請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/92364
標題: 利用基因表現量及轉錄因子資料預測基因在特定表徵下的調控關係
Construction of phenotype-specific regulatory programs using gene expression and transcription factor binding data
作者: Te-Wei Tzeng
關鍵字: gene expression
transcription factors
ChIP-seq experiments
ChIP-seq 數據
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Potential etiologic and functional implications of genome‐ wide association loci for human diseases and traits. Proceedings of the National Academy of Sciences of the United States of America 106(23): 9362‐9367. [33] David T.W. Tzeng†, Yu-Ting Tseng†, Matthew Ung, I-En Liao, Chun-Chi Liu*, Chao Cheng*. (2014) DPRP: A database of phenotype-specific regulatory programs derived from transcription factor binding data. Nucleic Acids Research 42: D178-183 (†co-first authors, *corresponding authors)
摘要: In the past decades, gene expression profiling has been widely used to result in an enormous amount of expression data available in the public database, in which the data sets are informative in elucidating transcriptional regulation of genes underlying various biological and clinical conditions. However, it is difficult to find the responsible transcription factors (TFs) which regulate the gene expression. With the technical advances in recent years, it facilitates systematically determining the target genes of TFs by ChIP-seq experiments. To support biologists discovering the regulatory programs underlying gene expression profiles, we build up a database of phenotype-specific regulatory programs (DPRP) derived from the integrative analysis of TF binding data and gene expression data. DPRP provides three methods: The Fisher's Exact Test, the Kolmogorov-Smirnov test and the BASE (Binding Association with Sorted Expression) algorithm to facilitate the integration of gene expression and ChIP-seq data for generating new hypotheses on transcriptional regulatory programs in biological and clinical studies.
在這十幾年來,許多研究者建立了不同型態的大規模基因表達資料庫。這些資料庫的數據對於闡述基因在不同病徵和各種生物型態下的轉錄作用有很大的幫助。然而,許多調控關係都是在後轉錄修飾中發生,因此要從基因表達資料去推論導致疾病的關鍵轉錄因子的轉錄調控網路有很高的難度。為了克服這個難題,我們將整合大規模多樣性的基因表達資料和 ChIP-seq/ChIP-chip 數據來建立一個探索疾病關鍵轉錄因子的調控網路 (Database of Phenotype-specific RegulatoryPrograms, DPRP)。 在 DPRP 網站,使用者可以上傳藥物處理和疾病相關的基因表達數據,我們實作了三種演算法來幫助使用者作線上分析 如此可以探討該數據的轉錄調控網路。,這三種演算法分別是: The Fisher's Exact Test, the Kolmogorov-Smirnov test and theBASE (Binding Association with Sorted Expression) 演算法。 雖然我們已經蒐集了大規模的 ENCODE 的轉錄因子數據以及 NCBI GEO 的基因表達數據,但我們預期在接下來幾年會有更多 ChIP-seq 數據出現,而我們也將會持續維護並更新這個網站來確保 DPRP 可以具備完整的轉錄因子數據,這將為使用者提供一個在生物學以及臨床醫學上一個功能強大的分析工具。
URI: http://hdl.handle.net/11455/92364
其他識別: U0005-3105201509492000
文章公開時間: 2018-07-15

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