TargetRNA3: predicting prokaryotic RNA regulatory targets with machine learning.

Small regulatory RNAs pervade prokaryotes, with the best-studied family of these non-coding genes corresponding to trans-acting regulators that bind via base pairing to their message targets. Given the increasing frequency with which these genes are being identified, it is important that methods for illuminating their regulatory targets keep pace. Using a machine learning approach, we investigate thousands of interactions between small RNAs and their targets, and we interrogate more than a hundred features indicative of these interactions.

MLpronto: A tool for democratizing machine learning.

The democratization of machine learning is a popular and growing movement. In a world with a wealth of publicly available data, it is important that algorithms for analysis of data are accessible and usable by everyone. We present MLpronto, a system for machine learning analysis that is designed to be easy to use so as to facilitate engagement with machine learning algorithms.

transcriptome assembly from a compendium of RNA-seq data sets.

Owing to the complexities of bacterial RNA biology, the transcriptomes of even the best studied bacteria are not fully understood. To help elucidate the transcriptional landscape of , we compiled a compendium of 3,376 RNA-seq data sets composed of more than 7 trillion sequenced bases, which we evaluate with a transcript assembly pipeline. We report expression profiles for all annotated genes as well as 5,071 other transcripts.

Identification and characterization of RNA binding sites for (p)ppGpp using RNA-DRaCALA.

Ligand-binding RNAs (RNA aptamers) are widespread in the three domains of life, serving as sensors of metabolites and other small molecules. When aptamers are embedded within RNA transcripts as components of riboswitches, they can regulate gene expression upon binding their ligands. Previous methods for biochemical validation of computationally predicted aptamers are not well-suited for rapid screening of large numbers of RNA aptamers.

A computational system for identifying operons based on RNA-seq data.

An operon is a set of neighboring genes in a genome that is transcribed as a single polycistronic message. Genes that are part of the same operon often have related functional roles or participate in the same metabolic pathways. The majority of all bacterial genes are co-transcribed with one or more other genes as part of a multi-gene operon.

Transcriptome Analysis of Neisseria gonorrhoeae during Natural Infection Reveals Differential Expression of Antibiotic Resistance Determinants between Men and Women.

is a bacterial pathogen responsible for the sexually transmitted infection gonorrhea. Emergence of antimicrobial resistance (AMR) of worldwide has resulted in limited therapeutic choices for this infection. Men who seek treatment often have symptomatic urethritis; in contrast, gonococcal cervicitis in women is usually minimally symptomatic, but may progress to pelvic inflammatory disease.

Small RNA profiling in identifies MrsI as necessary for an anticipatory iron sparing response.

One key to the success of as a pathogen is its ability to reside in the hostile environment of the human macrophage. Bacteria adapt to stress through a variety of mechanisms, including the use of small regulatory RNAs (sRNAs), which posttranscriptionally regulate bacterial gene expression. However, very little is currently known about mycobacterial sRNA-mediated riboregulation.

Identification and Characterization of Serotype-Specific Variation in Group A Streptococcus Pilus Expression.

Isolates of a given bacterial pathogen often display phenotypic variation, and this can negatively impact public health, for example, by reducing the efficacy of preventative measures. Here, we identify that the human pathogen group A (GAS; ) expresses pili on its cell surface in a serotype-specific manner. Specifically, we show that serotype M3 GAS isolates, which are nonrandomly associated with causing particularly severe and lethal invasive infections, produce negligible amounts of pili relative to serotype M1 and M49 isolates.

Probing the sRNA regulatory landscape of P. aeruginosa: post-transcriptional control of determinants of pathogenicity and antibiotic susceptibility.

During environmental adaptation bacteria use small regulatory RNAs (sRNAs) to repress or activate expression of a large fraction of their proteome. We extended the use of the in vivo RNA proximity ligation method toward probing global sRNA interactions with their targets in Pseudomonas aeruginosa and verified the method with a known regulon controlled by the PrrF1 sRNA. We also identified two sRNAs (Sr0161 and ErsA) that interact with the mRNA encoding the major porin OprD responsible for the uptake of carbapenem antibiotics.

GRIL-seq provides a method for identifying direct targets of bacterial small regulatory RNA by in vivo proximity ligation.

The first step in the post-transcriptional regulatory function of most bacterial small non-coding RNAs (sRNAs) is base pairing with partially complementary sequences of targeted transcripts. We present a simple method for identifying sRNA targets in vivo and defining processing sites of the regulated transcripts. The technique, referred to as global small non-coding RNA target identification by ligation and sequencing (GRIL-seq), is based on preferential ligation of sRNAs to the ends of base-paired targets in bacteria co-expressing T4 RNA ligase, followed by sequencing to identify the chimaeras.

The Gonococcal Transcriptome during Infection of the Lower Genital Tract in Women.

Gonorrhea is a highly prevalent disease resulting in significant morbidity worldwide, with an estimated 106 cases reported annually. Neisseria gonorrhoeae, the causative agent of gonorrhea, colonizes and infects the human genital tract and often evades host immune mechanisms until successful antibiotic treatment is used. The alarming increase in antibiotic-resistant strains of N.

De novo assembly of bacterial transcriptomes from RNA-seq data.

Transcriptome assays are increasingly being performed by high-throughput RNA sequencing (RNA-seq). For organisms whose genomes have not been sequenced and annotated, transcriptomes must be assembled de novo from the RNA-seq data. Here, we present novel algorithms, specific to bacterial gene structures and transcriptomes, for analysis of bacterial RNA-seq data and de novo transcriptome assembly.

