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/.     

sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization

Background

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.

Results

Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.

Conclusions

sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/.     

A Computational Framework to Infer Human Disease-Associated Long Noncoding RNAs

As a major class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been implicated in various critical biological processes. Accumulating researches have linked dysregulations and mutations of lncRNAs to a variety of human disorders and diseases. However, to date, only a few human lncRNAs have been associated with diseases. Therefore, it is very important to develop a computational method to globally predict potential associated diseases for human lncRNAs. In this paper, we developed a computational framework to accomplish this by combining human lncRNA expression profiles, gene expression profiles, and human disease-associated gene data. Applying this framework to available human long intergenic noncoding RNAs (lincRNAs) expression data, we showed that the framework has reliable accuracy. As a result, for non-tissue-specific lincRNAs, the AUC of our algorithm is 0.7645, and the prediction accuracy is about 89%. This study will be helpful for identifying novel lncRNAs for human diseases, which will help in understanding the roles of lncRNAs in human diseases and facilitate treatment. The corresponding codes for our method and the predicted results are all available at http://asdcd.amss.ac.cn/MingXiLiu/lncRNA-disease.html.     

CRAC: an integrated approach to the analysis of RNA-seq reads

A large number of RNA-sequencing studies set out to predict mutations, splice junctions or fusion RNAs. We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions. CRAC is available at http://crac.gforge.inria.fr.     

The small nucleolar ribonucleoprotein (snoRNP) database

Small nucleolar ribonucleoproteins (snoRNPs) are widely studied and characterized as guide RNAs for sequence-specific 2′-O-ribose methylation and psuedouridylation of ribosomal RNAs. In addition, snoRNAs have also been shown to interact with some tRNAs and direct alternative splicing in mRNA biogenesis. Recent advances in bioinformatics have resulted in new algorithms able to rapidly identify noncoding RNAs generally and snoRNAs specifically in genomic and metagenomic sequences, resulting in a rapid increase in the number and diversity of identified snoRNA sequences. The snoRNP database is a web-based collection of snoRNA and snoRNA-associated protein sequences from a wide range of species. The database currently contains 8994 snoRNA sequences from Bacteria, Archaea, and Eukaryotes and 589 snoRNA-associated protein sequences. The snoRNP database can be found at: http://evolveathome.com/snoRNA/snoRNA.php.     

MetaMetaDB: A Database and Analytic System for Investigating Microbial Habitability

MetaMetaDB (http://mmdb.aori.u-tokyo.ac.jp/) is a database and analytic system for investigating microbial habitability, i.e., how a prokaryotic group can inhabit different environments. The interaction between prokaryotes and the environment is a key issue in microbiology because distinct prokaryotic communities maintain distinct ecosystems. Because 16S ribosomal RNA (rRNA) sequences play pivotal roles in identifying prokaryotic species, a system that comprehensively links diverse environments to 16S rRNA sequences of the inhabitant prokaryotes is necessary for the systematic understanding of the microbial habitability. However, existing databases are biased to culturable prokaryotes and exhibit limitations in the comprehensiveness of the data because most prokaryotes are unculturable. Recently, metagenomic and 16S rRNA amplicon sequencing approaches have generated abundant 16S rRNA sequence data that encompass unculturable prokaryotes across diverse environments; however, these data are usually buried in large databases and are difficult to access. In this study, we developed MetaMetaDB (Meta-Metagenomic DataBase), which comprehensively and compactly covers 16S rRNA sequences retrieved from public datasets. Using MetaMetaDB, users can quickly generate hypotheses regarding the types of environments a prokaryotic group may be adapted to. We anticipate that MetaMetaDB will improve our understanding of the diversity and evolution of prokaryotes.     

PDBsum extras: SARS‐CoV‐2 and AlphaFold models

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS‐CoV‐2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.     

