Computational Approach For Prediction Research Articles

Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites.

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

PMSFF: Improved Protein Binding Residues Prediction through Multi-Scale Sequence-Based Feature Fusion Strategy.

Proteins perform different biological functions through binding with various molecules which are mediated by a few key residues and accurate prediction of such protein binding residues (PBRs) is crucial for understanding cellular processes and for designing new drugs. Many computational prediction approaches have been proposed to identify PBRs with sequence-based features. However, these approaches face two main challenges: (1) these methods only concatenate residue feature vectors with a simple sliding window strategy, and (2) it is challenging to find a uniform sliding window size suitable for learning embeddings across different types of PBRs. In this study, we propose one novel framework that could apply multiple types of PBRs Prediciton task through Multi-scale Sequence-based Feature Fusion (PMSFF) strategy. Firstly, PMSFF employs a pre-trained language model named ProtT5, to encode amino acid residues in protein sequences. Then, it generates multi-scale residue embeddings by applying multi-size windows to capture effective neighboring residues and multi-size kernels to learn information across different scales. Additionally, the proposed model treats protein sequences as sentences, employing a bidirectional GRU to learn global context. We also collect benchmark datasets encompassing various PBRs types and evaluate our PMSFF approach to these datasets. Compared with state-of-the-art methods, PMSFF demonstrates superior performance on most PBRs prediction tasks.

MGCN-PolyA: An Integrated Computational Framework for Predicting Poly(A) Signals with Multiscale-gated Convolutional Networks

Background: The accurate recognition of the polyadenylation signal (PAS) from DNA sequences is essential for understanding gene transcriptional regulation. A variety of machine learning-based computational methods have been developed to predict PAS in recent years; however, their performance and their generalization ability are unsatisfactory. It is highly desirable to design more preferable computational approaches for PAS prediction. Methods: In this work, we developed an integrated framework MGCN-PolyA for PAS prediction across four species, including Homo sapiens, Bos taurus, Mus musculus, and Drosophila melanogaster. MGCN-Poly(A) benefits from the diversity of feature engineering and the effectiveness of the model architecture. We combined features from different perspectives, such as word embedding, One-hot encoding, K-mer frequency, and Enhanced Nucleic Acid Composition (ENAC), which complement each other and provide rich and comprehensive information for model learning. In model architecture, MGCN-Poly(A) leverages a two-channel multi-scale gated convolutional network to effectively learn high-level feature representations at different scales, and then combines the statistical features to predict PAS using random forest algorithm. These designs not only speed up network training, but also improves the generalization ability Results: The benchmarking experiments on the independent test datasets demonstrate that MGCNPolyA outperforms other state-of-the-art algorithms in identifying PAS. MGCN-PolyA has the highest accuracy on all test datasets, and its excellent performance on cross-species validation also demonstrates the robustness of our model. Conclusion: Extracting features from different perspectives is important for PAS recognition, and the integration of DNNs and shallow machine learning algorithms can improve the model performance.

PLM-T3SE: Accurate Prediction of Type III Secretion Effectors Using Protein Language Model Embeddings.

The Type III secretion effectors (T3SEs) are bacterial proteins synthesized by Gram-negative pathogens and delivered into host cells via the Type III secretion system (T3SS). These effectors usually play a pivotal role in the interactions between bacteria and hosts. Hence, the precise identification of T3SEs aids researchers in exploring the pathogenic mechanisms of bacterial infections. Since the diversity and complexity of T3SE sequences often make traditional experimental methods time-consuming, it is imperative to explore more efficient and convenient computational approaches for T3SE prediction. Inspired by the promising potential exhibited by pre-trained language models in protein recognition tasks, we proposed a method called PLM-T3SE that utilizes protein language models (PLMs) for effective recognition of T3SEs. First, we utilized PLM embeddings and evolutionary features from the position-specific scoring matrix (PSSM) profiles to transform protein sequences into fixed-length vectors for model training. Second, we employed the extreme gradient boosting (XGBoost) algorithm to rank these features based on their importance. Finally, a MLP neural network model was used to predict T3SEs based on the selected optimal feature set. Experimental results from the cross-validation and independent test demonstrated that our model exhibited superior performance compared to the existing models. Specifically, our model achieved an accuracy of 98.1%, which is 1.8%-42.4% higher than the state-of-the-art predictors based on the same independent data set test. These findings highlight the superiority of the PLM-T3SE and the remarkable characterization ability of PLM embeddings for T3SE prediction.

BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe2O3-based ฺBCLpro combining steam gasification for H2 production. Aspen Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model. A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H2 yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. Findings highlight the CB algorithm's superior performance, achieving up to 98% predictive accuracy, with carbon content, reducer temperature, and Fe2O3/Al2O3 mass ratio identified as crucial features. The algorithm has been developed into a user-friendly tool, BCLH2Pro, accessible via a web server. This tool is designed to assist in reducing costs, optimizing biomass selection, and planning operational conditions to maximize H2 yield in BCLpro systems. Access to the tool can be obtained through the following link: http://bclh2pro.pythonanywhere.com/.

PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions, and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel miRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3’-untranslated regions (3’-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable, and user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

Phase-field modeling of fracture for ferromagnetic materials through Maxwell’s equation

Electro-active materials are classified as electrostrictive and piezoelectric materials. They deform under the action of an external electric field. Piezoelectric material, as a special class of active materials, can produce an internal electric field when subjected to mechanical stress or strain. In return, there is the converse piezoelectric response, which expresses the induction of the mechanical deformation in the material when it is subjected to the application of the electric field. This work presents a variational-based computational modeling approach for failure prediction of ferromagnetic materials. In order to solve this problem, a coupling between magnetostriction and mechanics is modeled, then the fracture mechanism in ferromagnetic materials is investigated. Furthermore, the failure mechanics of ferromagnetic materials under the magnetostrictive effects is studied based on a variational phase-field model of fracture. Phase-field fracture is numerically challenging since the energy functional may admit several local minima, imposing the global irreversibility of the fracture field and dependency of regularization parameters related discretization size. Here, the failure behavior of a magnetoelastic solid body is formulated based on the Helmholtz free energy function, in which the strain tensor, the magnetic induction vector, and the crack phase-field are introduced as state variables. This coupled formulation leads to a continuity equation for the magnetic vector potential through well-known Maxwell’s equations. Hence, the energetic crack driving force is governed by the coupled magneto-mechanical effects under the magneto-static state. Several numerical results substantiate our developments.

MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.

Drug-target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target prediction have received much attention and made many advances, they are still a challenging task that requires further research. The main challenges are manifested as follows: (i) most graph neural network-based methods only consider the information of the first-order neighboring nodes (drug and target) in the graph, without learning deeper and richer structural features from the higher-order neighboring nodes. (ii) Existing methods do not consider both the sequence and structural features of drugs and targets, and each method is independent of each other, and cannot combine the advantages of sequence and structural features to improve the interactive learning effect. To address the above challenges, a Multi-view Integrated learning Network that integrates Deep learning and Graph Learning (MINDG) is proposed in this study, which consists of the following parts: (i) a mixed deep network is used to extract sequence features of drugs and targets, (ii) a higher-order graph attention convolutional network is proposed to better extract and capture structural features, and (iii) a multi-view adaptive integrated decision module is used to improve and complement the initial prediction results of the above two networks to enhance the prediction performance. We evaluate MINDG on two dataset and show it improved DTI prediction performance compared to state-of-the-art baselines. https://github.com/jnuaipr/MINDG.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction.

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

High-throughput quantitation of protein-RNA UV-crosslinking efficiencies as a predictive tool for high-confidence identification of RNA-binding proteins.

UV-crosslinking has proven to be an invaluable tool for the identification of RNA-protein interactomes. The paucity of methods for distinguishing background from bona fide RNA-protein interactions, however, makes attribution of RNA-binding function on UV-crosslinking alone challenging. To address this need, we previously reported an RNA-binding protein (RBP) confidence scoring metric (RCS), incorporating both signal-to-noise (S:N) and protein abundance determinations to distinguish high- and low-confidence candidate RBPs. Although RCS has utility, we sought a direct metric for quantification and comparative evaluation of protein-RNA interactions. Here we propose the use of protein-specific UV-crosslinking efficiency (%CL), representing the molar fraction of a protein that is crosslinked to RNA, for functional evaluation of candidate RBPs. Application to the HeLa RNA interactome yielded %CL values for 1097 proteins. Remarkably, %CL values span over five orders of magnitude. For the HeLa RNA interactome, %CL values comprise a range from high efficiency, high specificity interactions, e.g., the Elav protein HuR and the Pumilio homolog Pum2, with %CL values of 45.9 and 24.2, respectively, to very low efficiency and specificity interactions, for example, the metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and alpha-enolase, with %CL values of 0.0016, 0.006, and 0.008, respectively. We further extend the utility of %CL through prediction of protein domains and classes with known RNA-binding functions, thus establishing it as a useful metric for RNA interactome analysis. We anticipate that this approach will benefit efforts to establish functional RNA interactomes and support the development of more predictive computational approaches for RBP identification.

