Discovering Structural Motifs in miRNA Precursors from the Viridiplantae Kingdom

How bioinformatics resources work with G4 RNAs

Quadruplexes (G4s) are of interest, which increases with the number of identified G4 structures and knowledge about their biomedical potential. These unique motifs form in many organisms, including humans, where their appearance correlates with various diseases. Scientists store and analyze quadruplexes using recently developed bioinformatic tools—many of them focused on DNA structures. With an expanding collection of G4 RNAs, we check how existing tools deal with them. We review all available bioinformatics resources dedicated to quadruplexes and examine their usefulness in G4 RNA analysis. We distinguish the following subsets of resources: databases, tools to predict putative quadruplex sequences, tools to predict secondary structure with quadruplexes and tools to analyze and visualize quadruplex structures. We share the results obtained from processing specially created RNA datasets with these tools.

Contact: mszachniuk@cs.put.poznan.pl

Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

ElTetrado: a tool for identification and classification of tetrads and quadruplexes

Background

Quadruplexes are specific structure motifs occurring, e.g., in telomeres and transcriptional regulatory regions. Recent discoveries confirmed their importance in biomedicine and led to an intensified examination of their properties. So far, the study of these motifs has focused mainly on the sequence and the tertiary structure, and concerned canonical structures only. Whereas, more and more non-canonical quadruplex motifs are being discovered.

Results

Here, we present ElTetrado, a software that identifies quadruplexes (composed of guanine- and other nucleobase-containing tetrads) in nucleic acid structures and classifies them according to the recently introduced ONZ taxonomy. The categorization is based on the secondary structure topology of quadruplexes and their component tetrads. It supports the analysis of canonical and non-canonical motifs. Besides the class recognition, ElTetrado prepares a dot-bracket and graphical representations of the secondary structure, which reflect the specificity of the quadruplex’s structure topology. It is implemented as a freely available, standalone application, available at https://github.com/tzok/eltetrado.

Conclusions

The proposed software tool allows to identify and classify tetrads and quadruplexes based on the topology of their secondary structures. It complements existing approaches focusing on the sequence and 3D structure.

Identification of Structural Motifs Using Networks of Hydrogen-Bonded Base Interactions in RNA Crystallographic Structures

RNA structural motifs can be identified using methods that analyze base–base interactions and the conformation of a structure’s backbone; however, these approaches do not necessarily take into consideration the hydrogen bonds that connect the bases or the networks of inter-connected hydrogen-bonded bases that are found in RNA structures. Large clusters of RNA bases that are tightly inter-connected by a network of hydrogen bonds are expected to be stable and relatively rigid substructures. Such base arrangements could therefore be present as structural motifs in RNA structures, especially when there is a requirement for a highly stable support platform or substructure to ensure the correct folding and spatial maintenance of functional sites that partake in catalysis or binding interactions. In order to test this hypothesis, we conducted a search in available RNA crystallographic structures in the Protein Data Bank database using queries that searched for profiles of bases inter-connected by hydrogen bonds. This method of searching does not require to have prior knowledge of the arrangement being searched. Our search results identified two clusters of six bases that are inter-connected by a network of hydrogen bonds. These arrangements of base sextuples have never been previously reported, thus making this the first report that proposes them as novel RNA tertiary motifs.

Evolutionary and Comparative Expression Analyses of TCP Transcription Factor Gene Family in Land Plants

The plant-specific Teosinte-branched 1/Cycloidea/Proliferating (TCP) transcription factor genes are involved in plants' development, hormonal pathways, and stress response but their evolutionary history is uncertain. The genome-wide analysis performed here for 47 plant species revealed 535 TCP candidates in terrestrial plants and none in aquatic plants, and that TCP family genes originated early in the history of land plants. Phylogenetic analysis divided the candidate genes into Classes I and II, and Class II was further divided into CYCLOIDEA (CYC) and CINCINNATA (CIN) clades; CYC is more recent and originated from CIN in angiosperms. Protein architecture, intron pattern, and sequence characteristics were conserved in each class or clade supporting this classification. The two classes significantly expanded through whole-genome duplication during evolution. Expression analysis revealed the conserved expression of TCP genes from lower to higher plants. The expression patterns of Class I and CIN genes in different stages of the same tissue revealed their function in plant development and their opposite effects in the same biological process. Interaction network analysis showed that TCP proteins tend to form protein complexes, and their interaction networks were conserved during evolution. These results contribute to further functional studies on TCP family genes.

Molecular Computing and Bioinformatics

Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of-and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions-even trillions-of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.

RNApolis: Computational Platform for RNA Structure Analysis

In the 1970s, computer scientists began to engage in research in the field of structural biology. The first structural databases, as well as models and methods supporting the analysis of biomolecule structures, started to be created. RNA was put at the centre of scientific interest quite late. However, more and more methods dedicated to this molecule are currently being developed. This paper presents RNApolis - a new computing platform, which offers access to seven bioinformatic tools developed to support the RNA structure study. The set of tools include a structural database and systems for predicting, modelling, annotating and evaluating the RNA structure. RNApolis supports research at different structural levels and allows the discovery, establishment, and validation of relationships between the primary, secondary and tertiary structure of RNAs. The platform is freely available at http://rnapolis.pl