All bioinformatic analyses of array data were performed in the R language and environment for statistical computing (version 2.10.0) implemented on CentOS 5.1 on an IBM HS21 Linux cluster.
Library preparations of the fragments, sequencing reactions, and preliminary analysis of the data were performed at the Center for Translational Genomics and Bioinformatics, Hospital San Raffaele, Milano, Italy.
A bioinformatics analysis of these data was performed focusing on transcription factors, immunoglobulin superfamily members, and lineage-specific transcripts.
Bioinformatics analysis of microarray data was performed using DIA 10 and information from the freely available online databases KEGG and DAVID (v6.7).
Bioinformatics processing of 3seq data was performed as described previously, including adaptor trimming, read alignments, peak calling, peak filtering to remove internal polyA priming events, and transcript quantification with minor modifications (Wang et al., 2013).
Extensive bioinformatics analysis of microarray data was performed to (1) characterize differences in transcript profiles between mammary PAR and MFP, with the specific aim of uncovering predominant transcriptomic signatures, and (2) uncover predominant signaling molecules (e.g., cytokines and growth factors) in one tissue relative to the other.
Bioinformatics analysis of the microarray data was performed using Perseus (1.2.6.16) as part of the MaxQuant Software Package [ 21] and R on the free open source integrated development environment R Studio.
Because of the low data compression gained in the assembly step and the small difference between contigs and reads mean length, bioinformatic processing of the 454 sequence data was performed on whole sets of unassembled reads.
Clustering of the microarray data was performed using the bioinformatics software program Genesis [68].
All manipulation of the data was performed within the bioinformatic environment R (www.r-project.org), using modules from the Bioconductor Affy and LIMMA packages.