Modeling and prediction of business success: a survey

Businesses are the driving force behind economic systems and are the lifeblood of the community. A business shares striking similarity to a living organism, including birth, infancy, rising, prosperity, and falling. The success of a business is not only important to the owners, but is also critical to the regional/domestic economic system, or even the global economy. Recent years have witnessed many new emerging businesses with tremendous success, such as Google, Apple, Facebook etc., yet millions of businesses also fail or fade out within a rather short period of time. Finding patterns/factors connected to the business rise and fall remains a long lasting question puzzling many economists, entrepreneurs, and government officials. Recent advancement in artificial intelligence, especially machine learning, has lend researchers powers to use data to model and predict business success. However, due to data driven nature of all machine learning methods, existing approaches are rather domain-driven and ad-hoc in their design and validations. In this paper, we propose a systematic review of modeling and prediction of business success. We first outline a triangle framework to showcase three parities connected to the business: Investment-Business-Market (IBM). After that, we align features into three main categories, each of which is focused on modeling a business from a particular perspective, such as sales, management, innovation etc., and further summarize different types of machine learning and deep learning methods for business modeling and prediction. The survey provides a comprehensive review of computational approaches for business performance modeling and prediction.

Adaptive lambda schemes for efficient relative binding free energy calculation.

The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.

A computational modeling approach for prediction of polarization losses inside porous micro-structured cathode used for CO2 reduction in SOEC

Multi-component electrochemical models were developed and simulated to predict various losses associated with solid oxide electrolysis cells (SOEC) for CO2 reduction. Electrochemical reduction reactions were modeled and simulated for two feed compositions: CO2/H2 and CO2/CO. The concentration overpotentials were calculated using Fick’s Model and Dusty Gas Model, and subsequently, current-voltage characteristics of SOEC were estimated and compared. In case of CO2/H2 feed gas mixture, the predicted applied voltage by Dusty Gas Model (DGM) and Fick’s Model are 2.37 and 2.03 V respectively at current density of 0.8 A/cm2. While in CO2/CO atmosphere, both the models estimated the same value of applied potential at 0.05 A/cm2. In CO2/H2 mixture, a deviated prediction of applied voltage by two models at the same current density might be due to the equimolar counter diffusion assumption, made by Fick’s Model, which is invalid for the large difference in molar mass of constituent gases CO2 (44 g/mol) and H2 (2 g/mol). Furthermore, models were simulated to assess the role of temperature and molar compositions feed gas on the current-voltage characteristics of SOEC.

SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances.

Protein functions are tightly related to the fine details of their 3D structures. To understand protein structures, computational prediction approaches are highly needed. Recently, protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning techniques. Most of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential function. Here, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue's pose according to all other residues' features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at https://github.com/gongtiansu/SASA-Net/.

Non-classical HLA-E restricted CMV 15-mer peptides are recognized by adaptive NK cells and induce memory responses.

Human cytomegalovirus (HCMV) reactivation causes complications in immunocompromised patients after hematopoietic stem cell transplantation (HSCT), significantly increasing morbidity and mortality. Adaptive Natural Killer (aNK) cells undergo a persistent reconfiguration in response to HCMV reactivation; however, the exact role of aNK cell memory in HCMV surveillance remains elusive. We employed mass spectrometry and computational prediction approaches to identify HLA-E-restricted HCMV peptides that can elucidate aNK cell responses. We also used the K562 cell line transfected with HLA-E0*0103 for specific peptide binding and blocking assays. Subsequently, NK cells were cocultured with dendritic cells (DCs) loaded with each of the identified peptides to examine aNK and conventional (c)NK cell responses. Here, we discovered three unconventional HLA-E-restricted 15-mer peptides (SEVENVSVNVHNPTG, TSGSDSDEELVTTER, and DSDEELVTTERKTPR) derived from the HCMV pp65-protein that elicit aNK cell memory responses restricted to HCMV. aNK cells displayed memory responses towards HMCV-infected cells and HCMV-seropositive individuals when primed by DCs loaded with each of these peptides and predicted 9-mer versions. Blocking the interaction between HLA-E and the activation NKG2C receptor but not the inhibitory NKG2A receptor abolished these specific recall responses. Interestingly, compared to the HLA-E complex with the leader peptide VMAPRTLIL, HLA-E complexes formed with each of the three identified peptides significantly changed the surface electrostatic potential to highly negative. Furthermore, these peptides do not comprise the classical HLA-E-restriction motifs. These findings suggest a differential binding to NKG2C compared to HLA-E complexes with classical leader peptides that may result in the specific activation of aNK cells. We then designed six nonameric peptides based on the three discovered peptides that could elicit aNK cell memory responses to HCMV necessary for therapeutic inventions. The results provide novel insights into HLA-E-mediated signaling networks that mediate aNK cell recall responses and maximize their reactivity.

