研發生物資訊軟體以整合分析新興病毒的演變與流行趨勢

Abstract

新興呼吸道病毒是公共衛生重要議題，它們的高突變率更突顯了監測病毒全基因體序列的必要性，現今許多線上分析工具並不適用於全基因體序列，本論文研究將從克服這些限制為目標開發新的軟體工具，深入分析了禽流感和新冠病毒。 關於禽流感全基因體序列分析部分，先以「流感病毒序列轉換」(FluConvert) 自動處理原始序列數據，並按照病毒命名法 (ABCD 類型/宿主/區域/菌株/年份/HxNy亞型) 重新排列病毒片段，序列對齊後轉譯為胺基酸序列。隨後「流感病毒序列溯源」(IniFlu) 軟體，彙整了這些具有顯著特徵的胺基酸序列，並根據研究目標分群，檢視不同分群中重要的病毒共有序列。分析結果獲得了除了HA 還有其他 10 種病毒蛋白中共有247 個與H5N2的高致病性具有相關的胺基酸點位變異，大部分的變異點位尚未被報導。在這套創新的軟體和方法的基礎上，我們繼續分析了2021 年 4 月至 9 月間台灣爆發新冠肺炎流行的Alpha 變種病毒株，從病毒基因指紋釐清不同的傳播鏈以及出現和防控主要流行病毒株的流行病學條件。以上二個研究成果說明了本基因序列分析軟體可以成功快速地分析不同病毒株全基因體，同時識別這些多基因共有特徵以進行綜合研究。 總之，這項研究為全面的病毒全基因序列分析提供了一站式平台，可以同時分析整個病毒全基因體，並輕鬆與其他重要資訊整合，以取得具有獨特特徵之病毒序列，未來仍需要努力建立實際實驗證據來驗證分析。然而本研究中所研發軟體分析甚至將此分析法應用於其他快速傳播、具致病力及有全球流行潛力病原，即早偵測具健康威脅新興病毒，找出演變關鍵，協助科學研究進展與成功防控。

Respiratory viruses with high mutation rates have become a significant public health concern, highlighting the need for monitoring complete viral sequences. While online sequence analysis tools exist, they cannot often analyze the entire genomic sequence, creating a gap that requires developing new software tools. This dissertation analyzes two emerging viruses, avian influenza viruses, and SARS-CoV-2. In the first part of the research work, I developed the analysis software packages to analyze whole AIV genome sequences comprehensively. The FluConvert software automatically processes raw sequence data, organizing viral segments based on virus nomenclature (ABCD Type/Host/Region/Strain/Year/HxNy Subtype) and aligning distinct genes, and translating them into protein sequences. Subsequently, the IniFlu software integrates protein sequences with significant characteristics, allowing for classification based on study objectives and examination of consensus sequences in different subgroups. This innovative approach has led to identifying 247 polygenic consensus signatures associated with highly pathogenic AIV (HPAIV) across HA and ten other proteins, most of which have not been reported in the literature. Our pioneering software and methods enable rapid analysis of diverse strains’ genomes while identifying polygenic consensus signatures for integrated investigations. The second part of the study focused on understanding the dynamic changes of SARS-CoV-2 Alpha variant strains in responses to various control measures during the outbreak in Taiwan from late April to September 2021. The goal was to delineate the epidemiological circumstances that allowed these strains to become predominant. The findings provided valuable insights into the emergence and control of a dominant viral strain during an outbreak. In conclusion, the study offered an integrated platform for comprehensive viral genome sequence analysis. It allows for simultaneous estimation of the complete viral genome while easily integrating other significant information to extract characteristics-specific viral sequences. Future experimental validation is required to support the analysis. Applying this integrated analysis method to other pathogens with rapid spread, high pathogenicity, and pandemic potential will provide insightful information for the early detection of emerging or health-threatening dominant viruses. Results from the study will contribute to scientific progress and early disease prevention and control success.