With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective mining of the information in the large-scale datasets that are obtained from gene expression microarrays, deep sequencing experiments or metabolic profiling is essential to uncover and then effectively target the critical regulators of diseased cell phenotypes. Estrogen Receptor α (ERα) is one of the master transcription factors regulating the gene programs that are important for estrogen responsive breast cancers. In order to understand to role of ERα signaling in breast cancer metabolism we utilized transcriptomic, cistromic and metabolomic data from MCF-7 cells treated with estradiol. In this report we described generation of samples for RNA-Seq, ChIP-Seq and metabolomics experiments and the integrative computational analysis of the obtained data. This approach is useful in delineating novel molecular mechanisms and gene regulatory circuits that are regulated by a particular transcription factor which impacts metabolism of normal or diseased cells.