Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble

As the biomedical impact of small RNAs grows, so does the need to understand competing structural alternatives for regions of functional interest. Suboptimal structure analysis provides significantly more RNA base pairing information than a single minimum free energy prediction. Yet computational enhancements like Boltzmann sampling have not been fully adopted by experimentalists since identifying meaningful patterns in this data can be challenging. Profiling is a novel approach to mining RNA suboptimal structure data which makes the power of ensemble-based analysis accessible in a stable and reliable way. Balancing abstraction and specificity, profiling identifies significant combinations of base pairs which dominate low-energy RNA secondary structures. By design, critical similarities and differences are highlighted, yielding crucial information for molecular biologists. The code is freely available via http://gtfold.sourceforge.net/profiling.html.     

Online database for mosquito (Diptera,Culicidae) occurrence records in French Guiana

A database providing information on mosquito specimens (Arthropoda: Diptera: Culicidae) collected in French Guiana is presented. Field collections were initiated in 2013 under the auspices of the CEnter for the study of Biodiversity in Amazonia (CEBA: http://www.labexceba.fr/en/). This study is part of an ongoing process aiming to understand the distribution of mosquitoes, including vector species, across French Guiana. Occurrences are recorded after each collecting trip in a database managed by the laboratory Evolution et Diversité Biologique (EDB), Toulouse, France. The dataset is updated monthly and is available online. Voucher specimens and their associated DNA are stored at the laboratory Ecologie des Forêts de Guyane (Ecofog), Kourou, French Guiana. The latest version of the dataset is accessible through EDB’s Integrated Publication Toolkit at http://130.120.204.55:8080/ipt/resource.do?r=mosquitoes_of_french_guiana or through the Global Biodiversity Information Facility data portal at http://www.gbif.org/dataset/5a8aa2ad-261c-4f61-a98e-26dd752fe1c5 It can also be viewed through the Guyanensis platform at http://guyanensis.ups-tlse.fr     

Identification of evolutionarily meaningful information within the mammalian RNA editing landscape

A large comparative genomic sequence study has determined the extent of conservation between RNA editing sites within the mammalian evolutionary tree.See related research by Pinto et al., http://genomebiology.com/2014/15/1/R5     

Vfold: A Web Server for RNA Structure and Folding Thermodynamics Prediction

Background

The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies.

Results

The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization.

Conclusions

The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at “http://rna.physics.missouri.edu”.     

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/     

UFold: fast and accurate RNA secondary structure prediction with deep learning

For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.     

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.     

DSIR: Assessing the Design of Highly Potent siRNA by Testing a Set of Cancer-Relevant Target Genes

Chemically synthesized small interfering RNA (siRNA) is a widespread molecular tool used to knock down genes in mammalian cells. However, designing potent siRNA remains challenging. Among tools predicting siRNA efficacy, very few have been validated on endogenous targets in realistic experimental conditions. We previously described a tool to assist efficient siRNA design (DSIR, Designer of siRNA), which focuses on intrinsic features of the siRNA sequence. Here, we evaluated DSIR’s performance by systematically investigating the potency of the siRNA it designs to target ten cancer-related genes. mRNA knockdown was measured by quantitative RT-PCR in cell-based assays, revealing that over 60% of siRNA sequences designed by DSIR silenced their target genes by at least 70%. Silencing efficacy was sustained even when low siRNA concentrations were used. This systematic analysis revealed in particular that, for a subset of genes, the efficiency of siRNA constructs significantly increases when the sequence is located closer to the 5′-end of the target gene coding sequence, suggesting the distance to the 5′-end as a new feature for siRNA potency prediction. A new version of DSIR incorporating these new findings, as well as the list of validated siRNA against the tested cancer genes, has been made available on the web (http://biodev.extra.cea.fr/DSIR).     

dSPRINT: predicting DNA,RNA, ion,peptide and small molecule interaction sites within protein domains

Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties of proteins are frequently analyzed based upon whether they contain one of these domains. To date, however, knowledge of whether and how protein domains interact with ligands has been limited to domains that have been observed in co-crystal structures; this leaves approximately two-thirds of human protein domain families uncharacterized with respect to whether and how they bind DNA, RNA, small molecules, ions and peptides. To fill this gap, we introduce dSPRINT, a novel ensemble machine learning method for predicting whether a domain binds DNA, RNA, small molecules, ions or peptides, along with the positions within it that participate in these types of interactions. In stringent cross-validation testing, we demonstrate that dSPRINT has an excellent performance in uncovering ligand-binding positions and domains. We also apply dSPRINT to newly characterize the molecular functions of domains of unknown function. dSPRINT’s predictions can be transferred from domains to sequences, enabling predictions about the ligand-binding properties of 95% of human genes. The dSPRINT framework and its predictions for 6503 human protein domains are freely available at http://protdomain.princeton.edu/dsprint.     

