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

2020 ◽  
pp. 90-97
Author(s):  
Hugo André Klauck ◽  
Gabriel Dall’Alba ◽  
Scheila de Avila e Silva ◽  
Ana Paula Longaray Delamare
Keyword(s):  
Open Access ◽  
Open Field ◽  
Computational Methods ◽  
Web Pages ◽  
Promoter Prediction ◽  
Gram Negative ◽  
Promoter Sequences ◽  
Web Tools ◽  
Intergenic Regions ◽  
Single Promoter

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.

Twin Wiki Wonders? Wikipedia and Wikibooks as Powerful Tools for Online Collaborative Writing

2011 ◽  
pp. 1078-1097
Author(s):  
Meng-Fen Grace Lin ◽  
Curtis J. Bonk ◽  
Suthiporn Sajjapanroj
Keyword(s):  
Open Access ◽  
Web 2.0 ◽  
Knowledge Sharing ◽  
Collaborative Writing ◽  
Community Building ◽  
Online Content ◽  
Web Tools ◽  
Single Person ◽  
The Creation ◽  
Shed Light

Web 2.0 technologies empower individuals to contribute thoughts and ideas rather than passively survey online content and resources. Such participatory environments foster opportunities for community building and knowledge sharing, while encouraging the creation of artifacts beyond what any single person could accomplish alone. In this chapter, we investigate the emergence and growth of two of such environments: the highly popular Wikipedia site and its sister project, Wikibooks. Wikipedia has grown out of trends for free and open access to Web tools and resources. While Wikipedians edit, contribute, and monitor distinct pieces of information or pages of documents, Wikibookians must focus on larger chunks of knowledge, including book modules or chapters as well as entire books. Several key differences between these two types of wiki environments are explored. In addition, surveys and interviews, conducted with Wikibookians, shed light on their challenges, frustrations, and successes.

Twin Wiki Wonders? Wikipedia and Wikibooks as Powerful Tools for Online Collaborative Writing

2011 ◽  
pp. 253-272
Author(s):  
Meng-Fen Grace Lin ◽  
Curtis J. Bonk ◽  
Suthiporn Sajjapanroj
Keyword(s):  
Open Access ◽  
Web 2.0 ◽  
Knowledge Sharing ◽  
Collaborative Writing ◽  
Community Building ◽  
Online Content ◽  
Web Tools ◽  
Single Person ◽  
The Creation ◽  
Shed Light

Web 2.0 technologies empower individuals to contribute thoughts and ideas rather than passively survey online content and resources. Such participatory environments foster opportunities for community building and knowledge sharing, while encouraging the creation of artifacts beyond what any single person could accomplish alone. In this chapter, we investigate the emergence and growth of two of such environments: the highly popular Wikipedia site and its sister project, Wikibooks. Wikipedia has grown out of trends for free and open access to Web tools and resources. While Wikipedians edit, contribute, and monitor distinct pieces of information or pages of documents, Wikibookians must focus on larger chunks of knowledge, including book modules or chapters as well as entire books. Several key differences between these two types of wiki environments are explored. In addition, surveys and interviews, conducted with Wikibookians, shed light on their challenges, frustrations, and successes.

scholarly journals Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yuming Zhao ◽  
Fang Wang ◽  
Su Chen ◽  
Jun Wan ◽  
Guohua Wang
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
High Throughput Sequencing ◽  
Prediction Models ◽  
Promoter Prediction ◽  
Sequencing Data ◽  
Protein Coding ◽  
Mirna Genes ◽  
Intergenic Regions

MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks.

scholarly journals Comparison of machine learning and deep learning techniques in promoter prediction across diverse species

2021 ◽  
Vol 7 ◽  
pp. e365
Author(s):  
Nikita Bhandari ◽  
Satyajeet Khare ◽  
Rahee Walambe ◽  
Ketan Kotecha
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Regulatory Elements ◽  
Gene Promoters ◽  
Promoter Prediction ◽  
Promoter Sequences ◽  
Learning Techniques

