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The eQTLs were determined using the Bioconductor program (27) GGtools 3.0 written by Vince Carey.
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Data quality checks were performed using the Bioconductor Lumi package for R statistical programming environment.
All statistical analyses for network constructions were performed in the R statistical programming environment using the Bioconductor open source package [ 51, 52].
Data processing and identification of differentially expressed genes was carried out in the R environment for statistical computing and programming [ 64] using the Bioconductor package bundle [ 65], Limma [ 66], aroma package [ 67] and the kth-package [ 68].
All these analyses were performed using the Bioconductor Package run in the R statistical programming environment [ 42].
Gene expression data were analyzed using the BioConductor package 'LIMMA' (version 3.6.1) in the R statistical programming environment (version 2.12.0).
The OTU-table created by qiime after denoising and chimera checking was imported into the statistical programming language R [ 18] using the Bioconductor [ 19] package phyloseq [ 20].
Affymetrix chip CEL files were imported into the statistical programming language R and analyzed using the Bioconductor package "affy" (Gentlemen et al. 2005).
The resulting mapping files (bam) were imported into the statistical program R (R Development Core Team 2006) using the Bioconductor package Rsamtools [ 134].
Official gene symbols for probe sets were obtained using the Bioconductor annotation database mouse4302.db.
All subsequent analysis was performed using the Bioconductor software environment.
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