Bacterial Promoter Features Description and Their Application on E. coli in silico Prediction and Recognition Approaches

Silva, Scheila de Ávila e; Echeverrigaray, Sérgio

doi:10.5772/48149

Cited by 9 publications

(7 citation statements)

References 63 publications

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“…In bacteria, the RNA polymerase contains five subunits (2a; b; b 0 ,u) and an extra s factor. 1,2 Thes factors can be labeled as s 24 ; s 28 ; s 32 ; s 38 ; s 54 and s 70 according to the molecular weights. Differents factors direct the RNA polymerase binding to different promoter regions, which can affect the consequent activation of genes.…”

Section: Introductionmentioning

confidence: 99%

“…s 24 and s 32 participate in heat-shock response, s 28 participates in the flagellar gene expression during normal growth, s 54 participates in nitrogen metabolism, and s 70 , called primary s factor, is in charge of transcription of most genes in growing cells. [2][3][4] Because the wet experiments are expensive to identify the types of promoters, several predictors were developed to identify the promoters based on the DNA sequence information; for example, iPro54-PseKNC 5 based on the PseKNC 6 was constructed to identify promoters. A position-correlation scoring function (PCSF) 7 and Bayes profile 8 were proposed to identify promoter.…”

Section: Introductionmentioning

confidence: 99%

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iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

Liu

2019

Molecular Therapy - Nucleic Acids

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Promoters are short regions at specific locations of DNA sequences, which are playing key roles in directing gene transcription. They can be grouped into six types (s 24 ; s 28 ; s 32 ; s 38 ; s 54 ; s 70). Recently, a predictor called "iPromoter-2L" was constructed to predict the promoters and their six types, which is the first approach to predict all the six types of promoters. However, its predictive quality still needs to be further improved for real-world application requirement. In this study, we proposed the smoothing cutting window algorithm to find the window fragments of the DNA sequences based on the conservation scores to capture the sequence patterns of promoters. For each window fragment, the discriminative features were extracted by using kmer and PseKNC. Combined with support vector machines (SVMs), different predictors were constructed and then clustered into several groups based on their distances. Finally, a new predictor called iPromoter-2L2.0 was constructed to identify the promoters and their six types, which was developed by ensemble learning based on the key predictors selected from the cluster groups. The results showed that iPromoter-2L2.0 outperformed other existing methods for both promoter prediction and identification of their six types, indicating that iPromoter-2L2.0 will be helpful for genomics analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

Liu

2019

Molecular Therapy - Nucleic Acids

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“…The most recent tools are based on machine learning models, such as CNNpromoter_b, using deep learning networks [6], BPROM, using linear discriminant analysis (LDA) [7], and bTSS finder, using artificial neural networks (ANN) [8]. However, these tools still return numerous false positives [9]. Such tools were developed using bacterial promoters and only search for the typical bacterial motifs of the −35 and −10 elements (TTGACA and TATAAT, respectively), thus not being suitable for phages genomes.…”

Section: Introductionmentioning

confidence: 99%

Predicting Promoters in Phage Genomes Using Machine Learning Models

Sampaio

Rocha

Oliveira

et al. 2019

Practical Applications of Computational Biology and Bioinformatics, 13th International Conference

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The renewed interest in phages as antibacterial agents has led to the exponentially growing number of sequenced phage genomes. Therefore, the development of novel bioinformatics methods to automate and facilitate phage genome annotation is of utmost importance. The most difficult step of phage genome annotation is the identification of promoters. As the existing methods for predicting promoters are not well suited for phages, we used machine learning models for locating promoters in phage genomes. Several models were created, using different algorithms and datasets, which consisted of known phage promoter and non-promoter sequences. All models showed good performance, but the ANN model provided better results for the smaller dataset (92% of accuracy, 89% of precision and 87% of recall) and the SVM model returned better results for the larger dataset (93% of accuracy, 91% of precision and 80% of recall). Both models were applied to the genome of Pseudomonas phage phiPsa17 and were able to identify both types of promoters, host and phage, found in phage genomes.

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“…The application of hidden neurons clouds the visualization of the learning process. After processing, the output layer carries the information out of the system …”

Section: Introductionmentioning

confidence: 99%

“…After processing, the output layer carries the information out of the system. 18 The ANN approach has been a successful application of machine learning along the years. It can be seen in promoter prediction tools such as Neural Networks Promoter Prediction, trained to predict E. coli promoter sequences.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the nucleotide content of Escherichia coli promoter sequences related to the alternative sigma factors

Dall'Alba

Casa

Notari

et al. 2018

J of Molecular Recognition

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Promoters are DNA sequences located upstream of the transcription start site of genes. In bacteria, the RNA polymerase enzyme requires additional subunits, called sigma factors (σ) to begin specific gene transcription in distinct environmental conditions. Currently, promoter prediction still poses many challenges due to the characteristics of these sequences. In this paper, the nucleotide content of Escherichia coli promoter sequences, related to five alternative σ factors, was analyzed by a machine learning technique in order to provide profiles according to the σ factor which recognizes them. For this, the clustering technique was applied since it is a viable method for finding hidden patterns on a data set. As a result, 20 groups of sequences were formed, and, aided by the Weblogo tool, it was possible to determine sequence profiles. These found patterns should be considered for implementing computational prediction tools. In addition, evidence was found of an overlap between the functions of the genes regulated by different σ factors, suggesting that DNA structural properties are also essential parameters for further studies.

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Bacterial Promoter Features Description and Their Application on E. coli in silico Prediction and Recognition Approaches

Cited by 9 publications

References 63 publications

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

iPromoter-2L2.0: Identifying Promoters and Their Types by Combining Smoothing Cutting Window Algorithm and Sequence-Based Features

Predicting Promoters in Phage Genomes Using Machine Learning Models

Analysis of the nucleotide content of Escherichia coli promoter sequences related to the alternative sigma factors

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