【摘要】 该研究发展了一种EST(expressedsequencetag)聚类方法 (ESTClustering) ,用于分析大规模EST测序中所产生的大量数据 ,以获得高质量、非重复表达序列。该方法在聚类过程中采用MEGABLAST工具对一致序列进行序列同源比较 ,并用phrap程序对每一EST簇进行拼接检验。这一聚类策略能降低测序错误带来的影响 ,有效识别基因家族成员 ,并避免选择性剪接的干扰。与NCBI(NationalCenterforBiotechnologyInformation)的UniGeneclustering方法相比 ,ESTClustering的聚类结果可以更好地反映表达序列的多样性。用ESTClustering对 112 2 5 6条拟南芥EST聚类测试 ,产生 2 35 81个EST簇 ,其中 135 97个EST簇有对应拟南芥基因组编码序列 ,与该基因组中有EST作为依据的预测基因数目接近。应用该方法对收集的 14 7191条水稻EST序列进行聚类 ,形成 33896个EST簇。更多还原

【Abstract】 We developed an EST (expressed sequence tag) clustering method,ESTClustering,to generate high quality unique expressed sequence based on large scale EST sequencing.The method uses consensus sequences to sequence analyze with megablast and assemble each cluster with phrap in clustering process.The clustering strategy can efficiently identify gene family and alternate splicing forms of expressed sequences.It can also reduce the adverse effects caused by sequence errors.The ESTClustering method tends to provide more expressed gene forms comparing with the UniGene clustering method of the National Center for Biotechnology Information.Analysis of the 112 256 ESTs of Arabidopsis with ESTClustering produced 23 581 EST clusters.Among these Arabidopsis EST clusters,13 597 have corresponding genome coding sequences and this number is close to the number of genes predicted with Arabidopsis ESTs.Using this clustering method,a total of 147 191 rice ESTs were clustered into 33 896 groups.更多还原