Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria
This paper presents a prediction of Bacillus subtilis promoters using a Support Vector Machine system. In the literature, there is a lack of information on Gram-positive bacterial promoter sequences compared to Gram-negative bacteria. Promoter sequence identification is essential for studying gene e...
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| Format: | Article |
| Language: | English |
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Elsevier
2018-08-01
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| Series: | Data in Brief |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340918305286 |
| _version_ | 1819131035536850944 |
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| author | Rafael Vieira Coelho Scheila de Avila e Silva Sergio Echeverrigaray Ana Paula Longaray Delamare |
| author_facet | Rafael Vieira Coelho Scheila de Avila e Silva Sergio Echeverrigaray Ana Paula Longaray Delamare |
| author_sort | Rafael Vieira Coelho |
| collection | DOAJ |
| description | This paper presents a prediction of Bacillus subtilis promoters using a Support Vector Machine system. In the literature, there is a lack of information on Gram-positive bacterial promoter sequences compared to Gram-negative bacteria. Promoter sequence identification is essential for studying gene expression. Initially, we collected the B. subtilis genome sequence from the NCBI database, and promoters were identified by their sigma factors in the DBTBS database. We then grouped the promoters according to 15 factors in 2 domains, corresponding to sigma 54 and sigma 70 of Gram-negative bacteria. Based on these data we developed a script in Python to search for promoters in the B. subtilis genome. After processing the data, we obtained 767 promoter sequences for B. subtilis, most of which were recognized by sigma SigA. To validate the data we found, we developed a software package called BacSVM+, which receives promoters as input and returns the best combination of parameters in a LibSVM library to predict promoter regions in the bacteria used in the simulation. All data gathered as well as the BacSVM+ software is available for download at http://bacpp.bioinfoucs.com/rafael/Sigmas.zip. Keywords: Promoter sequences, Bacillus subtilis, SVM |
| first_indexed | 2024-12-22T09:09:06Z |
| format | Article |
| id | doaj.art-bf5ef8aa1f2344cf847d96adc97eb7c7 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-22T09:09:06Z |
| publishDate | 2018-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-bf5ef8aa1f2344cf847d96adc97eb7c72022-12-21T18:31:31ZengElsevierData in Brief2352-34092018-08-0119264270Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteriaRafael Vieira Coelho0Scheila de Avila e Silva1Sergio Echeverrigaray2Ana Paula Longaray Delamare3Rio Grande do Sul Federal Institute of Education, Science and Technology (IFRS), Farroupilha Campus, Farroupilha, RS, Brazil; Corresponding author.Biotechnology Institute, University of Caxias do Sul (UCS), Caxias do Sul, RS, BrazilBiotechnology Institute, University of Caxias do Sul (UCS), Caxias do Sul, RS, BrazilBiotechnology Institute, University of Caxias do Sul (UCS), Caxias do Sul, RS, BrazilThis paper presents a prediction of Bacillus subtilis promoters using a Support Vector Machine system. In the literature, there is a lack of information on Gram-positive bacterial promoter sequences compared to Gram-negative bacteria. Promoter sequence identification is essential for studying gene expression. Initially, we collected the B. subtilis genome sequence from the NCBI database, and promoters were identified by their sigma factors in the DBTBS database. We then grouped the promoters according to 15 factors in 2 domains, corresponding to sigma 54 and sigma 70 of Gram-negative bacteria. Based on these data we developed a script in Python to search for promoters in the B. subtilis genome. After processing the data, we obtained 767 promoter sequences for B. subtilis, most of which were recognized by sigma SigA. To validate the data we found, we developed a software package called BacSVM+, which receives promoters as input and returns the best combination of parameters in a LibSVM library to predict promoter regions in the bacteria used in the simulation. All data gathered as well as the BacSVM+ software is available for download at http://bacpp.bioinfoucs.com/rafael/Sigmas.zip. Keywords: Promoter sequences, Bacillus subtilis, SVMhttp://www.sciencedirect.com/science/article/pii/S2352340918305286 |
| spellingShingle | Rafael Vieira Coelho Scheila de Avila e Silva Sergio Echeverrigaray Ana Paula Longaray Delamare Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria Data in Brief |
| title | Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria |
| title_full | Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria |
| title_fullStr | Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria |
| title_full_unstemmed | Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria |
| title_short | Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria |
| title_sort | bacillus subtilis promoter sequences data set for promoter prediction in gram positive bacteria |
| url | http://www.sciencedirect.com/science/article/pii/S2352340918305286 |
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