隐半马氏模型在3'剪接位点识别中的应用

新近的基因识别软件比先前的软件有着显著的提高,但是在外显子水平上的敏感性和特异性仍然不十分令人满意.这是因为已有软件对于剪接位点,翻译起始等生物信号位点的识别还不够有效.如果能够分别提高这些生物信号位点的识别效果,就能够提高整体的基因识别效率.隐半马氏模型能够很好地刻画3'剪接位点(acceptor)的结构.据此开发的一套对acceptor进行识别的算法在Burset/Guigo的数据集上经过检验,获得了比已有算法更好的识别率.该模型的成功还使得我们对剪接点上游的分支位点和嘧啶富含区的概貌有了一定的认识,加深了人们对于acceptor的结构和剪接过程的理解.

Application of Hidden Semi-Markov Model to 3' Splice Sites Identification

In order to improve exon level sensitivity and specificity of recent gene-finding programs, strong "search by signal" components are needed to identify splice sites, translation start and other biological signal sites. A new model for the identification of 3' splice sites (acceptors) using Hidden Semi-Markov Model (HSMM) was introduced. This model is proved to be particularly suitable for modeling the biological structure of acceptors. When tested in Burset/Guigo dataset,this new method demonstrated an improved accuracy compared with existing method. The success of this model gives a deep understanding of the structure of acceptors and the biological process of splicing.