ASDCD: Antifungal Synergistic Drug Combination Database

Finding effective drugs to treat fungal infections has important clinical significance based on high mortality rates, especially in an immunodeficient population. Traditional antifungal drugs with single targets have been reported to cause serious side effects and drug resistance. Nowadays, however, drug combinations, particularly with respect to synergistic interaction, have attracted the attention of researchers. In fact, synergistic drug combinations could simultaneously affect multiple subpopulations, targets, and diseases. Therefore, a strategy that employs synergistic antifungal drug combinations could eliminate the limitations noted above and offer the opportunity to explore this emerging bioactive chemical space. However, it is first necessary to build a powerful database in order to facilitate the analysis of drug combinations. To address this gap in our knowledge, we have built the first Antifungal Synergistic Drug Combination Database (ASDCD), including previously published synergistic antifungal drug combinations, chemical structures, targets, target-related signaling pathways, indications, and other pertinent data. Its current version includes 210 antifungal synergistic drug combinations and 1225 drug-target interactions, involving 105 individual drugs from more than 12,000 references. ASDCD is freely available at http://ASDCD.amss.ac.cn.     

Mammalian Mitochondrial ncRNA Database

Mammalian Mitochondrial ncRNA is a web-based database, which provides specific information on non-coding RNA in mammals. This database includes easy searching, comparing with BLAST and retrieving information on predicted structure and its function about mammalian ncRNAs.

Availability

The database is available for free at http://www.iitm.ac.in/bioinfo/mmndb/     

SECISearch3 and Seblastian: new tools for prediction of SECIS elements and selenoproteins

Selenoproteins are proteins containing an uncommon amino acid selenocysteine (Sec). Sec is inserted by a specific translational machinery that recognizes a stem-loop structure, the SECIS element, at the 3′ UTR of selenoprotein genes and recodes a UGA codon within the coding sequence. As UGA is normally a translational stop signal, selenoproteins are generally misannotated and designated tools have to be developed for this class of proteins. Here, we present two new computational methods for selenoprotein identification and analysis, which we provide publicly through the web servers at http://gladyshevlab.org/SelenoproteinPredictionServer or http://seblastian.crg.es. SECISearch3 replaces its predecessor SECISearch as a tool for prediction of eukaryotic SECIS elements. Seblastian is a new method for selenoprotein gene detection that uses SECISearch3 and then predicts selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able to both identify known selenoproteins and predict new selenoproteins. By applying these tools to diverse eukaryotic genomes, we provide a ranked list of newly predicted selenoproteins together with their annotated cysteine-containing homologues. An analysis of a representative candidate belonging to the AhpC family shows how the use of Sec in this protein evolved in bacterial and eukaryotic lineages.     

CoryneBase: Corynebacterium Genomic Resources and Analysis Tools at Your Fingertips

Corynebacteria are used for a wide variety of industrial purposes but some species are associated with human diseases. With increasing number of corynebacterial genomes having been sequenced, comparative analysis of these strains may provide better understanding of their biology, phylogeny, virulence and taxonomy that may lead to the discoveries of beneficial industrial strains or contribute to better management of diseases. To facilitate the ongoing research of corynebacteria, a specialized central repository and analysis platform for the corynebacterial research community is needed to host the fast-growing amount of genomic data and facilitate the analysis of these data. Here we present CoryneBase, a genomic database for Corynebacterium with diverse functionality for the analysis of genomes aimed to provide: (1) annotated genome sequences of Corynebacterium where 165,918 coding sequences and 4,180 RNAs can be found in 27 species; (2) access to comprehensive Corynebacterium data through the use of advanced web technologies for interactive web interfaces; and (3) advanced bioinformatic analysis tools consisting of standard BLAST for homology search, VFDB BLAST for sequence homology search against the Virulence Factor Database (VFDB), Pairwise Genome Comparison (PGC) tool for comparative genomic analysis, and a newly designed Pathogenomics Profiling Tool (PathoProT) for comparative pathogenomic analysis. CoryneBase offers the access of a range of Corynebacterium genomic resources as well as analysis tools for comparative genomics and pathogenomics. It is publicly available at http://corynebacterium.um.edu.my/.     

Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.