CAJ | 학술논문

采用生物信息学方法开发了一个预测病毒miRNA的计算机程序MiRfilter,通过提取已知的病毒miRNA的生物学特征,规定参数及参数范围,并以此为筛选标准,预测潜在的病毒miRNA.用EBV和RLCV两种病毒的正负样本对MiRfiher的预测精度进行检测.MiRfilter从39个EBV已知的miRNA中正确识别了30个,从116个负数据中检测到9个假阳性数据;从20个RLCV的miRNA中正确识别了15个,从108个负数据中检测到2个假阳性数据.用取白果蝇和线虫预测区域的186个负数据检测MiRfilwr的特异性,在186个负数据中没有预测出假阳性序列.
채용생물신식학방법개발료일개예측병독miRNA적계산궤정서MiRfilter,통과제취이지적병독miRNA적생물학특정,규정삼수급삼수범위,병이차위사선표준,예측잠재적병독miRNA.용EBV화RLCV량충병독적정부양본대MiRfiher적예측정도진행검측.MiRfilter종39개EBV이지적miRNA중정학식별료30개,종116개부수거중검측도9개가양성수거;종20개RLCV적miRNA중정학식별료15개,종108개부수거중검측도2개가양성수거.용취백과승화선충예측구역적186개부수거검측MiRfilwr적특이성,재186개부수거중몰유예측출가양성서렬.
This paper develops a computational procedure named MiRfilter to predict the vires miRNAs.MiRfilter selects some important biological features of miRNAs as parameters to predict the potential precursors and miRNAs.When using two samples to test MiRfilter's accuracy,it successfully retrieves 30 EBV-encoded miRNAs from 39 known ones,and predicts 9 false positive miRNAs from 1 16 negative ones.Then it retrieves 15 RLCV-encoded miRNAs from 20 known ones and predicts 2 false miRNAs from 108 negative ones.Further,when using another species to test MiRfilter's specificity,it predicts none false positive miRNA from 186 negative ones in D.melanogaster and C.elegans.