Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease.

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson's disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

Extracellular vesicle small RNA cargo discriminates non-cancer donors from pediatric B-lymphoblastic leukemia patients.

Pediatric B-acute lymphoblastic leukemia (B-ALL) is a disease of abnormally growing B lymphoblasts. Here we hypothesized that extracellular vesicles (EVs), which are nanosized particles released by all cells (including cancer cells), could be used to monitor B-ALL severity and progression by sampling plasma instead of bone marrow. EVs are especially attractive as they are present throughout the circulation regardless of the location of the originating cell. First, we used nanoparticle tracking analysis to compare EVs between non-cancer donor (NCD) and B-ALL blood plasma; we found that B-ALL plasma contains more EVs than NCD plasma. We then isolated EVs from NCD and pediatric B-ALL peripheral blood plasma using a synthetic peptide-based isolation technique (Vn96), which is clinically amenable and isolates a broad spectrum of EVs. RNA-seq analysis of small RNAs contained within the isolated EVs revealed a signature of differentially packaged and exclusively packaged RNAs that distinguish NCD from B-ALL. The plasma EVs contain a heterogenous mixture of miRNAs and fragments of long non-coding RNA (lncRNA) and messenger RNA (mRNA). Transcripts packaged in B-ALL EVs include those involved in negative cell cycle regulation, potentially suggesting that B-ALL cells may use EVs to discard gene sequences that control growth. In contrast, NCD EVs carry sequences representative of multiple organs, including brain, muscle, and epithelial cells. This signature could potentially be used to monitor B-ALL disease burden in pediatric B-ALL patients via blood draws instead of invasive bone marrow aspirates.

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types.

The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship's trajectory, or kinematic, which capture the motion properties of a ship's movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us to provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric-kinematic taxonomy could be further developed as a tool for more accessible feature selection.

sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction.

Bacterial 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 programsin 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.

Promotech: a general tool for bacterial promoter recognition.

Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. However, most of these tools were designed to recognize promoters in one or few bacterial species. Here, we present Promotech, a machine-learning-based method for promoter recognition in a wide range of bacterial species. We compare Promotech's performance with the performance of five other promoter prediction methods. Promotech outperforms these other programs in terms of area under the precision-recall curve (AUPRC) or precision at the same level of recall. Promotech is available at https://github.com/BioinformaticsLabAtMUN/PromoTech .

Computational Methods for Elucidating Gene Expression Regulation in Bacteria.

Research over the past two decades has uncovered an unexpected complexity and intricacy of gene expression regulation in bacteria. Bacteria have (1) numerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation.To understand bacteria gene expression regulation, one needs to identify promoters, terminators, and sRNAs together with their targets. Here we describe the state of the art in computational methods to perform promoter recognition, sRNA identification, and sRNA target prediction. Additionally, we provide step-by-step instructions to use current approaches to perform these tasks.

Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning.

Ulcerative colitis (UC) is one of the most common forms of inflammatory bowel disease (IBD) characterized by inflammation of the mucosal layer of the colon. Diagnosis of UC is based on clinical symptoms, and then confirmed based on endoscopic, histologic and laboratory findings. Feature selection and machine learning have been previously used for creating models to facilitate the diagnosis of certain diseases. In this work, we used a recently developed feature selection algorithm (DRPT) combined with a support vector machine (SVM) classifier to generate a model to discriminate between healthy subjects and subjects with UC based on the expression values of 32 genes in colon samples. We validated our model with an independent gene expression dataset of colonic samples from subjects in active and inactive periods of UC. Our model perfectly detected all active cases and had an average precision of 0.62 in the inactive cases. Compared with results reported in previous studies and a model generated by a recently published software for biomarker discovery using machine learning (BioDiscML), our final model for detecting UC shows better performance in terms of average precision.

A Two-Component System Acquired by Horizontal Gene Transfer Modulates Gene Transfer and Motility via Cyclic Dimeric GMP.

Bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) is an important intracellular signaling molecule that affects diverse physiological processes in bacteria. The intracellular levels of c-di-GMP are controlled by proteins acting as diguanylate cyclase (DGC) and phosphodiesterase (PDE) enzymes that synthesize and degrade c-di-GMP, respectively. In the alphaproteobacterium Rhodobacter capsulatus, flagellar motility and gene exchange via production of the gene transfer agent RcGTA are regulated by c-di-GMP. One of the R. capsulatus proteins involved in this regulation is Rcc00620, which contains an N-terminal two-component system response regulator receiver (REC) domain and C-terminal DGC and PDE domains. We demonstrate that the enzymatic activity of Rcc00620 is regulated through the phosphorylation status of its REC domain, which is controlled by a cognate histidine kinase protein, Rcc00621. In this system, the phosphorylated form of Rcc00620 is active as a PDE enzyme and stimulates gene transfer and motility. In addition, we discovered that the rcc00620 and rcc00621 genes are present in only one lineage within the genus Rhodobacter and were acquired via horizontal gene transfer from a distantly related alphaproteobacterium in the order Sphingomonadales. Therefore, a horizontally acquired regulatory system regulates gene transfer in the recipient organism.

Learning-induced mRNA alterations in olfactory bulb mitral cells in neonatal rats.

In the olfactory bulb, a cAMP/PKA/CREB-dependent form of learning occurs in the first week of life that provides a unique mammalian model for defining the epigenetic role of this evolutionarily ancient plasticity cascade. Odor preference learning in the week-old rat pup is rapidly induced by a 10-min pairing of odor and stroking. Memory is demonstrable at 24 h, but not 48 h, posttraining. Using this paradigm, pups that showed peppermint preference 30 min posttraining were sacrificed 20 min later for laser microdissection of odor-encoding mitral cells. Controls were given odor only. Microarray analysis revealed that 13 nonprotein-coding mRNAs linked to mRNA translation and splicing and 11 protein-coding mRNAs linked to transcription differed with odor preference training. MicroRNA23b, a translation inhibitor of multiple plasticity-related mRNAs, was down-regulated. Protein-coding transcription was up-regulated for Sec23b, Clic2, Rpp14, Dcbld1, Magee2, Mstn, Fam229b, RGD1566265, and Mgst2. Gng12 and Srcg1 mRNAs were down-regulated. Increases in Sec23b, Clic2, and Dcbld1 proteins were confirmed in mitral cells in situ at the same time point following training. The protein-coding changes are consistent with extracellular matrix remodeling and ryanodine receptor involvement in odor preference learning. A role for CREB and AP1 as triggers of memory-related mRNA regulation is supported. The small number of gene changes identified in the mitral cell input/output link for 24 h memory will facilitate investigation of the nature, and reversibility, of changes supporting temporally restricted long-term memory.

Metagenomic analysis suggests broad metabolic potential in extracellular symbionts of the bivalve Thyasira cf. gouldi.

BACKGROUND: Next-generation sequencing has opened new avenues for studying metabolic capabilities of bacteria that cannot be cultured. Here, we provide a metagenomic description of chemoautotrophic gammaproteobacterial symbionts associated with Thyasira cf. gouldi, a sediment-dwelling bivalve from the family Thyasiridae. Thyasirid symbionts differ from those of other bivalves by being extracellular, and recent work suggests that they are capable of living freely in the environment. RESULTS: Thyasira cf. gouldi symbionts appear to form mixed, non-clonal populations in the host, show no signs of genomic reduction and contain many genes that would only be useful outside the host, including flagellar and chemotaxis genes. The thyasirid symbionts may be capable of sulfur oxidation via both the sulfur oxidation and reverse dissimilatory sulfate reduction pathways, as observed in other bivalve symbionts. In addition, genes for hydrogen oxidation and dissimilatory nitrate reduction were found, suggesting varied metabolic capabilities under a range of redox conditions. The genes of the tricarboxylic acid cycle are also present, along with membrane bound sugar importer channels, suggesting that the bacteria may be mixotrophic. CONCLUSIONS: In this study, we have generated the first thyasirid symbiont genomic resources. In Thyasira cf. gouldi, symbiont populations appear non-clonal and encode genes for a plethora of metabolic capabilities; future work should examine whether symbiont heterogeneity and metabolic breadth, which have been shown in some intracellular chemosymbionts, are signatures of extracellular chemosymbionts in bivalves.

Cyclic di-GMP-Mediated Regulation of Gene Transfer and Motility in Rhodobacter capsulatus.

