A型流感病毒蛋白質序列特徵之搜尋並透過其在時間軸上之變化分析提出分群結果

Abstract

A型流感病毒除了可能於每年當中造成數次的季節性流感之外，每隔數十年便有機會產生全球性大規模流行的高感染性流感，例如2009年的H1N1新型流感便是屬於一種具高破壞性的流感病毒。因此，如何減緩流感病毒所帶來的傷害便是現今被高度關注的一項議題。由於A型流感病毒具有高度的病毒變異性，且可透過抗原微變以及抗原移型等方式進行突變，這些因素使得A型流感病毒難以進行預測以及分析。但是在過往的研究中指出，藉由分析一些可以區分物種的重要特徵位置(Signature)對於流感病毒的毒性分析以及疫苗製作上有著相當大的助益。 在我們的論文當中，我們首先從NCBI流感病毒資料庫中獲取所需的病毒資料，並且依不同的年代區段將資料進行分割，接著使用ARI的計算方法對各個區段中的資料進行重要特徵位置的搜尋，最後根據這些選出的重要特徵位置所對應的ARI值之變化情形對其進行分群。而在我們的分群結果中可以明顯看出各個群當中的ARI值折線圖是相當密合的，並且在部分的分群結果中透過相關文獻的佐證之下，我們發現這些位置能夠影響流感病毒的毒性或著具有類似的功能性。

Infuenza A viruses(IAV) caused epidemic several times a year, and sometimes a highly pathogenic IAV will cause a global outbreak of infection, for example the 2009 influenza A(H1N1) pandemic is one of the most destructive global pandemics in human history. Therefore, how to mitigating the severity of a new influenza pandemic is now a top global public health priority. In fact, the unique host range and two mechanisms for variation in viruses, antigenic drift and antigenic shift, makes the IAV is hard to predict. Identification of the host-associated genomic signatures is a cost-effective approach to analysis amount of viral sequences data for mitigating the illness and deaths. In this thesis, we first download the IAV sequences data from NCBI, and then we divide the virus data into several chronological groups. In the chronologically experiment, after identified the chronological signatures, we purpose a cluter alalysis of chronological signatures based on the ARI values over time. In our cluster results, the chronological signatures for each cluster shows highly correlated on the ARI values trend over time, and some signature sites, which in the same cluster, shows likely in the same functional domain or correlated with the pathogenic of IAV.