Gene promoters are the key DNA regulatory elements positioned around the transcription start sites and are responsible for regulating gene transcription process. Various alignment-based, signal-based and content-based approaches are reported for the prediction of promoters. However, since all promoter sequences do not show explicit features, the prediction performance of these techniques is poor. Therefore, many machine learning and deep learning models have been proposed for promoter prediction. In this work, we studied methods for vector encoding and promoter classification using genome sequences of three distinct higher eukaryotes viz. yeast (Saccharomyces cerevisiae), A. thaliana (plant) and human (Homo sapiens). We compared one-hot vector encoding method with frequency-based tokenization (FBT) for data pre-processing on 1-D Convolutional Neural Network (CNN) model. We found that FBT gives a shorter input dimension reducing the training time without affecting the sensitivity and specificity of classification. We employed the deep learning techniques, mainly CNN and recurrent neural network with Long Short Term Memory (LSTM) and random forest (RF) classifier for promoter classification at k-mer sizes of 2, 4 and 8. We found CNN to be superior in classification of promoters from non-promoter sequences (binary classification) as well as species-specific classification of promoter sequences (multiclass classification). In summary, the contribution of this work lies in the use of synthetic shuffled negative dataset and frequency-based tokenization for pre-processing. This study provides a comprehensive and generic framework for classification tasks in genomic applications and can be extended to various classification problems.

scholarly journals Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria

Data in Brief ◽  
2018 ◽  
Vol 19 ◽  
pp. 264-270 ◽  
Author(s):  
Rafael Vieira Coelho ◽  
Scheila de Avila e Silva ◽  
Sergio Echeverrigaray ◽  
Ana Paula Longaray Delamare
Keyword(s):  
Bacillus Subtilis ◽  
Promoter Prediction ◽  
Gram Positive ◽  
Data Set ◽  
Promoter Sequences ◽  
Gram Positive Bacteria

scholarly journals IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Kiran Sree Pokkuluri ◽  
Ramesh Babu Inampudi ◽  
S. S. S. N. Usha Devi Nedunuri
Keyword(s):  
Cellular Automata ◽  
Dna Sequences ◽  
Promoter Prediction ◽  
Coding Region ◽  
Mathematical Methods ◽  
Protein Coding ◽  
Promoter Sequences ◽  
Specificity And Sensitivity ◽  
Average Accuracy ◽  
Proposed Model

Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000). The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata) and MCC (modified clonal classifier) to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992) datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006) dataset and nonpromoters from EID (Saxonov et al., 2000) and UTRdb (Pesole et al., 2002) datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.

scholarly journals Genetic Environment and Expression of the Extended-Spectrum β-Lactamase blaPER-1 Gene in Gram-Negative Bacteria

2005 ◽  
Vol 49 (5) ◽  
pp. 1708-1713 ◽  
Author(s):  
Laurent Poirel ◽  
Ludovic Cabanne ◽  
Haluk Vahaboglu ◽  
Patrice Nordmann
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Amino Acid Identity ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Acid Identity ◽  
Promoter Sequences ◽  
Gene Coding ◽  
Serovar Typhimurium ◽  
Insertion Elements ◽  
Providencia Stuartii

ABSTRACT The genetic location of the gene coding for the expanded-spectrum β-lactamase PER-1 was analyzed in a series of gram-negative isolates. It was identified as part of a composite transposon bracketed by two novel insertion elements, ISPa12 and ISPa13, belonging to the IS4 family that possess transposases that share 63% amino acid identity and that are chromosomally located in Pseudomonas aeruginosa, Providencia stuartii, and Acinetobacter baumannii. On the contrary, the bla PER-1 gene was identified just downstream of an ISPa12 element but not within a composite transposon when it was located on a plasmid in Salmonella enterica serovar Typhimurium and A. baumannii isolates. In both cases, expression of the bla PER-1 gene was driven by promoter sequences located in ISPa12.

