PromoterPredict: sequence-based modelling of Escherichia coli σ 70 promoter strength yields logarithmic dependence between promoter strength and sequence Article Swipe
Ramit Bharanikumar
,
Keshav Aditya R. Premkumar
,
Ashok Palaniappan
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.7717/peerj.5862
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.7717/peerj.5862
We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the σ 70 factor of RNA polymerase. σ 70 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 σ 70 -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.
Related Topics
Concepts
Promoter
Random hexamer
Biology
RNA polymerase
Sequence (biology)
Mathematics
Genetics
Computational biology
Escherichia coli
Molecular biology
Gene
Gene expression
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj.5862
- OA Status
- gold
- Cited By
- 26
- References
- 33
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2952802059Canonical identifier for this work in OpenAlex
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https://doi.org/10.7717/peerj.5862Digital Object Identifier
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PromoterPredict: sequence-based modelling of Escherichia coli σ 70 promoter strength yields logarithmic dependence between promoter strength and sequenceWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2018Year of publication
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2018-11-07Full publication date if available
- Authors
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Ramit Bharanikumar, Keshav Aditya R. Premkumar, Ashok PalaniappanList of authors in order
- Landing page
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https://doi.org/10.7717/peerj.5862Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.7717/peerj.5862Direct OA link when available
- Concepts
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Promoter, Random hexamer, Biology, RNA polymerase, Sequence (biology), Mathematics, Genetics, Computational biology, Escherichia coli, Molecular biology, Gene, Gene expressionTop concepts (fields/topics) attached by OpenAlex
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26Total citation count in OpenAlex
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2025: 4, 2024: 2, 2023: 4, 2022: 2, 2021: 6Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | PeerJ, Inc. |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320104 |
| primary_location.source.host_organization_lineage_names | PeerJ, Inc. |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | PeerJ |
| primary_location.landing_page_url | https://doi.org/10.7717/peerj.5862 |
| publication_date | 2018-11-07 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W1979200451, https://openalex.org/W2166277117, https://openalex.org/W1498410508, https://openalex.org/W2072361909, https://openalex.org/W2158266834, https://openalex.org/W2080233018, https://openalex.org/W2122291638, https://openalex.org/W2112264024, https://openalex.org/W1993154270, https://openalex.org/W2041890054, https://openalex.org/W116193106, https://openalex.org/W2047882868, https://openalex.org/W2111441093, https://openalex.org/W1594947347, https://openalex.org/W2232370058, https://openalex.org/W2140952049, https://openalex.org/W2071424950, https://openalex.org/W2089632280, https://openalex.org/W2117357746, https://openalex.org/W2126939295, https://openalex.org/W1982228455, https://openalex.org/W126745639, https://openalex.org/W2020251999, https://openalex.org/W2003479120, https://openalex.org/W2588481196, https://openalex.org/W2168597324, https://openalex.org/W1969395440, https://openalex.org/W2047360542, https://openalex.org/W2054735322, https://openalex.org/W1518026427, https://openalex.org/W1992026284, https://openalex.org/W2011457085, https://openalex.org/W2067702313 |
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| abstract_inverted_index.−10 | 59, 105, 130, 185 |
| abstract_inverted_index.−35 | 57, 103, 132, 187 |
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| abstract_inverted_index.https://promoterpredict.com | 227 |
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