Diagnosing Seasonal Forecast Skill of the Indian Ocean Dipole Mode Using Model Analogs

Abstract A retrospective tropical Indian Ocean dipole mode (IOD) hindcast for 1958–2014 was conducted using 20 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with a model-based analog forecast (MAF) method. In the MAF approach, forecast ensembles are extracted from preexisting model simulations by finding the states that initially best match an observed anomaly and tracking their subsequent evolution, with no additional model integrations. By optimizing the key factors in the MAF method, we suggest that the optimal domain for the analog criteria should be concentrated in the tropical Indian Ocean region for IOD predictions. Including external forcing trends improves the skills of the east and west poles of the IOD, but not the IOD prediction itself. The MAF IOD prediction showed comparable skills to the assimilation-initialized hindcast, with skillful predictions corresponding to a 4- and 3-month lead, respectively. The IOD forecast skill had significant decadal variations during the 55-yr period, with low skill after the early 2000s and before 1985 and high skill during 1985–2000. This work offers a computational efficient and practical approach for seasonal prediction of the tropical Indian Ocean sea surface temperature.

MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning.

Identifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization and reliable negative sample selection, remain to be addressed. To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new "guilty-by-association"-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning. MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/. Supplementary data are available at Bioinformatics online.

A computational geometric learning approach for person axial and slanting depth prediction using single RGB camera

A computational geometric learning approach for person axial and slanting depth prediction using single RGB camera

A machine learning based computational approach for prediction of cation distribution in spinel crystal.

In this study, a machine learning based computational approach has been developed to investigate the cation distribution in spinel crystals. The computational approach integrates the construction of datasets consisting of the energies calculated from density functional theory, the training of machine learning models to derive the relationship between system energy and structural features, and atomistic Monte Carlo simulations to sample the thermodynamic equilibrium structures of spinel crystals. It is found that the support vector machine model yields excellent performance in energy predictions based on spinel crystal structures. Furthermore, the developed computational approach has been applied to predict the cation distribution in single spinel MgAl2O4 and MgFe2O4 and double spinel MgAl2-aFeaO4. Agreeing with the available experimental data, the computational approach correctly predicts that the equilibrium degree of inversion of MgAl2O4 increases with temperature, whereas the degree of inversion of MgFe2O4 decreases with temperature. Additionally, it is predicted that the equilibrium occupancy of Mg cations at the tetrahedral and octahedral sites in MgAl2-aFeaO4 could be tuned as a function of chemical composition. Therefore, this study presents a reliable computational approach that can be extended to study the variation of cation distribution with processing temperature and chemical composition in a wide range of complex multi-cation spinel oxides with numerous applications.

Computational approaches for circRNAs prediction and in silico characterization.

Circular RNAs (circRNAs) are single-stranded and covalently closed non-coding RNA molecules originated from RNA splicing. Their functions include regulatory potential over other RNA species, such as microRNAs, messenger RNAs and RNA binding proteins. For circRNA identification, several algorithms are available and can be classified in two major types: pseudo-reference-based and split-alignment-based approaches. In general, the data generated from circRNA transcriptome initiatives is deposited on public specific databases, which provide a large amount of information on different species and functional annotations. In this review, we describe the main computational resources for the identification and characterization of circRNAs, covering the algorithms and predictive tools to evaluate its potential role in a particular transcriptomics project, including the public repositories containing relevant data and information for circRNAs, recapitulating their characteristics, reliability and amount of data reported.