Mouse Development

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Protocol publication

[…] For transcriptome sequencing (RNA-seq) experiments, RNA was subjected to poly(A) selection with a Dynabeads mRNA purification kit (Invitrogen), followed by reverse transcription using a NEB RNA Ultra kit and library generation using a TruSeq library generation kit (Illumina). We performed full-length mRNA-seq experiments in three biological replicates for Hdac2KI/+ and the corresponding wild-type brains and applied the “union” model of the htseq-count script () to calculate the number of reads associated with each of the 21,608 mouse RefSeq genes for each sample. We used these counts to compute reads per kilobase per million (RPKM) values for each gene and determined Spearman's correlation coefficient (ρ) for each set of biological replicates. Based on the high correlation of the replicates (ρ = 0.99 between each 2 of the 3 wild-type brains and between each 2 of the 3 Hdac2KI/+ brains), we used the log-transformed means of RPKM values under each condition to plot the distribution of gene expression levels by using kernel density estimation. Based on this distribution, we set the threshold for gene expression to 1 RPKM (log2 RPKM value equal to zero). This is consistent with data from previous studies, which estimated that the value of 1 RPKM corresponds to 1 transcript per cell (). The analysis of differentially expressed genes across the two conditions was performed by using htseq-count and the Bioconductor edgeR package (, ). Genes that showed a minimum of a 2-fold change in expression levels (adjusted P value of ≤0.05) were classified as upregulated, whereas genes displaying a fold change of ≤0.5 (adjusted P value of ≤0.05) were categorized as downregulated. […]

Pipeline specifications

Software tools HTSeq, edgeR
Application RNA-seq analysis
Organisms Mus musculus