Computational protocol: Long Non-Coding RNAs Associated with Metabolic Traits in Human White Adipose Tissue

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Protocol publication

[…] Total RNA was extracted from samples of cohort 1 using the RNeasy Lipid Tissue Mini Kit (74,804, Qiagen, Hilden, Germany) as per manufacturer's instructions. RNA concentration and purity were measured using a using Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Lafayette, USA). The quality of the extracted RNA samples was investigated using the Agilent Bioanalyzer (Agilent, Santa Clara, CA) and all RNA samples submitted for sequencing had an RNA Integrity Number (RIN) above 8. RNA libraries for sequencing were prepared using TruSeq RNA kits (Illumina, CA, USA) according to the manufacturer's instructions with the following changes. The protocols were automated using an MBS 1200 pipetting station (Nordiag AB, Sweden). All purification steps and gel-cuts were replaced by the magnetic bead clean-up methods as previously described (). The samples were sequenced on an Illumina HiSeq 2000 as paired-end reads to 100 bp.To obtain the abundance estimation for each gene, the transcript abundance quantification was first conducted using the ultrafast quasi-alignment tool Kallisto (), which pseudo-aligned the sequencing reads from each sample to the assembly based on the FANTOM-CAT. Then the gene-level estimates, representing the overall transcriptional output of each gene, were obtained by summing the corresponding transcript-level estimates using a Bioconductor package, Tximport (). Differential gene expression analysis was conducted at the gene level using EdgeR, after applying the filter for at least half of the samples above the detection level (cpm > 1), selecting for genes with a false discovery rate (FDR) of <0.05 (). Identified genes were interrogated for their functional classes and importance in biology using the pathway analysis with the Ingenuity Pathway Analysis tool (IPA, Ingenuity Systems, Inc., Redwood City, CA, USA). Canonical pathways with a p-value (corrected using the Benjamini-Hochberg method) <0.01 (expected FDR < 1%) were significantly enriched for differentially expressed genes. [...] Weighted gene co-expression network analysis (WGCNA) was performed on the 17,000 filtered genes from the RNA-seq data of obese and lean subjects using a R package (). The automatic one-step network construction and module detection method with default settings were used, which include an unsigned type of topological overlap matrix (TOM), a power β of 6, a minimal module size of 30, and a branch merge cut height of 0.25. All modules were represented by a colour. The module eigengene was used to represent each module, which was calculated by the first principal component. Using the module eigengene, the Module-Trait relationships were estimated by calculating the Pearson's correlations between the module eigengene and the clinical traits included in the analysis. Those Module-Trait relationships were used to select potential biologically interesting modules for downstream analysis. [...] Gene expression profiling in cohort 2 was performed using GeneChip® Human Transcriptome Array (HTA)-2.0 and has been published previously (GSE101492) (). For the current study, the raw data were analyzed with packages available from Bioconductor (http://www.bioconductor.org). Normalization and calculation of gene expression were performed with the robust multichip average expression measure using the oligo package (). Before further analysis, collapseRows R function () was used to convert and collapse the transcript abundance quantification detected by Affymetrix probesets to mapped FANTOM-CAT IDs. Differential gene expression analysis was conducted at the gene level using Limma, selecting for genes with a FDR < 0.05 (). […]

Pipeline specifications

Software tools kallisto, tximport, edgeR, IPA, WGCNA
Databases FANTOM
Application RNA-seq analysis
Organisms Homo sapiens
Diseases Neoplasms, Adipose Tissue