A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators.
BackgroundEstrogen is a chemical messenger that has an influence on many breast cancers as it helps cells to grow and divide. These cancers are often known as estrogen responsive cancers in which estrogen receptor occupies the surface of the cells. The successful treatment of breast cancers requires understanding gene expression, identifying of tumor markers, acquiring knowledge of cellular pathways, etc. In this paper we introduce our proposed triclustering algorithm δ-TRIMAX that aims to find genes that are coexpressed over subset of samples across a subset of time points. Here we introduce a novel mean-squared residue for such 3D dataset. Our proposed algorithm yields triclusters that have a mean-squared residue score below a threshold δ.ResultsWe have applied our algorithm on one simulated dataset and one real-life dataset. The real-life dataset is a time-series dataset in estrogen induced breast cancer cell line. To establish the biological significance of genes belonging to resultant triclusters we have performed gene ontology, KEGG pathway and transcription factor binding site enrichment analysis. Additionally, we represent each resultant tricluster by computing its eigengene and verify whether its eigengene is also differentially expressed at early, middle and late estrogen responsive stages. We also identified hub-genes for each resultant triclusters and verified whether the hub-genes are found to be associated with breast cancer. Through our analysis CCL2, CD47, NFIB, BRD4, HPGD, CSNK1E, NPC1L1, PTEN, PTPN2 and ADAM9 are identified as hub-genes which are already known to be associated with breast cancer. The other genes that have also been identified as hub-genes might be associated with breast cancer or estrogen responsive elements. The TFBS enrichment analysis also reveals that transcription factor POU2F1 binds to the promoter region of ESR1 that encodes estrogen receptor α. Transcription factor E2F1 binds to the promoter regions of coexpressed genes MCM7, ANAPC1 and WEE1.ConclusionsThus our integrative approach provides insights into breast cancer prognosis.
BackgroundExploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits.ResultsIn this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm δ-TRIMAX. Its aim is to make optimal use of δ-TRIMAX in extracting groups of co-expressed genes from time series gene expression data, or from any 3D gene expression dataset, by adding the powerful capabilities of an evolutionary algorithm to retrieve overlapping triclusters. We have compared the performance of our newly developed algorithm, EMOA- δ-TRIMAX, with that of other existing triclustering approaches using four artificial dataset and three real-life datasets. Moreover, we have analyzed the results of our algorithm on one of these real-life datasets monitoring the differentiation of human induced pluripotent stem cells (hiPSC) into mature cardiomyocytes. For each group of co-expressed genes belonging to one tricluster, we identified key genes by computing their membership values within the tricluster. It turned out that to a very high percentage, these key genes were significantly enriched in Gene Ontology categories or KEGG pathways that fitted very well to the biological context of cardiomyocytes differentiation.ConclusionsEMOA- δ-TRIMAX has proven instrumental in identifying groups of genes in transcriptomic data sets that represent the functional categories constituting the biological process under study. The executable file can be found at http://www.bioinf.med.uni-goettingen.de/fileadmin/download/EMOA-delta-TRIMAX.tar.gz.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0635-8) contains supplementary material, which is available to authorized users.
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