sRNATarBase: A comprehensive database of bacterial sRNA targets verified by experiments

Bacterial sRNAs are an emerging class of small regulatory RNAs, 40–500 nt in length, which play a variety of important roles in many biological processes through binding to their mRNA or protein targets. A comprehensive database of experimentally confirmed sRNA targets would be helpful in understanding sRNA functions systematically and provide support for developing prediction models. Here we report on such a database—sRNATarBase. The database holds 138 sRNA–target interactions and 252 noninteraction entries, which were manually collected from peer-reviewed papers. The detailed information for each entry, such as supporting experimental protocols, BLAST-based phylogenetic analysis of sRNA–mRNA target interaction in closely related bacteria, predicted secondary structures for both sRNAs and their targets, and available binding regions, is provided as accurately as possible. This database also provides hyperlinks to other databases including GenBank, SWISS-PROT, and MPIDB. The database is available from the web page http://ccb.bmi.ac.cn/srnatarbase/.     

An integrated approach for finding overlooked genes in Shigella

Background

The completion of numerous genome sequences introduced an era of whole-genome study. However, many genes are missed during genome annotation, including small RNAs (sRNAs) and small open reading frames (sORFs). In order to improve genome annotation, we aimed to identify novel sRNAs and sORFs in Shigella, the principal etiologic agents of bacillary dysentery.

Methodology/Principal Findings

We identified 64 sRNAs in Shigella, which were experimentally validated in other bacteria based on sequence conservation. We employed computer-based and tiling array-based methods to search for sRNAs, followed by RT-PCR and northern blots, to identify nine sRNAs in Shigella flexneri strain 301 (Sf301) and 256 regions containing possible sRNA genes. We found 29 candidate sORFs using bioinformatic prediction, array hybridization and RT-PCR verification. We experimentally validated 557 (57.9%) DOOR operon predictions in the chromosomes of Sf301 and 46 (76.7%) in virulence plasmid.We found 40 additional co-expressed gene pairs that were not predicted by DOOR.

Conclusions/Significance

We provide an updated and comprehensive annotation of the Shigella genome. Our study increased the expected numbers of sORFs and sRNAs, which will impact on future functional genomics and proteomics studies. Our method can be used for large scale reannotation of sRNAs and sORFs in any microbe with a known genome sequence.     

Assessing the utility of thermodynamic features for microRNA target prediction under relaxed seed and no conservation requirements

Background

Many computational microRNA target prediction tools are focused on several key features, including complementarity to 5′seed of miRNAs and evolutionary conservation. While these features allow for successful target identification, not all miRNA target sites are conserved and adhere to canonical seed complementarity. Several studies have propagated the use of energy features of mRNA:miRNA duplexes as an alternative feature. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement.

Methodology/Principal Findings

We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide sites of AGO protein interaction. This trend is confirmed on datasets that assay the effect of miRNAs on mRNA and protein expression changes, and a simple linear regression model leads to significant correlation of predicted versus observed expression change. Compared to 6-mer seed matches as baseline, application of our energy-based model leads to ∼3–5-fold enrichment on highly down-regulated targets, and allows for prediction of strictly imperfect targets with enrichment above baseline.

Conclusions/Significance

In conclusion, our results indicate significant promise for energy-based miRNA target prediction that includes a broader range of targets without having to use conservation or impose stringent seed match rules.     

The diversity of small non-coding RNAs in the diatom Phaeodactylum tricornutum

Background

Marine diatoms constitute a major component of eukaryotic phytoplankton and stand at the crossroads of several evolutionary lineages. These microalgae possess peculiar genomic features and novel combinations of genes acquired from bacterial, animal and plant ancestors. Furthermore, they display both DNA methylation and gene silencing activities. Yet, the biogenesis and regulatory function of small RNAs (sRNAs) remain ill defined in diatoms.

Results

Here we report the first comprehensive characterization of the sRNA landscape and its correlation with genomic and epigenomic information in Phaeodactylum tricornutum. The majority of sRNAs is 25 to 30 nt-long and maps to repetitive and silenced Transposable Elements marked by DNA methylation. A subset of this population also targets DNA methylated protein-coding genes, suggesting that gene body methylation might be sRNA-driven in diatoms. Remarkably, 25-30 nt sRNAs display a well-defined and unprecedented 180 nt-long periodic distribution at several highly methylated regions that awaits characterization. While canonical miRNAs are not detectable, other 21-25 nt sRNAs of unknown origin are highly expressed. Besides, non-coding RNAs with well-described function, namely tRNAs and U2 snRNA, constitute a major source of 21-25 nt sRNAs and likely play important roles under stressful environmental conditions.

Conclusions

P. tricornutum has evolved diversified sRNA pathways, likely implicated in the regulation of largely still uncharacterized genetic and epigenetic processes. These results uncover an unexpected complexity of diatom sRNA population and previously unappreciated features, providing new insights into the diversification of sRNA-based processes in eukaryotes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-698) contains supplementary material, which is available to authorized users.     

SPPS: a sequence-based method for predicting probability of protein-protein interaction partners

Background

The molecular network sustained by different types of interactions among proteins is widely manifested as the fundamental driving force of cellular operations. Many biological functions are determined by the crosstalk between proteins rather than by the characteristics of their individual components. Thus, the searches for protein partners in global networks are imperative when attempting to address the principles of biology.

