改进NNPP2.2算法的启动子预测：以大肠杆菌DNA序列为例的研究

Improving promoter prediction for the NNPP2.2 algorithm: a case study using Escherichia coli DNA sequences.

作者信息

Burden S, Lin Y-X, Zhang R

机构信息

Department of Mathematics and Applied Statistics, University of Wollongong Wollongong, NSW 2522, Australia.

出版信息

Bioinformatics. 2005 Mar 1;21(5):601-7. doi: 10.1093/bioinformatics/bti047. Epub 2004 Sep 28.

DOI:10.1093/bioinformatics/bti047

PMID:15454410

Abstract

MOTIVATION

Although a great deal of research has been undertaken in the area of promoter prediction, prediction techniques are still not fully developed. Many algorithms tend to exhibit poor specificity, generating many false positives, or poor sensitivity. The neural network prediction program NNPP2.2 is one such example.

RESULTS

To improve the NNPP2.2 prediction technique, the distance between the transcription start site (TSS) associated with the promoter and the translation start site (TLS) of the subsequent gene coding region has been studied for Escherichia coli K12 bacteria. An empirical probability distribution that is consistent for all E.coli promoters has been established. This information is combined with the results from NNPP2.2 to create a new technique called TLS-NNPP, which improves the specificity of promoter prediction. The technique is shown to be effective using E.coli DNA sequences, however, it is applicable to any organism for which a set of promoters has been experimentally defined.

AVAILABILITY

The data used in this project and the prediction results for the tested sequences can be obtained from http://www.uow.edu.au/~yanxia/E_Coli_paper/SBurden_Results.xls

CONTACT

alh98@uow.edu.au.

摘要

动机

尽管在启动子预测领域已经进行了大量研究，但预测技术仍未完全发展成熟。许多算法往往表现出特异性差，产生许多假阳性结果，或者灵敏度低。神经网络预测程序NNPP2.2就是这样一个例子。

结果

为了改进NNPP2.2预测技术，对大肠杆菌K12细菌中与启动子相关的转录起始位点（TSS）和后续基因编码区的翻译起始位点（TLS）之间的距离进行了研究。已经建立了一种对所有大肠杆菌启动子都一致的经验概率分布。该信息与NNPP2.2的结果相结合，创建了一种名为TLS-NNPP的新技术，提高了启动子预测的特异性。使用大肠杆菌DNA序列证明了该技术是有效的，然而，它适用于任何一组启动子已通过实验定义的生物体。