Gene transfer agents (GTAs) are bacteriophage-like particles produced by several bacterial and archaeal lineages that contain small pieces of the producing cells' genomes that can be transferred to other cells in a process similar to transduction. One well-studied GTA is RcGTA, produced by the alphaproteobacterium Rhodobacter capsulatus RcGTA gene expression is regulated by several cellular regulatory systems, including the CckA-ChpT-CtrA phosphorelay. The transcription of multiple other regulator-encoding genes is affected by the response regulator CtrA, including genes encoding putative enzymes involved in the synthesis and hydrolysis of the second messenger bis-(3'-5')-cyclic dimeric GMP (c-di-GMP). To investigate whether c-di-GMP signaling plays a role in RcGTA production, we disrupted the CtrA-affected genes potentially involved in this process. We found that disruption of four of these genes affected RcGTA gene expression and production. We performed site-directed mutagenesis of key catalytic residues in the GGDEF and EAL domains responsible for diguanylate cyclase (DGC) and c-di-GMP phosphodiesterase (PDE) activities and analyzed the functions of the wild-type and mutant proteins. We also measured RcGTA production in R. capsulatus strains where intracellular levels of c-di-GMP were altered by the expression of either a heterologous DGC or a heterologous PDE. This adds c-di-GMP signaling to the collection of cellular regulatory systems controlling gene transfer in this bacterium. Furthermore, the heterologous gene expression and the four gene disruptions had similar effects on R. capsulatus flagellar motility as found for gene transfer, and we conclude that c-di-GMP inhibits both RcGTA production and flagellar motility in R. capsulatusIMPORTANCE Gene transfer agents (GTAs) are virus-like particles that move cellular DNA between cells. In the alphaproteobacterium Rhodobacter capsulatus, GTA production is affected by the activities of multiple cellular regulatory systems, to which we have now added signaling via the second messenger dinucleotide molecule bis-(3'-5')-cyclic dimeric GMP (c-di-GMP). Similar to the CtrA phosphorelay, c-di-GMP also affects R. capsulatus flagellar motility in addition to GTA production, with lower levels of intracellular c-di-GMP favoring increased flagellar motility and gene transfer. These findings further illustrate the interconnection of GTA production with global systems of regulation in R. capsulatus, providing additional support for the notion that the production of GTAs has been maintained in this and related bacteria because it provides a benefit to the producing organisms.

Prioritizing bona fide bacterial small RNAs with machine learning classifiers.

Bacterial small (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial species through RNA sequencing. The existence of putative sRNAs is usually validated by Northern blot analysis. However, the large amount of novel putative sRNAs reported in the literature makes it impractical to validate each of them in the wet lab. In this work, we applied five machine learning approaches to construct twenty models to discriminate bona fide sRNAs from random genomic sequences in five bacterial species. Sequences were represented using seven features including free energy of their predicted secondary structure, their distances to the closest predicted promoter site and Rho-independent terminator, and their distance to the closest open reading frames (ORFs). To automatically calculate these features, we developed an sRNA Characterization Pipeline (sRNACharP). All seven features used in the classification task contributed positively to the performance of the predictive models. The best performing model obtained a median precision of 100% at 10% recall and of 64% at 40% recall across all five bacterial species, and it outperformed previous published approaches on two benchmark datasets in terms of precision and recall. Our results indicate that even though there is limited sRNA sequence conservation across different bacterial species, there are intrinsic features in the genomic context of sRNAs that are conserved across taxa. We show that these features are utilized by machine learning approaches to learn a species-independent model to prioritize bona fide bacterial sRNAs.

Genetic variation associated with healthy traits and environmental conditions in Vaccinium vitis-idaea.

BACKGROUND: Lingonberry (Vaccinium vitis-idaea L.), one of the least studied fruit crops in the Ericaceae family, has a dramatically increased worldwide demand due to its numerous health benefits. Genetic markers can facilitate the selection of berries with desirable climatic adaptations, agronomic and nutritious characteristics to improve cultivation programs. However, no genomic resources are available for this species. RESULTS: We used Genotyping-by-Sequencing (GBS) to analyze the genetic variation of 56 lingonberry samples from across Newfoundland and Labrador, Canada. To elucidate a potential adaptation to environmental conditions we searched for genotype-environment associations by applying three distinct approaches to screen the identified single nucleotide polymorphisms (SNPs) for correlation with six environmental variables. We also searched for an association between the identified SNPs and two phenotypic traits: the total phenolic content (TPC) and antioxidant capacity (AC) of fruit. We identified 1586 high-quality putative SNPs using the UNEAK pipeline available in TASSEL. We found 132 SNPs likely associated with at least one of the environmental or phenotypic variables. To obtain insights on the function of the genomic sequences containing the SNPs likely to be associated with the environmental or phenotypic variables, we performed a sequence-based functional annotation and identified homologous protein-coding sequences with functional roles related to abiotic stress response, pathogen defense, RNA metabolism, and, most interestingly, phenolic compound biosynthesis. CONCLUSIONS: The putative SNPs discovered are the first genomic resource for lingonberry. This resource might prove useful in high-density quantitative trait locus analysis, and association mapping. The identified candidate genes containing the SNPs need further studies on their potential role in local adaptation of lingonberry. Altogether, the present study provides new resources that can be used to breed for desirable traits in lingonberry.

