針對全基因體定序資料探討次世代基因定序 偵測拷貝數變異的方法

Abstract

拷貝數變異 (copy number variation, CNV) 一般指1kbp到數百萬bp之缺失或重複的基因變異，屬於亞微觀的基因組結構變異(submicroscopic structural variation)的一種，與罕見疾病或複雜疾病有高度相關。傳統上找尋CNV使用晶片全基因體定量分析術(array-CGH)，而隨著次世代定序(next generation sequencing, NGS)技術的進步，NGS近年來已被廣泛用於CNV的偵測。為有效率地分析NGS所產生的巨量且複雜的資料，新穎的電腦計算偵測CNV的方法亦需同步發展。本篇研究使用五種以read depth coverage為基礎發展的電腦計算軟體：cn.MOPS、CNVnator、JointSLM、RDXplorer、ReadDepth，分析來自UK10K計畫下健康歐洲人的全基因體定序(whole genome sequencing)資料，來偵測其中CNV之位置。論文中，我們分析流程、比較其步驟之同異、觀察其偵測的結果，並試著評比各種電腦計算方法，供使用者參考。

Copy Number Variations(CNV) are structural rearrangements of the genome like deletions, duplications, inversions, and translocations. Typically, CNVs are detected by experimental tools such as array CGH. With the advance of next generation sequencing (NGS) technology, NGS have been widely used in the detection of CNVs. To handle big data from NGS, new computational methods must be developed. In our research, we use five common read-depth based software to analyze whole genome sequencing data from the UK10K project. We make a process analysis, compare the differences and observe results. We also make a process pipeline for CNV detection in NGS data, which helps us to see how it works.