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dc.contributor.authorLee, Tzong-Yien_US
dc.contributor.authorChang, Wen-Chien_US
dc.contributor.authorHsu, Justin Bo-Kaien_US
dc.contributor.authorChang, Tzu-Haoen_US
dc.contributor.authorShien, Dray-Mingen_US
dc.date.accessioned2014-12-08T15:22:54Z-
dc.date.available2014-12-08T15:22:54Z-
dc.date.issued2012-01-17en_US
dc.identifier.issn1471-2164en_US
dc.identifier.urihttp://dx.doi.org/S3en_US
dc.identifier.urihttp://hdl.handle.net/11536/16152-
dc.description.abstractBackground: Sequence features in promoter regions are involved in regulating gene transcription initiation. Although numerous computational methods have been developed for predicting transcriptional start sites (TSSs) or transcription factor (TF) binding sites (TFBSs), they lack annotations for do not consider some important regulatory features such as CpG islands, tandem repeats, the TATA box, CCAAT box, GC box, over-represented oligonucleotides, DNA stability, and GC content. Additionally, the combinatorial interaction of TFs regulates the gene group that is associated with same expression pattern. To investigate gene transcriptional regulation, an integrated system that annotates regulatory features in a promoter sequence and detects co-regulation of TFs in a group of genes is needed. Results: This work identifies TSSs and regulatory features in a promoter sequence, and recognizes co-occurrence of cis-regulatory elements in co-expressed genes using a novel system. Three well-known TSS prediction tools are incorporated with orthologous conserved features, such as CpG islands, nucleotide composition, over-represented hexamer nucleotides, and DNA stability, to construct the novel Gene Promoter Miner (GPMiner) using a support vector machine (SVM). According to five-fold cross-validation results, the predictive sensitivity and specificity are both roughly 80%. The proposed system allows users to input a group of gene names/symbols, enabling the co-occurrence of TFBSs to be determined. Additionally, an input sequence can also be analyzed for homogeneity of experimental mammalian promoter sequences, and conserved regulatory features between homologous promoters can be observed through cross-species analysis. After identifying promoter regions, regulatory features are visualized graphically to facilitate gene promoter observations. Conclusions: The GPMiner, which has a user-friendly input/output interface, has numerous benefits in analyzing human and mouse promoters. The proposed system is freely available at http://GPMiner.mbc.nctu.edu.tw/.en_US
dc.language.isoen_USen_US
dc.titleGPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene groupen_US
dc.typeArticleen_US
dc.identifier.doiS3en_US
dc.identifier.journalBMC GENOMICSen_US
dc.citation.volume13en_US
dc.citation.issueen_US
dc.citation.epageen_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000303923300004-
dc.citation.woscount1-
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