Abstract
An important topic in computational biology is to identify transcriptional modules through sequence analysis and gene expression profiling. A transcriptional module is formed by a group of genes under control of one or several transcription factors (TFs) that bind to cis-regulatory elements in the promoter regions of those genes. In this paper, we develop an integrative approach, namely motif-guided sparse decomposition (mSD), to uncover transcriptional modules by combining motif information and gene expression data. The method exploits the interplay of co-expression and co-regulation to find regulated gene patterns guided by TF binding information. Specifically, a motif-guided clustering method is first developed to estimate transcription factor binding activities (TFBAs); sparse component analysis is then followed to further identify TFs’ target genes. The experimental results show that the mSD approach can successfully help uncover condition-specific transcriptional modules that may have important implications in endocrine therapy of breast cancer.
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Gong, T. et al. (2008). Sparse Decomposition of Gene Expression Data to Infer Transcriptional Modules Guided by Motif Information. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2008. Lecture Notes in Computer Science(), vol 4983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79450-9_23
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DOI: https://doi.org/10.1007/978-3-540-79450-9_23
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