臺灣博碩士論文加值系統

由於許多微生物無法在實驗室中人工培養，限制了生物學家了解物種及其聚落之間的互動關係，近年來由於次世代定序技術的成熟與相關技術的快速發展，透過環境樣本取得大量遺傳物質以區別物種與基因間關聯性的metagenomics研究亦受到關注，然而，隨之而來的問題，如快速取得的大量序列同時增加分類(binning)上的困難，因為環境遺傳物質樣本混雜著許多物種的核苷酸片段，要如何分辨出其原來所屬的物種，成為了一項需要克服的新問題。以往的分類方法分為基於組成份特徵分類演算法跟基於同源序列分類演算法兩種，組成份的方法是透過序列的核甘酸成分出現頻率特徵來分類序列，這種方法可快速的分類大量序列但錯誤率會較高，另一種方法是利用資料庫裡同源序列分類樣本序列雖然所需要時間較長，但準確率也較高；本篇研究將利用metagenome序列來計算各種n-nucleotide組成份，並配合基因演化演算法來找出最佳的參數，使用支持向量機和五倍的交叉驗證來得出預測結果，由結果得知我們的方法比以往的使用組成份頻率的方法擁有更高的精準率，所花費的時間也比利用同源序列減少許多，所以我們的方法大幅改善了以往的分類方法。

Metagenomics in environmental samples collected each DNA is unidentified DNA may have more sample in environmental. Metagenomics face a major challenge is to analyze the binning of the original sequence to be the same or similar communities in the category. The new sequencing technology makes it easier for metagenomics, simple and quick to get the sequence, but also more difficult to classify, because of short sequences to produce more than the previous technology. Classification short sequence 100 base pairs (bp) have until now relatively inaccurate, researchers need to use the older, long reading technology. We propose Support Vector Machine algorithm with Genetic Algorithm, a classifier to classify metagenome data, trained complete genomes planning and significantly improved the previous classification method based on synthetic.

目錄
致謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1實驗動機 1
1.2實驗目的 1
第二章 文獻研究回顧 2
第三章 材料與方法 5
3.1 Data set 5
3.2 Support Vector Machine 7
3.3選取特徵 9
3.3.1 Gapped Di-nucleotide Composition (Dj) 9
3.3.2 Local nucleotide Composition (NxC) 10
3.3.3 Tri-nucleotide (T) 11
3.3.4 Tetra-nucleotide (Te) 12
3.3.5 Penta-nucleotide (Pn) 13
3.4 Genetic Algorithm 14
第四章 研究結果與討論 16
4.1 評估公式 16
4.2 個別特徵的五倍交叉驗證結果 17
4.3 分類樹 27
4.4 整合策略 28
4.5 整體分類預測結果分析 30
第五章 結論 31
參考文獻 32
附錄 35

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