PromoterPredict: sequence-based modelling of Escherichia coli σ 70 promoter strength yields logarithmic dependence between promoter strength and sequence

PeerJ

article

Ramit Bharanikumar¹  Keshav Aditya R. Premkumar²  Ashok Palaniappan³ 

[1] Biotechnology, Sri Venkateswara College of Engineering;Computer Science and Engineering, Sri Venkateswara College of Engineering;Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed University

关键词: Regression modelling;    Promoter sequences;    Promoter strength prediction;    Sigma70 promoters;    Genetic engineering;    Weak promoters;    PWM construction;    Data mining;    Software tools;   

DOI  :  10.7717/peerj.5862

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the σ70 factor of RNA polymerase. σ70 promoters are ubiquitously used in recombinant DNA technology, but characterizing their strength is demanding in terms of both time and money. We parsed a comprehensive database of bacterial promoters for the −35 and −10 hexamer regions of σ70-binding promoters and used these sequences to construct the respective position weight matrices (PWM). Next we used a well-characterized set of promoters to train a multivariate linear regression model and learn the mapping between PWM scores of the −35 and −10 hexamers and the promoter strength. We found that the log of the promoter strength is significantly linearly associated with a weighted sum of the −10 and −35 sequence profile scores. We applied our model to 100 sets of 100 randomly generated promoter sequences to generate a sampling distribution of mean strengths of random promoter sequences and obtained a mean of 6E-4 ± 1E-7. Our model was further validated by cross-validation and on independent datasets of characterized promoters. PromoterPredict accepts −10 and −35 hexamer sequences and returns the predicted promoter strength. It is capable of dynamic learning from user-supplied data to refine the model construction and yield more robust estimates of promoter strength. PromoterPredict is available as both a web service (https://promoterpredict.com) and standalone tool (https://github.com/PromoterPredict). Our work presents an intuitive generalization applicable to modelling the strength of other promoter classes.

【 授权许可】

CC BY