BACKGROUND: Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. RESULTS: The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. CONCLUSIONS: IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

MOTIVATION: Many studies have shown that epigenetic changes, such as altered DNA methylation and histone modifications, are linked to estrogen receptor α (ERα)-positive tumors and disease prognoses. Several recent studies have applied high-throughput technologies such as ChIP-seq and MBD-seq to interrogate the altered architectures of ERα regulation in tamoxifen (Tam)-resistant breast cancer cells. However, the details of combinatorial epigenetic regulation of ERα target genes in breast cancers with acquired Tam resistance have not yet been fully examined. RESULTS: We developed a computational approach to identify and analyze epigenetic patterns associated with Tam resistance in the MCF7-T cell line as opposed to the Tam-sensitive MCF7 cell line, with the goal of understanding the underlying mechanisms of epigenetic regulatory influence on resistance to Tam treatment in breast cancer. In this study, we used ChIP-seq of ERα, RNA polymerase II, three histone modifications and MBD-seq data of DNA methylation in MCF7 and MCF7-T cells to train hidden Markov models (HMMs). We applied the Bayesian information criterion to determine that a 20-state HMM was best, which was reduced to a 14-state HMM with a Bayesian information criterion score of 1.21291 × 10(7). We further identified four classes of biologically meaningful states in this breast cancer cell model system, and a set of ERα combinatorial epigenetic regulated target genes. The correlated gene expression level and gene ontology analyses showed that different gene ontology terms were enriched with Tam-resistant versus sensitive breast cancer cells. Our study illustrates the applicability of HMM-based analysis of genome-wide high-throughput genomic data to study epigenetic influences on E2/ERα regulation in breast cancer.

UNLABELLED: ChIP-based technology is becoming the leading technology to globally profile thousands of transcription factors and elucidate the transcriptional regulation mechanisms in living cells. It has evolved rapidly in recent years, from hybridization with spotted or tiling microarray (ChIP-chip), to pair-end tag sequencing (ChIP-PET), to current massively parallel sequencing (ChIP-seq). Although there are many tools available for identifying binding sites (peaks) for ChIP-chip and ChIP-seq, few of them are available as easy-accessible online web tools for processing both ChIP-chip and ChIP-seq data for the ChIP-based user community. As such, we have developed a comprehensive web application tool for processing ChIP-chip and ChIP-seq data. Our web tool W-ChIPeaks employed a probe-based (or bin-based) enrichment threshold to define peaks and applied statistical methods to control false discovery rate for identified peaks. The web tool includes two different web interfaces: PELT for ChIP-chip, BELT for ChIP-seq, where both were tested on previously published experimental data. The novel features of our tool include a comprehensive output for identified peaks with GFF, BED, bedGraph and .wig formats, annotated genes to which these peaks are related, a graphical interpretation and visualization of the results via a user-friendly web interface. AVAILABILITY: http://motif.bmi.ohio-state.edu/W-ChIPeaks/.