Results

We have developed a web-based tool “Sequence-based Protein Partners Search” (SPPS) to explore interacting partners of proteins, by searching over a large repertoire of proteins across many species. SPPS provides a database containing more than 60,000 protein sequences with annotations and a protein-partner search engine in two modes (Single Query and Multiple Query). Two interacting proteins of human FBXO6 protein have been found using the service in the study. In addition, users can refine potential protein partner hits by using annotations and possible interactive network in the SPPS web server.

Conclusions

SPPS provides a new type of tool to facilitate the identification of direct or indirect protein partners which may guide scientists on the investigation of new signaling pathways. The SPPS server is available to the public at http://mdl.shsmu.edu.cn/SPPS/.     

Prediction of drought-resistant genes in Arabidopsis thaliana using SVM-RFE

Background

Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits.

Methodology

In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination) feature selection method for the prediction. To address small sample sizes, we developed a modified approach for SVM-RFE by using bootstrapping and leave-one-out cross-validation. We also expanded our study to predict genes involved in water susceptibility.

Conclusions

We analyzed the top 10 genes predicted to be involved in water tolerance. Seven of them are connected to known biological processes in drought resistance. We also analyzed the top 100 genes in terms of their biological functions. Our study shows that the SVM-RFE method is a highly promising method in analyzing plant microarray data for studying genotype-phenotype relationships. The software is freely available with source code at http://ccst.jlu.edu.cn/JCSB/RFET/.     

Internet-based CBT for depression with and without telephone tracking in a national helpline: randomised controlled trial

Background

Telephone helplines are frequently and repeatedly used by individuals with chronic mental health problems and web interventions may be an effective tool for reducing depression in this population.

Aim

To evaluate the effectiveness of a 6 week, web-based cognitive behaviour therapy (CBT) intervention with and without proactive weekly telephone tracking in the reduction of depression in callers to a helpline service.

Method

155 callers to a national helpline service with moderate to high psychological distress were recruited and randomised to receive either Internet CBT plus weekly telephone follow-up; Internet CBT only; weekly telephone follow-up only; or treatment as usual.

Results

Depression was lower in participants in the web intervention conditions both with and without telephone tracking compared to the treatment as usual condition both at post intervention and at 6 month follow-up. Telephone tracking provided by a lay telephone counsellor did not confer any additional advantage in terms of symptom reduction or adherence.

Conclusions

A web-based CBT program is effective both with and without telephone tracking for reducing depression in callers to a national helpline.

Trial Registration

Controlled-Trials.com ISRCTN93903959     

miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets

Background

Many prediction tools for microRNA (miRNA) targets have been developed, but inconsistent predictions were observed across multiple algorithms, which can make further analysis difficult. Moreover, the nomenclature of human miRNAs changes rapidly. To address these issues, we developed a web-based system, miRSystem, for converting queried miRNAs to the latest annotation and predicting the function of miRNA by integrating miRNA target gene prediction and function/pathway analyses.

Results

First, queried miRNA IDs were converted to the latest annotated version to prevent potential conflicts resulting from multiple aliases. Next, by combining seven algorithms and two validated databases, potential gene targets of miRNAs and their functions were predicted based on the consistency across independent algorithms and observed/expected ratios. Lastly, five pathway databases were included to characterize the enriched pathways of target genes through bootstrap approaches. Based on the enriched pathways of target genes, the functions of queried miRNAs could be predicted.

Conclusions

MiRSystem is a user-friendly tool for predicting the target genes and their associated pathways for many miRNAs simultaneously. The web server and the documentation are freely available at http://mirsystem.cgm.ntu.edu.tw/.     

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria

Background

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed.

Methods

A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein''s critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed.

Results

The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets.

Conclusions/Significance

UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.     

Identification and Comparative Analysis of the Tegillarca granosa Haemocytes MicroRNA Transcriptome in Response to Cd Using a Deep Sequencing Approach

Background

MicroRNAs (miRNAs) are endogenous non-coding small RNAs (sRNAs) that can base pair with their target mRNAs, which represses their translation or induces their degradation in various biological processes. To identify miRNAs regulated by heavy metal stress, we constructed two sRNA libraries for the blood clam Tegillarca granosa: one for organisms exposed to toxic levels of cadmium (Cd) and one for a control group.

Results

Sequencing of the two libraries and subsequent analysis revealed 215 conserved and 39 new miRNAs. Most of the new miRNAs in T. granosa were up- or down-regulated in response to Cd exposure. There were significant differences in expression between the Cd and control groups for 16 miRNAs. Of these, five miRNAs were significantly up-regulated and 11 were significantly down-regulated in the Cd stress library. Potential targets were predicted for the 16 differential miRNAs in pre-miRNAs identified according to sequence homology. Some of the predicted miRNA targets are associated with regulation of the response to stress induced by heavy metals. Five differentially expressed miRNAs (Tgr-nmiR-8, Tgr-nmiR-21, Tgr-miR-2a, Tgr-miR-10a-5p, and Tgr-miR-184b) were validated by qRT-PCR.

Conclusion

Our study is the first large-scale identification of miRNAs in T. granosa haemocytes. Our findings suggest that some miRNAs and their target genes and pathways may play critical roles in the responses of this species to environmental heavy metal stresses.     

Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning

Motivation

The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences.

Methods

In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences.

Results

Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction.

Availability

The web server and datasets are freely available at http://bcell.whu.edu.cn.