Computational protocol: Dynamic transcriptome changes during adipose tissue energy expenditure reveal critical roles for long noncoding RNA regulators

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Protocol publication

[…] The quality of RNA-seq reads was ascertained through the FASTQC tool http://www.bioinformatics.babraham.ac.uk/projects/fastqc. The average sequencing depth was 40.33 million reads per sample and the median per-base quality was >30 for all the samples. No further trimming of the bases was performed. Sequencing reads were then mapped to the mouse reference genome (mm10) using TopHat-2.0.9 alignment tool []. The mean mapping rate was 89.69%. Transcript/gene assembly and abundance estimation were performed using Cufflinks-2.1.1 [], resulting in the generation of counts, normalized for transcript-length and library size (FPKM). At this stage, 2 sets of FPKM results were generated with different normalization methods (different library size): the first one was normalized to the total number of reads using option—total-hits-norm in Cufflinks to reflect the absolute mRNA/lncRNA expression level ( and Data), which was used to assess the absolute abundance of mRNAs and lncRNAs; the second one was normalized to the number of reads mapped to previously annotated mRNAs/lncRNAs using option—compatible-hits-norm in Cufflinks (), which was used for differential expression, mRNA–lncRNA coexpression network, tissue specificity, and other analysis. Differential expression analysis for mRNAs and lncRNAs was performed using the Cuffdiff program within Cufflinks package. […]

Pipeline specifications

Software tools FastQC, TopHat, Cufflinks
Application RNA-seq analysis