基於次世代定序資料偵測小沉默核醣核酸上的非範本附尾修飾的高效排比器

Abstract

小沉默核醣核酸（small silencing RNA）的3’端被加上非範本（non-templated）的核苷酸序列，稱為tailing，這些非範本序列已被證實會影響small silencing RNA本身的穩定性。利用次世代定序技術的高解析度特性，可以偵測到small silencing RNA是否有被加上非範本的核苷酸序列，但也伴隨著百萬計的序列資料。雖然目前已經發展出許多有效率的演算法以及分析工具能夠處理這些次世代定序後的巨量序列，但至今都還是無法有效地搜尋小核醣核酸的非範本核苷酸序列。 此篇論文，我們利用Burrows–Wheeler轉換（BWT）演算法實作出快速且精確的排比工具”Tailor”，其可搜尋定序後的small silencing RNA在轉錄前基因體中的位置，並且也支援偵測3’端的非範本序列，進而得知small silencing RNA的表現量以及非範本序列的偏好。論文中，我們利用模擬資料與真實資料測試Tailor的效能以及準確率，最後也展現Tailor在分析small silencing RNA的應用例子。

Non-templated nucleotide addition to the 3’ end of small silencing RNAs, namely tailing, has been proved to affect the stability of the RNA molecules. Next generation sequencing made it possible to detect tailing at nucleotide resolution. Although many effective algorithms and analysis tools have been developed to handle the massive amount of data, none of them can effectively identify the non-templated nucleotide of small silencing RNAs. In this work, we propose a fast and accurate alignment tool “Tailor” based on the Burrows-Wheeler transform (BWT) algorithm. Tailor is capable of aligning the small silencing RNAs to the genome and identifying the non-templates tails if exist. The expression level of the small silencing RNAs and the base preference of those non-templated nucleotides can be revealed. We further used simulated datasets to test the performance and accuracy of Tailor, and analyzed the real datasets of the small silencing RNAs with Tailor.