運用高通量全外顯子定序偵測複製數變異工具之評估與整合

複製數變異 ( Copy Number Variations，CNVs ) 是一種其長度定義為大於50核苷酸，且在DNA序列上發生多重複製( Amplifications )、刪除 ( Deletions )、易位 ( Translocations )、嵌入 ( Insertions ) 的基因組變異。初期研究指出CNVs與正常人之神經系統功能、細胞生長調節及調節新陳代謝有關，並與多項疾病有所關聯，包括自閉症、精神分裂症、肥胖等，近年來相關研究亦指出CNVs與癌症有著密不可分的關係。目前已知超過半數之CNVs區段與蛋白質編碼區 (protein-coding region) 重疊，而全外顯子定序 ( whole-exome sequencing，WES ) 是針對蛋白質編碼區進行定序，具備成本低、準確度高且運算資源少等特性，因此WES已成為臨床應用不可或缺之工具。CNVs偵測準確性對於臨床診斷與預後評估具有重要的影響。目前已有許多工具致力於偵測WES資料中之CNVs，然而不同偵測策略皆有其不同之偵測極限 ( limitation )，且目前尚未有單一一套偵測工具能偵測出所有類型之CNVs，也尚未有任一平台能提供各種偵測工具之整合分析。本研究針對上述兩點，利用TCGA (The Cancer Genome Atlas) WES 資料評估多項可偵測Somatic Copy Number Variations ( somatic CNVs, SCNVs ) 之工具，並利用Virtual Machine ( VM ) 架構各種偵測SCNV工具之整合分析平台。經評估後發現ExomeCNV及VEGAWES偵測SCNVs準確率較高，EXCAVATOR可偵測較長片段之SCNVs，VarScan2需要較多時間進行CNVs偵測。本研究將評估後之結果彙整而成表格，可方便使用者尋求是用於自身實驗設計之偵測工具並透過一簡單指令即可使用本研究建立之分析平台偵測somatic CNVs。

關鍵字

全外顯子定序；複製數變異；複製數變異偵測工具

並列摘要

Copy Number Variations (CNVs) are a form of structural variation that manifest as amplifications, deletions, translocations, and insertions in the genome with segment size larger than 50 bp. Previous studies have reported that CNVs are associated with biological functions of nervous system, cellular development and metabolism in healthy people while also have relationships with diseases such as autism, schizophrenia and obesity. Recent related studies have also uncovered additional important role of CNVs in cancers. With the decreasing costs and high accuracy of next-generation sequencing, whole-exome sequencing ( WES ) has become a dominant method for identifying CNVs in both research and clinical settings. Since the accurate identification of CNVs may affect successful clinical diagnosis and prognosis, substantial efforts have been devoted to develop tools for detecting CNVs for WES, but these tools have their own limitation. However, no single method can achieve the complete detection of all kinds of CNV events. Accordingly, we tried to evaluate as many detection tools as possible by using WES data obtained from TCGA (The Cancer Genome Atlas) GChub, to achieve a fully consideration and evaluation of existing somatic copy number variations ( somatic CNVs, SCNVs ) detection tools. Furthermore, we also constructed and integrated platform for CNVs detection in VM. After evaluation, the study found that ExomeCNV and VEGAWES could have higher accuracy for detecting CNVs; EXCAVATOR could have preference for large CNVs; VarScan2 could need more time to execute CNVs detecting. The study also made a table to summarize all result of evaluation and the table will be convenient for users to find tools which could be fitted their own experimental design. Finally, users can use a simple command line to execute analysis pipeline made by the study to detect CNVs.

並列關鍵字

whole-exome sequencing ； copy number variation ； CNV tools

參考文獻

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運用高通量全外顯子定序偵測複製數變異工具之評估與整合

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