建立植物轉錄調控網路在系統生物學中已經成為一個重要課題。隨著生物晶片及轉錄因子結合位置(TFBSs)資料的增加，建立轉錄調控網路變成是刻不容緩的工作。為了這個目標，這個研究建立了一個資料庫支援系統－AtPAN (Arabidopsis thaliana Promoter Analysis Net)，這個系統可以預測一個或多個阿拉伯芥基因啟動子上的TFBSs以及其相對應的轉錄因子(TFs)。根據生物晶片表現的資料和文獻，AtPAN可以進一步分析目標基因及與其共同表現的轉錄因子(TFs)。與這些轉錄因子有相互作用的蛋白質也被整合來重建基因轉錄調控網路。此外，亦可分析一群基因啟動子上所具有之共同的組合調控轉錄因子(combinatorial TFs)。另外，AtPAN提供了高可信度的轉錄因子，這些轉錄因子結合在兩同源基因啟動子保留區內。然而，在AtPAN中並非所有基因都具有實驗驗證的轉錄起始位置(TSSs)去定義有功能的啟動子。因此，這個研究應用了向量支持器 (SVM) 演算法來找出高可信度的轉錄起始位置。組蛋白三上第四號賴胺酸三甲基化(H3K4me3), 組蛋白三上第九號賴胺酸乙酰基化(H3K9ac), 轉錄起始位置標籤(TSS tags),核小體佔用率(nucleosome occupancy) 等次世代定序 (NGSs)資料，及在轉錄起始位置附近富集的模體(motifs)相關資訊都被整合來建立轉錄起始位置預測模型。這個預測模型的精確度、敏感度、專一性及正確性分別為91.27%, 90.67%, 91.33%, 91.00%。藉由應用這個預測模型，可找出每個阿拉伯芥基因的高可信度的轉錄起始位置。總歸之，AtPAN不只成功的利用高通量資料去找出高可信度的TSSs，同時也建立了易操作的網頁介面重建阿拉伯芥的基因轉錄調控網路。這個資源目前可在http://AtPAN.itps.ncku.edu.tw/免費使用。

Construction of transcriptional regulatory networks (TRNs) in plants has received considerable attention in systems biology. With increasing availability of high-throughput data and transcription factor binding profiles, reconstruction of TRNs becomes an urgent task. For this purpose, this work develops a database-assisted system, AtPAN (Arabidopsis thaliana Promoter Analysis Net), capable of detecting transcription factor binding sites (TFBSs) and their corresponding transcription factors (TFs) in a promoter or a set of promoters in Arabidopsis. For further analysis, according to the microarray expression data and literature, the co-expressed TFs and their target genes can be retrieved from AtPAN. Proteins interacting with the co-expressed TFs were also incorporated to reconstruct TRNs. Furthermore, combinatorial TFs can be identified in a group of gene promoters. Additionally, high-confident TFs located in conserved regions between homologous genes are also provided in AtPAN. However, not all the genes in AtPAN have their experimentally verified transcription start sites (TSSs) to define a functional promoter. Therefore, a Support Vector Machine (SVM) algorithm was applied to identify high-confident TSSs in this study. With several next-generation sequencing (NGS) datasets of H3K4me3, H3K9ac, TSS tags and nucleosome occupancy, information of enriched motifs around known TSSs is also incorporated to construct a TSSs prediction model. The performance of the model in this study is 91.27%, 90.67%, 91.33%, 91.00% for precision, sensitivity, specificity and accuracy, respectively. Following implementation of this prediction model, high-confident TSSs were representative for each Arabidopsis genes. In conclusion, this work not only successfully identify high-confident TSSs based on high-through sequencing data, but also established a user-friendly web interface for users to investigate the mechanism of gene regulation. The resource is now freely available at http://AtPAN.itps.ncku.edu.tw/.

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