次世代定序(Next-Generation-Sequencing)技術的高通量定序能力，使得DNA定序達到全基因體深度定序的等級，也大幅降低了定序所需的人力與成本，但大量的序列資料也成為電腦計算上的一項新課題。近年來發展了多種次世代序列的組裝軟體，已廣泛地用於處理次世代定序的大量短序列資料，但定序的實驗過程並非完美，除了定序錯誤之外，DNA序列並未隨機取自基因體序列，而是與序列上的鳥糞嘌呤(Guanine, G）以及胞嘧啶(Cytosine, C)的數量有關，此一定序偏差(sequencing bias)將造成資料的不均勻分布，並直接影響到基因體組裝(genome assembly)的結果。
　　為了探討定序偏差對於全基因體組裝的影響，本文以金黃色葡萄球菌、大腸桿菌、結核桿菌、阿拉伯芥一號染色體與水稻五號染色體共五種模式物種(Model Species)的參考序列(Reference Sequence)，模擬出片段長度100個核苷酸的序列資料並加入不同程度的定序偏差，進而以組裝軟體ALLPATHS-LG、ABySS、Edena、SOAPdenovo、SSAKE、Velvet與Velvet-SC處理之，經由組裝結果與參考序列的比對，以連續性與正確度兩項標準評估定序偏差對於不同組裝軟體的影響程度。
　　研究結果指出大部分的組裝軟體雖然可處理低程度的定序偏差，然而定序偏差一旦增大將造成組裝的連續性與正確性降低，每一個軟體對於定序偏差的影響不但有程度上的不同，所傾向之錯誤亦有差異。定序偏差之主要影響可歸咎於區域地序列深度(read coverage)不足，故適度的定序深度將扮演一個重要的角色，此外，基因體中區域GC含量(GC content)之變異量亦為影響偏差的主因之一。

　The next generation sequencing technology is a now important approach to decode the genome. Dealing with the millions of short reads had become a significant issue in the field of computing. In the recent years, a series of tools had been developed to assembly the huge amount of fragments into more continuous sequences. However, the inherent sequencing bias may reduce the performance of assembly. The effects of bias on assembly have not been systematically discussed in the past.
　In this study, we simulate reads with specific degree of sequencing bias and error rate profile for S.aureus, E.coli, M.tuberculosis, Arabidopsis thaliana Chr.1 and Oryza sativa Chr.5. We consider various scenario of bias for each assembler including ALLPATHS-LG, ABySS, Edena, SOAPdenovo, SSAKE, Velvet and Velvet-SC and employ an assembly evaluating tool, GAGE, to discuss the assemblies by both N50 length and accuracy.
　The biased data sets will lead the fracture and error within assemblies. The regions with low read coverage are either unable to be assembled or produce the sequence contain SNPs, Indels or reconstructions. Although the most assemblers are capable to deal with small degree of bias within bacterial data, the bias result much deeper impact for the more complex plant genome. The reasonable amount of reads plays an important role to mitigate the bias. This study provides a novel landscape of assembly for the relationship between the coverage and sequencing bias.

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