REPA: Applying Pathway Analysis to Genome-Wide Transcription Factor Binding Data.

Pathway analysis has been extensively applied to aid in the interpretation of the results of genome-wide transcription profiling studies, and has been shown to successfully find associations between the biological phenomena under study and biological pathways. There are two widely used approaches of pathway analysis: over-representation analysis, and gene set analysis. Recently genome-wide transcription factor binding data has become widely available allowing for the application of pathway analysis to this type of data. In this work, we developed regulatory enrichment pathway analysis (REPA) to apply gene set analysis to genome-wide transcription factor binding data to infer associations between transcription factors and biological pathways. We used the transcription factor binding data generated by the ENCODE project, and gene sets from the Molecular Signatures and KEGG databases. Our results showed that 54 percent of the predictions examined have literature support and that REPA's recall is roughly 54 percent. This level of precision is promising as several of REPA's predictions are expected to be novel and can be used to guide new research avenues. In addition, the results of our case studies showed that REPA enhances the interpretation of genome-wide transcription profiling studies by suggesting putative regulators behind the observed transcriptional responses.

GeNET: a web application to explore and share Gene Co-expression Network Analysis data.

Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several computer programs for gene-to-gene network visualization, but these programs have limitations in terms of integrating all the data generated by a GCNA and making these data available online. To facilitate sharing and study of GCNA data, we developed GeNET. For researchers interested in sharing their GCNA data, GeNET provides a convenient interface to upload their data and automatically make it accessible to the public through an online server. For researchers interested in exploring GCNA data published by others, GeNET provides an intuitive online tool to interactively explore GCNA data by genes, gene sets or modules. In addition, GeNET allows users to download all or part of the published data for further computational analysis. To demonstrate the applicability of GeNET, we imported three published GCNA datasets, the largest of which consists of roughly 17,000 genes and 200 conditions. GeNET is available at bengi.cs.mun.ca/genet.

CD24 induces changes to the surface receptors of B cell microvesicles with variable effects on their RNA and protein cargo.

The CD24 cell surface receptor promotes apoptosis in developing B cells, and we recently found that it induces B cells to release plasma membrane-derived, CD24-bearing microvesicles (MVs). Here we have performed a systematic characterization of B cell MVs released from WEHI-231 B lymphoma cells in response to CD24 stimulation. We found that B cells constitutively release MVs of approximately 120 nm, and that CD24 induces an increase in phosphatidylserine-positive MV release. RNA cargo is predominantly comprised of 5S rRNA, regardless of stimulation; however, CD24 causes a decrease in the incorporation of protein coding transcripts. The MV proteome is enriched with mitochondrial and metabolism-related proteins after CD24 stimulation; however, these changes were variable and could not be fully validated by Western blotting. CD24-bearing MVs carry Siglec-2, CD63, IgM, and, unexpectedly, Ter119, but not Siglec-G or MHC-II despite their presence on the cell surface. CD24 stimulation also induces changes in CD63 and IgM expression on MVs that is not mirrored by the changes in cell surface expression. Overall, the composition of these MVs suggests that they may be involved in releasing mitochondrial components in response to pro-apoptotic stress with changes to the surface receptors potentially altering the cell type(s) that interact with the MVs.

Genome-wide identification and characterization of small RNAs in Rhodobacter capsulatus and identification of small RNAs affected by loss of the response regulator CtrA.

Small non-coding RNAs (sRNAs) are involved in the control of numerous cellular processes through various regulatory mechanisms, and in the past decade many studies have identified sRNAs in a multitude of bacterial species using RNA sequencing (RNA-seq). Here, we present the first genome-wide analysis of sRNA sequencing data in Rhodobacter capsulatus, a purple nonsulfur photosynthetic alphaproteobacterium. Using a recently developed bioinformatics approach, sRNA-Detect, we detected 422 putative sRNAs from R. capsulatus RNA-seq data. Based on their sequence similarity to sRNAs in a sRNA collection, consisting of published putative sRNAs from 23 additional bacterial species, and RNA databases, the sequences of 124 putative sRNAs were conserved in at least one other bacterial species; and, 19 putative sRNAs were assigned a predicted function. We bioinformatically characterized all putative sRNAs and applied machine learning approaches to calculate the probability of a nucleotide sequence to be a bona fide sRNA. The resulting quantitative model was able to correctly classify 95.2% of sequences in a validation set. We found that putative cis-targets for antisense and partially overlapping sRNAs were enriched with protein-coding genes involved in primary metabolic processes, photosynthesis, compound binding, and with genes forming part of macromolecular complexes. We performed differential expression analysis to compare the wild type strain to a mutant lacking the response regulator CtrA, an important regulator of gene expression in R. capsulatus, and identified 18 putative sRNAs with differing levels in the two strains. Finally, we validated the existence and expression patterns of four novel sRNAs by Northern blot analysis.

