由於定序技術不斷進步，現今在研究轉錄調控機制時，常使用染色體免疫沉澱定序(ChIP-seq)檢測轉錄因子在染色體上的結合位點，探索蛋白質與染色體的交互作用及調控關係。公開資源如GEO或是SRA等資料庫雖存放了大量的ChIP-seq實驗資料，但卻因缺乏系統性的整理與分析導致這些資料無法被有效率地使用。許多研究團隊在近幾年開發了ChIP-seq相關的資料庫提供更便利地資料檢索及下游分析功能，但針對植物物種設計的ChIP-seq資料庫卻非常稀少，相比動物資料庫分析功能也匱乏許多。為解決上述問題，本實驗室先前研究中曾開發一個名為PCBase (Plant ChIP-seq Database)的植物ChIP-seq資料庫，提供使用者查找植物的蛋白質與DNA的調控關係，但PCBase的資料整合及資料視覺化方面的功能仍不夠完善。因此本研究改善舊有PCBase的缺點，將之升級為PCBase 2.0資料庫，其涵蓋了七個高等植物與一個藻類物種，收錄了205種不同的轉錄調控蛋白，合計共1,982筆的ChIP-seq資料，並透過重新設計資料庫介面及功能來改善資料庫功能及其資料可理解性。在此更新版本中，我們引入標籤化整理系統，藉由將所有ChIP-seq資料的實驗組織及處理條件標籤化，提升資料庫的資料檢索效率並實現整合分析的功能。除此之外，藉由資料標籤索引優化了ChIP-seq的分析結果，PCBase 2.0針對基因及蛋白質的調控功能分析新增了 (1)各條件處理/組織下蛋白質在目標基因上的結合區域偏好分析、(2)調控因子結合熱點動態顯示工具、(3)各處理條件下調控蛋白之標靶基因富集功能分析。另外PCBase 2.0使用MEME及STRME分析轉錄因子的特徵序列，並將所獲得之轉錄因子結合位置權重矩陣(position weight matrices)用於掃描任意序列上潛在的轉錄因子結合區域。藉由改善先前資料庫分析系統的不足，我們希望PCBase 2.0能夠提供使用者一個更加友善的ChIP-seq資料庫平台，幫助研究者們能夠更便利地使用實驗驗證之大數據分析結果來研究植物細胞內的轉錄調控關係。

Chromatin Immunoprecipitation sequencing (ChIP-seq) is a powerful technique used to study the interactions between proteins and DNA throughout the entire genome. Public repositories such as GEO and SRA contain a vast amount of ChIP-seq data, but a significant portion of it is remains unorganized or unanalyzed. This poses a challenge for researchers who want to obtain insights into gene regulation. While some ChIP-seq databases were developed from previous studies, there is still a shortage of plant-specific databases for ChIP-seq data mining and analysis. In this study, a database named PCBase 2.0 (http://PCBase.itps.ncku.edu.tw) has been established to identify the transcriptional relationships of 205 different transcription regulators from 1,982 ChIP-seq samples across seven land plant species and one green alga. All ChIP-seq data were manually curated and systematically analyzed. To improve the understanding and usability of ChIP-seq data, a tagging system was used to classify the disorganized ChIP-seq samples into corresponding tissue/treatment clusters. This helped improve the following integrated analyses: (i) searchable gene-centered interfaces revealing the binding preferences of genetic regions based on experimental condition clusters, (ii) an in-house developed peak-visualization function illustrating all regulatory proteins occupancy regions dynamically, and (iii) significantly enriched functional annotations of the regulator target genes in different conditions. Moreover, the de novo motif discovery of each transcription factor was performed by MEME and STREME algorithms. The position weight matrices were further used to scan the potential transcription factor binding sites on any promoter sequence of interest. As stated above, we believe that PCBase 2.0 represents a user-friendly platform to investigate transcriptional regulatory relationships based on experimental ChIP-seq data.

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