RNA secondary structure prediction from multi-aligned sequences

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.Comment: A preprint of an invited review manuscript that will be published in a chapter of the book `Methods in Molecular Biology'. Note that this version of the manuscript may differ from the published versio

Synthesis and characterization of ZnO nanopowder by non-basic

Nanocrystalline ZnO particles were prepared from methanolic solutions of zinc acetate dihydrate without using base such as NaOH or LiOH through a colloid process carried out at a low temperature of 60 o C. The precipitate obtained after 12-72h contained ZnO, covered with polymeric species of zinc hydroxo acetate. The reaction course was studied by mass spectrometry means. To complete the hydrolysis process, up to pure ZnO, it was necessarily to reflux the white precipitate separate from methanolic solution, in water at 80 o C. We found that reaction time in the presence of methanol primarily influenced the size of the particles, while the reaction time in the presence of water mainly influenced the ZnO purity

Bromodomains in Human-Immunodeficiency Virus-Associated Neurocognitive Disorders: A Model of Ferroptosis-Induced Neurodegeneration

Human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND) comprise a group of illnesses marked by memory and behavioral dysfunction that can occur in up to 50% of HIV patients despite adequate treatment with combination antiretroviral drugs. Iron dyshomeostasis exacerbates HIV-1 infection and plays a major role in Alzheimer’s disease pathogenesis. In addition, persons living with HIV demonstrate a high prevalence of neurodegenerative disorders, indicating that HAND provides a unique opportunity to study ferroptosis in these conditions. Both HIV and combination antiretroviral drugs increase the risk of ferroptosis by augmenting ferritin autophagy at the lysosomal level. As many viruses and their proteins exit host cells through lysosomal exocytosis, ferroptosis-driving molecules, iron, cathepsin B and calcium may be released from these organelles. Neurons and glial cells are highly susceptible to ferroptosis and neurodegeneration that engenders white and gray matter damage. Moreover, iron-activated microglia can engage in the aberrant elimination of viable neurons and synapses, further contributing to ferroptosis-induced neurodegeneration. In this mini review, we take a closer look at the role of iron in the pathogenesis of HAND and neurodegenerative disorders. In addition, we describe an epigenetic compensatory system, comprised of bromodomain-containing protein 4 (BRD4) and microRNA-29, that may counteract ferroptosis by activating cystine/glutamate antiporter, while lowering ferritin autophagy and iron regulatory protein-2. We also discuss potential interventions for lysosomal fitness, including ferroptosis blockers, lysosomal acidification, and cathepsin B inhibitors to achieve desirable therapeutic effects of ferroptosis-induced neurodegeneration

SYNTHESIS, MECHANICAL AND STRUCTURAL PROPERTIES AND BIOLOGICAL ACTIVITY OF SOME NANOSTRUCTURED BONE SCAFFOLDS

Three new biodegradable and bioactive scaffolds based on hydroxyapatite (HAP), β tricalcium phosphate (β-TCP) and a composite HAP+β-TCP are studied in this paper. Their mechanical, structural properties and long term behaviour in simulated body fluid (SBF) were analysed. Also, these scaffolds were subjected to in vitro evaluation of cell behaviour. The open porosity from 16.41% to 34.26% it resulted. The compression resistance (2-3 MPa) is very close to the cancellous bone. It resulted that simultaneously with the dissolution, on the surface of materials were deposited calcium and phosphorous ions, namely, these materials are favourable to the bone formation. The scaffold morphology showed a dense polycrystalline structure with cavities (porosity), junctions and interconnections that can facilitate the cell proliferation. SEM micrographs revealed that, after 2100 immersion hours in SBF, the scaffolds coated with a layer of micro-sized grains. The in vitro results proved that hFOB 1.19 cells are able to attach, migrate in the interior of scaffolds, proliferate, synthesize and extracellularly assemble ECM molecules

RNAalifold: improved consensus structure prediction for RNA alignments

<p>Abstract</p> <p>Background</p> <p>The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.</p> <p>Results</p> <p>We show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets.</p> <p>Conclusion</p> <p>The new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.</p

Employing machine learning for reliable miRNA target identification in plants

<p>Abstract</p> <p>Background</p> <p>miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.</p> <p>Result</p> <p>In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like <it>Arabidopsis</it>, <it>Medicago</it>, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version.</p> <p>Conclusion</p> <p>A machine learning multivariate feature tool has been implemented in parallel and locally installable form, for plant miRNA target identification. The performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.</p

PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences

Motivation: Predicting RNA–RNA interactions is essential for determining the function of putative non-coding RNAs. Existing methods for the prediction of interactions are all based on single sequences. Since comparative methods have already been useful in RNA structure determination, we assume that conserved RNA–RNA interactions also imply conserved function. Of these, we further assume that a non-negligible amount of the existing RNA–RNA interactions have also acquired compensating base changes throughout evolution. We implement a method, PETcofold, that can take covariance information in intra-molecular and inter-molecular base pairs into account to predict interactions and secondary structures of two multiple alignments of RNA sequences

A Combinatorial Framework for Designing (Pseudoknotted) RNA Algorithms

We extend an hypergraph representation, introduced by Finkelstein and Roytberg, to unify dynamic programming algorithms in the context of RNA folding with pseudoknots. Classic applications of RNA dynamic programming energy minimization, partition function, base-pair probabilities...) are reformulated within this framework, giving rise to very simple algorithms. This reformulation allows one to conceptually detach the conformation space/energy model -- captured by the hypergraph model -- from the specific application, assuming unambiguity of the decomposition. To ensure the latter property, we propose a new combinatorial methodology based on generating functions. We extend the set of generic applications by proposing an exact algorithm for extracting generalized moments in weighted distribution, generalizing a prior contribution by Miklos and al. Finally, we illustrate our full-fledged programme on three exemplary conformation spaces (secondary structures, Akutsu's simple type pseudoknots and kissing hairpins). This readily gives sets of algorithms that are either novel or have complexity comparable to classic implementations for minimization and Boltzmann ensemble applications of dynamic programming