SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas.
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
22 Sep 2020
22 Sep 2020
Historique:
received:
11
02
2020
accepted:
31
08
2020
entrez:
23
9
2020
pubmed:
24
9
2020
medline:
3
11
2020
Statut:
epublish
Résumé
In silico promoter prediction represents an important challenge in bioinformatics as it provides a first-line approach to identifying regulatory elements to support wet-lab experiments. Historically, available promoter prediction software have focused on sigma factor-associated promoters in the model organism E. coli. As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such as Pseudomonas, a Gram-negative bacterium of crucial medical and biotechnological importance. We developed SAPPHIRE, a promoter predictor for σ70 promoters in Pseudomonas. This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the - 35 and - 10 boxes of σ70 promoters found experimentally in P. aeruginosa and P. putida. SAPPHIRE currently outperforms established predictive software when classifying Pseudomonas σ70 promoters and was built to allow further expansion in the future. SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com . Alternatively, users can download the tool as a Python 3 script for local application from this site.
Sections du résumé
BACKGROUND
BACKGROUND
In silico promoter prediction represents an important challenge in bioinformatics as it provides a first-line approach to identifying regulatory elements to support wet-lab experiments. Historically, available promoter prediction software have focused on sigma factor-associated promoters in the model organism E. coli. As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such as Pseudomonas, a Gram-negative bacterium of crucial medical and biotechnological importance.
RESULTS
RESULTS
We developed SAPPHIRE, a promoter predictor for σ70 promoters in Pseudomonas. This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the - 35 and - 10 boxes of σ70 promoters found experimentally in P. aeruginosa and P. putida. SAPPHIRE currently outperforms established predictive software when classifying Pseudomonas σ70 promoters and was built to allow further expansion in the future.
CONCLUSIONS
CONCLUSIONS
SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com . Alternatively, users can download the tool as a Python 3 script for local application from this site.
Identifiants
pubmed: 32962628
doi: 10.1186/s12859-020-03730-z
pii: 10.1186/s12859-020-03730-z
pmc: PMC7510298
doi:
Substances chimiques
DNA, Bacterial
0
Sigma Factor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
415Subventions
Organisme : H2020 European Research Council
ID : 819800
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