scholarly journals sRNARFTarget: A fast machine-learning-based approach for transcriptome-wide sRNA Target Prediction

2021 ◽  
Author(s):  
Kratika Naskulwar ◽  
Lourdes Peña-Castillo
Keyword(s):  
Machine Learning ◽  
Bacterial Species ◽  
Prediction Method ◽  
Target Prediction ◽  
Accurate Method ◽  
Adaptive Responses ◽  
Small Regulatory Rnas ◽  
Regulatory Rnas ◽  
Species Specific ◽  
Bacterial Transcriptome

AbstractBacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programs in terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget.

scholarly journals Cis- and Trans-Encoded Small Regulatory RNAs in Bacillus subtilis

2021 ◽  
Vol 9 (9) ◽  
pp. 1865
Author(s):  
Sabine Brantl ◽  
Peter Müller
Keyword(s):  
Potential Role ◽  
Bacterial Species ◽  
Model Organism ◽  
Physiological Role ◽  
Base Pairing ◽  
Small Regulatory Rnas ◽  
Regulatory Rnas ◽  
Rna Chaperones ◽  
Cis And Trans

Small regulatory RNAs (sRNAs) that act by base-pairing are the most abundant posttranscriptional regulators in all three kingdoms of life. Over the past 20 years, a variety of approaches have been employed to discover chromosome-encoded sRNAs in a multitude of bacterial species. However, although largely improved bioinformatics tools are available to predict potential targets of base-pairing sRNAs, it is still challenging to confirm these targets experimentally and to elucidate the mechanisms as well as the physiological role of their sRNA-mediated regulation. Here, we provide an overview of currently known cis- and trans-encoded sRNAs from B. subtilis with known targets and defined regulatory mechanisms and on the potential role of RNA chaperones that are or might be required to facilitate sRNA regulation in this important Gram-positive model organism.

scholarly journals Stress Signaling in Cyanobacteria: A Mechanistic Overview

Life ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 312
Author(s):  
Raphaël Rachedi ◽  
Maryline Foglino ◽  
Amel Latifi
Keyword(s):  
Stress Responses ◽  
Photosynthetic Bacteria ◽  
Thermal Stresses ◽  
Current Knowledge ◽  
Adaptive Responses ◽  
Cellular Functions ◽  
Component Systems ◽  
Small Regulatory Rnas ◽  
Two Component Systems ◽  
Regulatory Rnas

Cyanobacteria are highly diverse, widely distributed photosynthetic bacteria inhabiting various environments ranging from deserts to the cryosphere. Throughout this range of niches, they have to cope with various stresses and kinds of deprivation which threaten their growth and viability. In order to adapt to these stresses and survive, they have developed several global adaptive responses which modulate the patterns of gene expression and the cellular functions at work. Sigma factors, two-component systems, transcriptional regulators and small regulatory RNAs acting either separately or collectively, for example, induce appropriate cyanobacterial stress responses. The aim of this review is to summarize our current knowledge about the diversity of the sensors and regulators involved in the perception and transduction of light, oxidative and thermal stresses, and nutrient starvation responses. The studies discussed here point to the fact that various stresses affecting the photosynthetic capacity are transduced by common mechanisms.

scholarly journals Characterization of a Novel Riboswitch-Regulated Lysine Transporter in Aggregatibacter actinomycetemcomitans

2010 ◽  
Vol 192 (23) ◽  
pp. 6240-6250 ◽  
Author(s):  
Peter Jorth ◽  
Marvin Whiteley
Keyword(s):  
Oral Cavity ◽  
Experimental Approach ◽  
Bacterial Species ◽  
Opportunistic Pathogen ◽  
Aggregatibacter Actinomycetemcomitans ◽  
Host Immune System ◽  
Biofilm Growth ◽  
Small Regulatory Rnas ◽  
Regulatory Rnas

ABSTRACT Aggregatibacter actinomycetemcomitans is an opportunistic pathogen that resides primarily in the mammalian oral cavity. In this environment, A. actinomycetemcomitans faces numerous host- and microbe-derived stresses, including intense competition for nutrients and exposure to the host immune system. While it is clear that A. actinomycetemcomitans responds to precise cues that allow it to adapt and proliferate in the presence of these stresses, little is currently known about the regulatory mechanisms that underlie these responses. Many bacteria use noncoding regulatory RNAs (ncRNAs) to rapidly alter gene expression in response to environmental stresses. Although no ncRNAs have been reported in A. actinomycetemcomitans, we propose that they are likely important for colonization and persistence in the oral cavity. Using a bioinformatic and experimental approach, we identified three putative metabolite-sensing riboswitches and nine small regulatory RNAs (sRNAs) in A. actinomycetemcomitans during planktonic and biofilm growth. Molecular characterization of one of the riboswitches revealed that it is a lysine riboswitch and that its target gene, lysT, encodes a novel lysine-specific transporter. Finally, we demonstrated that lysT and the lysT lysine riboswitch are conserved in over 40 bacterial species, including the phylogenetically related pathogen Haemophilus influenzae.

scholarly journals Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2789 ◽  
Author(s):  
Yoshiaki Maeda ◽  
Yui Sugiyama ◽  
Atsushi Kogiso ◽  
Tae-Kyu Lim ◽  
Manabu Harada ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Imaging System ◽  
Bacterial Species ◽  
Support Vector ◽  
Learning Approaches ◽  
Wide Range ◽  
Central Regions ◽  
Species Specific ◽  
Staphylococcus Spp

Detection and discrimination of bacteria are crucial in a wide range of industries, including clinical testing, and food and beverage production. Staphylococcus species cause various diseases, and are frequently detected in clinical specimens and food products. In particular, S. aureus is well known to be the most pathogenic species. Conventional phenotypic and genotypic methods for discrimination of Staphylococcus spp. are time-consuming and labor-intensive. To address this issue, in the present study, we applied a novel discrimination methodology called colony fingerprinting. Colony fingerprinting discriminates bacterial species based on the multivariate analysis of the images of microcolonies (referred to as colony fingerprints) with a size of up to 250 μm in diameter. The colony fingerprints were obtained via a lens-less imaging system. Profiling of the colony fingerprints of five Staphylococcus spp. (S. aureus, S. epidermidis, S. haemolyticus, S. saprophyticus, and S. simulans) revealed that the central regions of the colony fingerprints showed species-specific patterns. We developed 14 discriminative parameters, some of which highlight the features of the central regions, and analyzed them by several machine learning approaches. As a result, artificial neural network (ANN), support vector machine (SVM), and random forest (RF) showed high performance for discrimination of theses bacteria. Bacterial discrimination by colony fingerprinting can be performed within 11 h, on average, and therefore can cut discrimination time in half compared to conventional methods. Moreover, we also successfully demonstrated discrimination of S. aureus in a mixed culture with Pseudomonas aeruginosa. These results suggest that colony fingerprinting is useful for discrimination of Staphylococcus spp.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

Sign in / Sign up

Export Citation Format

Share Document