PromoterPredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between promoter strength and sequence
By Ramit Bharanikumar, K. A. R. Premkumar, Ashok Palaniappan. 2018
26 citations recorded by the source corpus.
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. Using a well-characterized set of promoters, we trained a multivariate linear regression model and found that the log of the promoter strength is significantly linearly associated with a weighted sum of the –10 and –35 sequence profile scores. It was found that the two regions contributed almost equally to the promoter strength. 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 confident estimates of promoter strength. Availability Open source code and a standalone executable with both dynamic model-building and prediction are available (under GNU General Public License 3.0) at https://github.com/PromoterPredict, and require Python 2.7 or greater. PromoterPredict is also available as a web service at https://promoterpredict.com. Contact apalania@scbt.sastra.edu
Subfields: Genetics, Molecular Biology, Evaluation & Benchmarks, Computational Biology & Bioinformatics
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In Molecular Biology
In Evaluation & Benchmarks
- Anirudh Ravula
- Chris Alberti
- J. Ainslie
- Santiago Ontañón
- Manzil Zaheer
- Philip Pham
- Kumar Avinava Dubey
In Computational Biology & Bioinformatics
Citations in the corpus
- Big Bird: Transformers for Longer Sequences (2020, methodology): “Multiple methods have been proposed to identify the promoter regions in a given DNA sequence [100, 59, 11, 99, 71], as it is an important first step in understanding gene regulation.”