Abstract
Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.
Highlights
In a living cell, rewiring of interactions among proteins, genes, and RNA molecules orchestrates how cells respond to external stimuli
One of the most fundamental regulatory relationships arise from transcription factors (TFs) that bound to the promoter of target genes (TGs) resulting in changing transcriptional dynamics
Our method produces gene regulatory network (GRN) that consists of multiple sub-networks where condition-specific interacting TFs regulate a set of TGs through intermediate genes
Summary
In a living cell, rewiring of interactions among proteins, genes, and RNA molecules orchestrates how cells respond to external stimuli. High-throughput experimental techniques, such as Chromatin Immunoprecipitation sequencing (ChIP-seq), have been widely utilized to construct GRNs detecting one-to-multiple relationships of TF and TGs (i.e., relations of a TF and the promoters of TGs where the TF binds to). Such experimental techniques are powerful but provide only partial snapshot of condition-specific GRN. It is necessary to develop computational methods for elucidating multiple-to-multiple relations of TFs and TGs in a specific condition. There have been several studies to identify multiple-to-multiple interactions. A study by Jolma et al (2015) tried to identify TF–TG regulations using a tailored experimental technique in a multiple-to-multiple fashion. Their work is still limited in identifying only 315 TF–TF interactions from ∼2,000 putative TFs
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