Identification of sRNAs expressed by the human pathogen Neisseria gonorrhoeae under disparate growth conditions.

In the last several years, bacterial gene regulation via small RNAs (sRNAs) has been recognized as an important mechanism controlling expression of essential proteins that are critical to bacterial growth and metabolism. Technologies such as RNA-seq are rapidly expanding the field of sRNAs and are enabling a global view of the "sRNAome" of several bacterial species. While numerous sRNAs have been identified in a variety of both Gram-negative and Gram-positive bacteria, only a very small number have been fully characterized in the human pathogen Neisseria gonorrhoeae, the etiological agent of the STD gonorrhea.

TargetRNA2: identifying targets of small regulatory RNAs in bacteria.

Many small, noncoding RNAs (sRNAs) in bacteria act as posttranscriptional regulators of messenger RNAs. TargetRNA2 is a web server that identifies mRNA targets of sRNA regulatory action in bacteria. As input, TargetRNA2 takes the sequence of an sRNA and the name of a sequenced bacterial replicon.

Computational analysis of bacterial RNA-Seq data.

Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data.

Mutations in interaction surfaces differentially impact E. coli Hfq association with small RNAs and their mRNA targets.

The RNA chaperone protein Hfq is required for the function of all small RNAs (sRNAs) that regulate mRNA stability or translation by limited base pairing in Escherichia coli. While there have been numerous in vitro studies to characterize Hfq activity and the importance of specific residues, there has been only limited characterization of Hfq mutants in vivo. Here, we use a set of reporters as well as co-immunoprecipitation to examine 14 Hfq mutants expressed from the E.

Computational identification of sRNA targets.

Many small noncoding RNAs (sRNAs) in bacteria act as posttranscriptional regulators by base pairing to their message targets. TargetRNA is a program that predicts the targets of a sRNA by identifying messages with significant potential to base pair with the sRNA. Since base pairing potential alone is insufficient to accurately identify sRNA targets, TargetRNA integrates several additional features of RNA interactions when predicting regulatory targets of a sRNA.

tRNA-like elements in Haloferax volcanii.

All functional RNAs are generated from precursor molecules by a plethora of processing steps. The generation of mature RNA molecules by processing is an important layer of gene expression regulation catalysed by ribonucleases. Here, we analysed 5S rRNA processing in the halophilic Archaeon Haloferax volcanii.

The entomopathogenic bacterial endosymbionts Xenorhabdus and Photorhabdus: convergent lifestyles from divergent genomes.

Members of the genus Xenorhabdus are entomopathogenic bacteria that associate with nematodes. The nematode-bacteria pair infects and kills insects, with both partners contributing to insect pathogenesis and the bacteria providing nutrition to the nematode from available insect-derived nutrients. The nematode provides the bacteria with protection from predators, access to nutrients, and a mechanism of dispersal.

Assessing computational tools for the discovery of small RNA genes in bacteria.

Over the past decade, a number of biocomputational tools have been developed to predict small RNA (sRNA) genes in bacterial genomes. In this study, several of the leading biocomputational tools, which use different methodologies, were investigated. The performance of the tools, both individually and in combination, was evaluated on ten sets of benchmark data, including data from a novel RNA-seq experiment conducted in this study.

Bioinformatic prediction and experimental verification of sRNAs in the haloarchaeon Haloferax volcanii.

Recently a small-scale RNomics study led to the experimental identification of 21 intergenic and 18 antisense sRNA genes in the haloarchaeon Haloferax volcanii. To broaden the knowledge about sRNAs in haloarchaea, two bioinformatic approaches were used to predict sRNA genes in the genome of H. volcanii.

Computational prediction and transcriptional analysis of sRNAs in Nitrosomonas europaea.

Bacterial small noncoding RNAs (sRNAs) have been discovered in many genetically well-studied microorganisms and have been shown to regulate critical cellular processes at the post-transcriptional level. In this study, we used comparative genomics and microarray data to analyze the genome of the ammonia-oxidizing bacterium Nitrosomonas europaea for the presence and expression of sRNAs. Fifteen genes encoding putative sRNAs (psRNAs) were identified.

Small RNAs of the halophilic archaeon Haloferax volcanii.

In recent years, sRNAs (small non-coding RNAs) have been found to be abundant in eukaryotes and bacteria and have been recognized as a novel class of gene expression regulators. In contrast, much less is known about sRNAs in archaea, except for snoRNAs (small nucleolar RNAs) that are involved in the modification of bases in stable RNAs. Therefore bioinformatic and experimental RNomics approaches were undertaken to search for the presence of sRNAs in the model archaeon Haloferax volcanii, resulting in more than 150 putative sRNA genes being identified.

Small non-coding RNAs in Streptomyces coelicolor.

In bacteria, small RNAs (sRNAs) make important regulatory contributions to an ever increasing number of cellular processes. To expand the repertoire of known sRNAs, we sought to identify novel sRNAs in the differentiating, multicellular bacterium Streptomyces coelicolor. We describe a combined bioinformatic and experimental approach that enabled the identification and characterization of nine novel sRNAs in S.

TargetRNA: a tool for predicting targets of small RNA action in bacteria.

Many small RNA (sRNA) genes in bacteria act as posttranscriptional regulators of target messenger RNAs. Here, we present TargetRNA, a web tool for predicting mRNA targets of sRNA action in bacteria. TargetRNA takes as input a genomic sequence that may correspond to an sRNA gene.