隨著生物資訊的蓬勃發展，生物學家常運用蛋白質序列來進行分析研究，常見的應用有未知序列的功能預測（Predict）、註解（Annotate）等方面，在進行這些研究時，最常需要的前置作業就是「序列分群」。序列在經過分群的動作後，可將具有相同功能的序列區分於同一群集裡，因此可利用群集的特性來做後續的研究發展。目前的序列分群大多是利用序列彼此之間的相似度來進行分群，但已經有學者認為單純這樣的作法是不足的，而且會造成一定程度的誤差。另外，有些生物學家想知道的分群結果不單單只是群集內的成員或群集的個數而已，他們還想要知道群集內與群集間的關係。
本研究要解決的問題是著重於蛋白質超家族（Super-Family）的序列分群，這是一般學者較少處理的部份。超家族的序列分群是有別於一般的序列分群，因為超家族內還包含不同的子家族，而不同的子家族可能具有不同的特性，因此除了需要建立在演化關係上來進行分群外，僅使用單一的工具或方法是無法完善的解決問題。基於上述的問題，本研究將利用演化的觀念來進行序列的分群。首先，利用已廣為生物學家使用的演化套件（Phylip）來進行演化樹的建置，接著經由距離解析、門檻值選擇、切割、合併、再合併等一連串的流程，最後將演化樹轉換成許多不同的子演化樹，而每一子演化樹即可代表一個群集。本研究的分群結果是基於演化樹而來的，因此比單純利用序列相似度的分群方法，是更具有演化上的意義，而且會有較佳的分群結果。

With the flourishing development of bioinformatics, biologists often use protein sequences to do analysis like predict and annotate unknown proteins. While carrying on these researches, the necessary leading work is clustering. Once the sequences are clustered, the sequences with similar function will be at same cluster and it is well to do follow-up researches by the cluster characteristic. Recently, the most common way to do clustering is exploiting the similarity degrees from sequences each other, but some researchers have already proved that this simple way is insufficient and will cause lots of errors. In addition, some biologists want to understand not only the members of the clusters or the number of the clusters, but also the relation in and between the clusters.
The problem we want to solve is to focus on the protein superfamily and this area is researches’ less treatment. It is different from general sequences clustering to protein superfamily clustering because there are still some different subfamilies in the superfamily. These subfamilies maybe have different characteristics and relations so it needs to be clustered based on evolution. Therefore only using simple tool or method couldn’t cope with this kind of problem. On the basis of the above mentions, we use the phylogenetic tree to cluster the sequences in protein superfamily. First, the standard package – Phylip is used to reconstruct the phylogenetic tree and then analyze it via a succession of procedures such as distance parsing, threshold choosing, splitting, merging, re-merging, and so on. Finally, the phylogenetic tree will be transformed into several sub-trees, and each sub-tree can represent one cluster. The method is based on phylogenetic tree, so it has evolutionary meanings and better clustering results than the methods based on sequences similarities.

英文文獻
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網站資料
PIR
(http://pir.georgetown.edu/)
PHYLIP
(http://evolution.genetics.washington.edu/phylip.html)
PFam
(http://www.sanger.ac.uk/Software/Pfam/)
SCOP
(http://scop.mrc-lmb.cam.ac.uk/scop/)
Swiss-Prot
(http://www.expasy.org/sprot/)
TROP's home page
(http://www.icp.ucl.ac.be/~opperd/)