目前生物資訊領域中一挑戰性課題即是，於人類的三十億個鹼基對當中，標記出每個基因特定的結構。其中，多聚腺苷酸化點(Polyadenylation site)是位於基因結尾的一種特徵結構。在多聚腺苷酸化點，前信使核醣核酸(pre-mRNA)會發生核酸內切，並緊接著在切開的位置形成多腺苷酸尾巴(poly(A) tail)，而多腺苷酸尾巴幾乎在所有成熟的真核生物前信使核醣核酸的末端都有發現。

前信使核醣核酸的”切開/多聚腺苷酸化作用”，最少與兩個調控訊號相關。這兩個調控訊號分別是(1)多腺苷酸訊號(PAS)，位於多聚腺苷酸化點的上游，約距離十到三十個核苷酸，且包含一個核苷酸組成具高度保守特性的AAUAAA六聚物(或是一個常見的變化型AUUAAA); (2)下游元素(DE)，位於多聚腺苷酸化點的下游，約相距二十到四十個核苷酸，是由特徵較不顯著但已知富含尿嘧啶(U)或尿嘧啶-鳥嘌呤(G-U)的序列所組成。

在這篇論文裡，我們以多聚腺苷酸化點為參考點，將人類基因的3’端依相關位置分為幾個狀態，採用關聯圖及其貝氏網路展開來建立各狀態的數學模型，以捕捉各狀態的特徵關聯性，並串連各狀態以建立一套人類基因3’端的隨機語法。接著，我們使用整合GenBank與Ensembl資料庫所得的基因資料測試我們的隨機語法對人類基因3’端的預測效果。

In bioinformatics, one of the challenging issue is to determine the specific structure of each
gene from the 3 billion base-pairs of human DNA sequences. Polyadenylation site is a
specific feature at the terminus of a gene which involves the endonucleolytic cleavage of the
pre-mRNA followed by the addition of a poly(A) tail, which is found at the 3’-terminal of
the majority of mRNA.
Factors related to cleavage and polyadenylation have to recognize associated signals, i.e.,
polyadenylation signal (PAS) and downstream element(DSE). PAS is the signal appearing
in 10 to 30 nucleotide upstream of the cleavage and polyadenylation site and is with a highly
conserved hexamer AAUAAA and a common variant AUUAAA in pre-mRNAs. DSE is in
20 to 40 nucleotide downstream to the cleavage and polyadenylation site and consists of a
much less conserved U- or GU-rich sequence.
In this thesis, we will construct a stochastic grammar of 3’-terminal of human genes by
establishing the dependency graphs and their expanded Bayesian networks of the features
in this region. Further more we will compare the performances of this stochastic grammar
and the PAS detector provided by former researchers.

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