Prediction and Recognition of Gram-Negative Bacterial Promoter Sequences: An Analysis of Available Web Tools

Hugo André Klauck, Gabriel Dall’Alba, Scheila de Avila e Silva* and Ana Paula Longaray Delamare
Additional contact information
Hugo André Klauck: Instituto Federal do Rio Grande do Sul – Campus Bento Gonçalves – RS, Brazil
Gabriel Dall’Alba: Biotechnology Institute – University of Caxias do Sul - RS, Brazil
Scheila de Avila e Silva*: Biotechnology Institute – University of Caxias do Sul - RS, Brazil
Ana Paula Longaray Delamare: Biotechnology Institute – University of Caxias do Sul - RS, Brazil

Journal of Biotechnology Research, 2020, vol. 6, issue 7, 90-97

Abstract: Many computational methods aim to improve the prediction and recognition of transcription elements in prokaryotes. Despite this, the natural features of those elements make their prediction and recognition remain as an open field of research. In this paper, we compared the open-access tools BacPP, BPROM, bTSSfinder, CNNPromoter_b, iPro70-PseZNC, NNPP2, PePPer, and PromPredict. First, we listed the overall functionalities of each tool and the resources available on their web pages. Later, we carried out a comparison of prediction results using 206 intergenic regions. When evaluating the prediction using intergenic regions containing a single promoter within each, NNPP2 and BacPP obtained >90% correct predictions, with NNPP2 obtaining the highest values of match between predicted promoter location and location indicated by RegulonDB. Overall, many discrepancies were observed among the results. They may be explained by the differences in the methodologies that each tool applies for promoter prediction, not excluding the natural features of promoters as a factor as well. In any case, the results highlight the necessity to continue the efforts to improve promoter prediction, perhaps combining multiple approaches. Through said efforts, some of the challenges of the postgenomic era may be tackled as well.

Keywords: Gram-negative; Web tools; E. coli; Bioinformatics; Computational methods; Promoter prediction tools. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.arpgweb.com/pdf-files/jbr6(7)90-97.pdf (application/pdf)
https://www.arpgweb.com/journal/16/archive/07-2020/7/6 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arp:rjbarp:2020:p:90-97

DOI: 10.32861/jbr.67.90.97

Access Statistics for this article

Journal of Biotechnology Research is currently edited by Dr. Ashraf Abd El-Halim El-Sayed

More articles in Journal of Biotechnology Research from Academic Research Publishing Group Rahim Yar Khan 64200, Punjab, Pakistan.
Bibliographic data for series maintained by Managing Editor ().