大數據於基因體學研究之應用

The Applications of Big Data Analysis in Genomic Research

盧子彬(Tzu-Pin Lu) ； 李建樂(Chien-Yueh Lee) ； 張耀尹(Yao-Yin Chang) ； 謝嘉珊(Chia-Shan Hsieh) ； 邱鈺喬(Yu-Chiao Chiu) ； 賴亮全(Liang-Chuan Lai) ； 蔡孟勳(Mong-Hsun Tsai) ； 莊曜宇(Eric Y. Chuang)

《台灣醫學》 20卷6期 (2016/11) Pp. 609-619

https://doi.org/10.6320/FJM.2016.20(6).7

摘要

近二十年來隨著高通量基因體實驗技術快速發展，取得每個人的基因資訊所需之時間與經費已大幅下降，因而基因資訊已成為當今生物醫學研究及臨床照護上必須考量的重要因素。回顧基因體實驗術的發展歷史，微陣列及次世代定序技術為最重要的兩個標竿，因此，本文將先就實驗技術的基本原理進行介紹，進而說明在面對如此龐大的大數據基因體資料時可能遭遇的問題及分析方式，最終透過實際的研究案例說明大數據基因體資料的重要性及未來之應用方向。

關鍵字

基因體資料 ； 微陣列 ； 次世代定序技術

並列摘要

With the advancement of high-throughput genomic technologies in the past two decades, the time consumption and cost for obtaining the genetic information of each individual has dropped substantially. Therefore, the genetic information has become an important feature that must be considered in both biomedical studies and clinical care. Among the genomic technologies, microarray and next-generation sequencing (NGS) are the two most important ones. In this article, we introduce the basic concepts of the experimental procedures of microarray and NGS first. Subsequently, we focus on the challenges of handling big data generated from genomic analysis, and discuss the possible solutions of different analysis pipelines. Lastly, the importance and potential applications of the high-throughput genomic data are demonstrated by several studies.

並列關鍵字

high-throughput genomic data ； microarray ； next-generation sequencing (NGS)

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