The glnAntrBC operon of Herbaspirillum seropedicae is transcribed by two oppositely regulated promoters upstream of glnA

2007 ◽  
Vol 53 (1) ◽  
pp. 100-105 ◽  
Author(s):  
Stefan Schwab ◽  
Emanuel M Souza ◽  
Marshall G Yates ◽  
Darlene C Persuhn ◽  
M Berenice R. Steffens ◽  
...  
Keyword(s):  
Transcriptional Regulation ◽  
Endophytic Bacterium ◽  
Wild Type ◽  
Herbaspirillum Seropedicae ◽  
Promoter Sequences ◽  
Intergenic Regions ◽  
In The Wild ◽  
Ammonium Regulation ◽  
Regulated Promoters ◽  
Wild Type Promoter

Herbaspirillum seropedicae is an endophytic bacterium that fixes nitrogen under microaerophilic conditions. The putative promoter sequences glnAp1 (σ70-dependent) and glnAp2 (σ54), and two NtrC-binding sites were identified upstream from the glnA, ntrB and ntrC genes of this microorganism. To study their transcriptional regulation, we used lacZ fusions to the H. seropedicae glnA gene, and the glnA-ntrB and ntrB-ntrC intergenic regions. Expression of glnA was up-regulated under low ammonium, but no transcription activity was detected from the intergenic regions under any condition tested, suggesting that glnA, ntrB and ntrC are co-transcribed from the promoters upstream of glnA. Ammonium regulation was lost in the ntrC mutant strain. A point mutation was introduced in the conserved –25/–24 dinucleotide (GG→TT) of the putative σ54-dependent promoter (glnAp2). Contrary to the wild-type promoter, glnA expression with the mutant glnAp2 promoter was repressed in the wild-type strain under low ammonium levels, but this repression was abolished in an ntrC background. Together our results indicate that the H. seropedicae glnAntrBC operon is regulated from two functional promoters upstream from glnA, which are oppositely regulated by the NtrC protein.Key words: Herbaspirillum seropedicae, nitrogen assimilation, glnAntrBC operon, transcriptional regulation.

scholarly journals Standards opportunities around data-bearing Web pages

2013 ◽  
Vol 371 (1987) ◽  
pp. 20120381 ◽  
Author(s):  
David Karger
Keyword(s):  
Information Management ◽  
End Users ◽  
Structured Data ◽  
Web Pages ◽  
Management Tools ◽  
Future Evolution ◽  
Left Behind ◽  
Web Tools ◽  
Web Developers ◽  
The Web

The evolving Web has seen ever-growing use of structured data, thanks to the way it enhances information authoring, querying, visualization and sharing. To date, however, most structured data authoring and management tools have been oriented towards programmers and Web developers. End users have been left behind, unable to leverage structured data for information management and communication as well as professionals. In this paper, I will argue that many of the benefits of structured data management can be provided to end users as well. I will describe an approach and tools that allow end users to define their own schemas (without knowing what a schema is), manage data and author (not program) interactive Web visualizations of that data using the Web tools with which they are already familiar, such as plain Web pages, blogs, wikis and WYSIWYG document editors. I will describe our experience deploying these tools and some lessons relevant to their future evolution.

scholarly journals Identification of Mycobacterial σ Factor Binding Sites by Chromatin Immunoprecipitation Assays

2006 ◽  
Vol 189 (5) ◽  
pp. 1505-1513 ◽  
Author(s):  
Sébastien Rodrigue ◽  
Joëlle Brodeur ◽  
Pierre-Étienne Jacques ◽  
Alain L. Gervais ◽  
Ryszard Brzezinski ◽  
...  
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Target Genes ◽  
Inducible Promoter ◽  
In Vitro Transcription ◽  
Promoter Sequences ◽  
Genome Wide ◽  
Σ Factor ◽  
Intergenic Regions ◽  
Transcriptional Start Sites

ABSTRACT Mycobacterium tuberculosis and Mycobacterium bovis are responsible for infections that cause a substantial amount of death, suffering, and loss around the world. Still, relatively little is known about the mechanisms of gene expression in these bacteria. Here, we used genome-wide location assays to identify direct target genes for mycobacterial σ factors. Chromatin immunoprecipitation assays were performed with M. bovis BCG for Myc-tagged proteins expressed using an anhydrotetracycline-inducible promoter, and enriched DNA fragments were hybridized to a microarray representing intergenic regions from the M. tuberculosis H37Rv genome. Several putative target genes were validated by quantitative PCR. The corresponding transcriptional start sites were identified for σF, σC, and σK, and consensus promoter sequences are proposed. Our conclusions were supported by the results of in vitro transcription assays. We also examined the role of each holoenzyme in the expression of σ factor genes. Our results revealed that many σ factors are expressed from autoregulated promoters.

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