OMARC: An online multimedia application for training health care providers in the assessment of respiratory conditions.

OBJECTIVES: OMARC, a multimedia application designed to support the training of health care providers for the identification of common lung sounds heard in a patient's thorax as part of a health assessment, is described and its positive contribution to user learning is assessed. The main goal of OMARC is to effectively help health-care students become familiar with lung sounds as part of the assessment of respiratory conditions. In addition, the application must be easy to use and accessible to students and practitioners over the internet. SYSTEM DESCRIPTION: OMARC was developed using an online platform to facilitate access to users in remote locations. OMARC's unique contribution as an educational software tool is that it presents a narrative about normal and abnormal lung sounds using interactive multimedia and sample case studies designed by professional health-care providers and educators. Its interface consists of two distinct components: a sounds glossary and a rich multimedia interface which presents clinical case studies and provides access to lung sounds placed on a model of a human torso. OMARC's contents can be extended through the addition of sounds and case studies designed by health-care educators and professionals. VALIDATION AND RESULTS: To validate OMARC and determine its efficacy in improving learning and capture user perceptions about it, we performed a pilot study with ten nursing students. Participants' performance was measured through an evaluation of their ability to identify several normal and adventitious/abnormal sounds prior and after exposure to OMARC. Results indicate that participants are able to better identify different lung sounds, going from an average of 63% (S.D. 18.3%) in the pre-test evaluation to an average of 90% (S.D. of 11.5%) after practising with OMARC. Furthermore, participants indicated in a user satisfaction questionnaire that they found the application helpful, easy to use and that they would recommend it to other persons in their field. CONCLUSIONS: OMARC is an online multimedia application for training health care students in the assessment of respiratory conditions. The software integrates multimedia technology and health-care education concepts to facilitate learning, while being useful and easy to use. Results from a pilot study indicate that OMARC significantly helps to improve the capacity of the users to correctly identify lung sounds for different respiratory conditions. In addition, participants' opinions about OMARC were quite positive: users were likely to recommend the application to other persons in their field and found the application easy to use and helpful to better identify lung sounds.

DETECTION OF BACTERIAL SMALL TRANSCRIPTS FROM RNA-SEQ DATA: A COMPARATIVE ASSESSMENT.

Small non-coding RNAs (sRNAs) are regulatory RNA molecules that have been identified in a multitude of bacterial species and shown to control numerous cellular processes through various regulatory mechanisms. In the last decade, next generation RNA sequencing (RNA-seq) has been used for the genome-wide detection of bacterial sRNAs. Here we describe sRNA-Detect, a novel approach to identify expressed small transcripts from prokaryotic RNA-seq data. Using RNA-seq data from three bacterial species and two sequencing platforms, we performed a comparative assessment of five computational approaches for the detection of small transcripts. We demonstrate that sRNA-Detect improves upon current standalone computational approaches for identifying novel small transcripts in bacteria.

Proteomics analysis of global regulatory cascades involved in clavulanic acid production and morphological development in Streptomyces clavuligerus.

The genus Streptomyces comprises bacteria that undergo a complex developmental life cycle and produce many metabolites of importance to industry and medicine. Streptomyces clavuligerus produces the ß-lactamase inhibitor clavulanic acid, which is used in combination with ß-lactam antibiotics to treat certain ß-lactam resistant bacterial infections. Many aspects of how clavulanic acid production is globally regulated in S. clavuligerus still remains unknown. We conducted comparative proteomics analysis using the wild type strain of S. clavuligerus and two mutants (ΔbldA and ΔbldG), which are defective in global regulators and vary in their ability to produce clavulanic acid. Approximately 33.5 % of the predicted S. clavuligerus proteome was detected and 192 known or putative regulatory proteins showed statistically differential expression levels in pairwise comparisons. Interestingly, the expression of many proteins whose corresponding genes contain TTA codons (predicted to require the bldA tRNA for translation) was unaffected in the bldA mutant.