在計算生物學中，辨認基因位置是極具挑戰性的研究。近年來，許多的物種經由定序，快速累積大量的序列資料，藉由基因體的比較得以辨認基因位置。
序列分析是生物資訊的基礎，其中序列比對是最重要的一項工具。利用相近物種之基因結構及其序列在演化上高度保留的特性，透過序列比對可以預測基因位置。然而，現有的比對工具，雖然可提供最佳解或近似最佳解，但卻受限於運算速度。在本篇論文中我們提出可快速比對的分法，並結合訊號的偵測以開發基因預測的工具，以期快速且正確的預測基因位置。
此方法可應用在兩條未標定基因註解的同源序列的比對，在實驗部分我們透過人鼠的DNA序列驗證了其效能，進而探討人鼠同源基因資料庫中潛在未知的表現子。依據預測的基因表現子的序列相似度及序列長度建置成可查詢的資料庫，並評估其KA/KS ratio。
隨著定序物種的增加，經由實驗驗證的基因註解數量與日俱增，使得已知基因註解的資訊可應用於辨認初定序物種基因位置。這個方法，亦可應用於一以知基因註解及一未標定基因註解的同源序列比對。除此之外，我們亦建立了短序列表現子的比對方法，此方法進一步提高了基因結構預測的準確率。經實驗驗證我們預測的基因正確率可達81%，而表現子的正確率更可高達96%，其中表現子的被錯估比例小於1%。

Identifying protein coding genes is a challenging task in computational biology. With the rapid accumulation of genomic sequences for various organisms, it is now feasible to identify novel genes and exons by genomic comparisons. Sequence analysis is the base of bioinformatics, in which the sequence alignment is a major and fundamental task. Comparing coding regions among organisms can pinpoint functionally important parts of proteins, which are more conserved than the other parts bearing no functional significance. However, most of the currently available alignment programs, though providing optimal or near optimal results, have been limited by their computation speed. In this thesis, we propose a new method for coding region alignments. Based on a probabilistic filtration approach, CORAL (COding Region ALignment) is a linear time alignment tool. Integrating CORAL and signal detectors, we developed two programs, EXONALIGN and GeneAlign for coding exon prediction.
EXONALIGN simultaneously aligns and predicts exons between homologous genes/ syntenic regions. To reduce computation time and improve prediction accuracy, EXONALIGN calculates strengths of intrinsic splice signals and applies CORAL to measure sequence homologies between regions flanked by pairs of candidate splice acceptor and donor of homologous genes. The performance of EXONALIGN was evaluated on the ROSETTA and the Projector data sets. The predictions obtained by EXONALIGN are comparable with those obtained by widely used gene prediction programs, confirming the benefit of importing the conservation of exon-intron structures into an exon/gene prediction tool. Finally, EXONALIGN was employed to explore novel human exons within the annotated human-mouse homologous genes. More than one hundred novel human and mouse exon pairs were predicted within annotated genes. These putative human exons are longer than 100 bp and show greater than 70% sequence conservation to the corresponding mouse exons. The KA/KS ratios of 75% of the predicted exons are smaller than 1, further supporting the likelihood that the majority of newly predicted human exons code for proteins.
Furthermore, with increasing numbers of gene annotations verified by experiments, it is feasible to identify genes in the newly sequenced genomes by comparing to the annotated genes of phylogenetically close organisms. GeneAlign predicts protein coding genes by measuring the homologies between the sequence of a newly sequenced genome and the homologue of a related genome. GeneAlign was tested on Projector data set of 491 human-mouse homologous sequence pairs. At the gene level, both the average sensitivity and the average specificity of GeneAlign are 81%, and they are larger than 96% at the exon level. The rates of missing exons and wrong exons are smaller than 1%.

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