RNAMotifScan: automatic identification of RNA structural motifs using secondary structural alignment

Recent studies have shown that RNA structural motifs play essential roles in RNA folding and interaction with other molecules. Computational identification and analysis of RNA structural motifs remains a challenging task. Existing motif identification methods based on 3D structure may not properly compare motifs with high structural variations. Other structural motif identification methods consider only nested canonical base-pairing structures and cannot be used to identify complex RNA structural motifs that often consist of various non-canonical base pairs due to uncommon hydrogen bond interactions. In this article, we present a novel RNA structural alignment method for RNA structural motif identification, RNAMotifScan, which takes into consideration the isosteric (both canonical and non-canonical) base pairs and multi-pairings in RNA structural motifs. The utility and accuracy of RNAMotifScan is demonstrated by searching for kink-turn, C-loop, sarcin-ricin, reverse kink-turn and E-loop motifs against a 23S rRNA (PDBid: 1S72), which is well characterized for the occurrences of these motifs. Finally, we search these motifs against the RNA structures in the entire Protein Data Bank and the abundances of them are estimated. RNAMotifScan is freely available at our supplementary website (http://genome.ucf.edu/RNAMotifScan).     

AlexSys: a knowledge-based expert system for multiple sequence alignment construction and analysis

Multiple sequence alignment (MSA) is a cornerstone of modern molecular biology and represents a unique means of investigating the patterns of conservation and diversity in complex biological systems. Many different algorithms have been developed to construct MSAs, but previous studies have shown that no single aligner consistently outperforms the rest. This has led to the development of a number of ‘meta-methods’ that systematically run several aligners and merge the output into one single solution. Although these methods generally produce more accurate alignments, they are inefficient because all the aligners need to be run first and the choice of the best solution is made a posteriori. Here, we describe the development of a new expert system, AlexSys, for the multiple alignment of protein sequences. AlexSys incorporates an intelligent inference engine to automatically select an appropriate aligner a priori, depending only on the nature of the input sequences. The inference engine was trained on a large set of reference multiple alignments, using a novel machine learning approach. Applying AlexSys to a test set of 178 alignments, we show that the expert system represents a good compromise between alignment quality and running time, making it suitable for high throughput projects. AlexSys is freely available from http://alnitak.u-strasbg.fr/∼aniba/alexsys.     

A database for human Y chromosome protein data

The human Y chromosome is the sex determining chromosome. The number of proteins associated with this chromosome is 196 and 107 of the 196 proteins have yet not been characterised. Here, we describe the analysis of these 107 proteins by computing various physicochemical properties using sequence and predicted structural data to elucidate molecular function. We present the derived data in the form a form a database made freely available for download, review, refinement and update.

Availability

http://puratham.googlepages.com/ or http://puratham.googlepages.com/ftpconnection     

Genome analysis of Desulfotomaculum gibsoniae strain GrollT a highly versatile Gram-positive sulfate-reducing bacterium

Desulfotomaculum gibsoniae is a mesophilic member of the polyphyletic spore-forming genus Desulfotomaculum within the family Peptococcaceae. This bacterium was isolated from a freshwater ditch and is of interest because it can grow with a large variety of organic substrates, in particular several aromatic compounds, short-chain and medium-chain fatty acids, which are degraded completely to carbon dioxide coupled to the reduction of sulfate. It can grow autotrophically with H₂ + CO₂ and sulfate and slowly acetogenically with H₂ + CO_2, formate or methoxylated aromatic compounds in the absence of sulfate. It does not require any vitamins for growth. Here, we describe the features of D. gibsoniae strain Groll^T together with the genome sequence and annotation. The chromosome has 4,855,529 bp organized in one circular contig and is the largest genome of all sequenced Desulfotomaculum spp. to date. A total of 4,666 candidate protein-encoding genes and 96 RNA genes were identified. Genes of the acetyl-CoA pathway, possibly involved in heterotrophic growth and in CO₂ fixation during autotrophic growth, are present. The genome contains a large set of genes for the anaerobic transformation and degradation of aromatic compounds, which are lacking in the other sequenced Desulfotomaculum genomes.     

DIACAN: Integrated Database for Antidiabetic and Anticancer Medicinal Plants

Medicinal plants and plant derived molecules are widely used in traditional cultures all over the world and they are becoming large popular among biomedical researchers and pharmaceutical companies as a natural alternative to synthetic medicine. Information related to medicinal plants and herbal drugs accumulated over the ages are scattered and unstructured which make it prudent to develop a curated database for medicinal plants. The Antidiabetic and Anticancer Medicinal Plants Database (DIACAN) aims to collect and provide an integrated platform for plants and phytochemiclas having antidiabetic or anticancer activity.

Availability

http://www.kaubic.in/diacan