Analysis of n-Gram based Promoter Recognition Methods and Application to Whole Genome Promoter Prediction
Analysis of n-Gram based Promoter Recognition Methods and Application to Whole Genome Promoter Prediction
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DOI：
10.3233/isb-2009-0388
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发表时间：
2009-01-01
影响因子：
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通讯作者：
Bapi, Raju S.
中科院分区：
文献类型：
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作者：
Rani, T. Sobha;Bapi, Raju S.
启动子预测是一个重要而复杂的问题。模式识别算法通常需要能够捕捉这种复杂性的特征。对启动子序列中某些碱基对组合的特殊偏爱是可能的。为了确定这些偏差，通常提取和分析n图。n-gram是从给定的字符流中选择n个连续字符，在这种情况下是DNA序列片段。本文对n = 2、3、4、5时n-grams在启动子预测中的有效性进行了系统研究。研究了以n-grams为特征的大肠杆菌和果蝇启动子神经网络分类器。在大肠杆菌n = 3和果蝇n = 4的情况下，似乎给出了最优的预测值。利用3克特征对大肠杆菌基因组序列进行启动子预测。与BPROM、NNPP和SAK等软件包相比，该结果在基因组启动子的阳性鉴定方面令人鼓舞。在果蝇基因组中也进行了全基因组启动子预测，但具有4克特征。
Promoter prediction is an important and complex problem. Pattern recognition algorithms typically require features that could capture this complexity. A special bias towards certain combinations of base pairs in the promoter sequences may be possible. In order to determine these biases n-grams are usually extracted and analyzed. An n-gram is a selection of n contiguous characters from a given character stream, DNA sequence segments in this case. Here a systematic study is made to discover the efficacy of n-grams for n = 2, 3, 4, 5 in promoter prediction. A study of n-grams as features for a neural network classifier for E. coli and Drosophila promoters is made. In case of E. coli n = 3 and in case of Drosophila n = 4 seem to give optimal prediction values. Using the 3-gram features, promoter prediction in the genome sequence of E. coli is done. The results are encouraging in positive identification of promoters in the genome compared to software packages such as BPROM, NNPP, and SAK. Whole genome promoter prediction in Drosophila genome was also performed but with 4-gram features.