Bioinformatics and machine learning approaches to understand the regulation of mobile genetic elements

Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.peer-reviewe

Small RNA targets : advances in prediction tools and high-throughput profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA–RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.peer-reviewe

Enhancing Semiempirical Quantum Mechanical Scoring with Machine Learning: a new scoring function that accounts for both the enthalpic and entropic contributions to the ligand binding free energy

Identifying hit compounds is a principal step in early-stage drug discovery. While many machine learning (ML) approaches have been proposed, in the absence of binding data, molecular docking is the most widely used option to predict binding modes and score hundreds of thousands of compounds for binding affinity to the target protein. Docking\u27s effectiveness is critically dependent on the protein-ligand (P-L) scoring function (SF), thus re-scoring with more rigorous SFs is a common practice. In this pilot study, we scrutinize the PM6-D3H4X/COSMO semi-empirical quantum mechanical (SQM) method as a docking pose re-scoring tool on 17 diverse receptors and ligand decoy sets, totaling 1.5 million P-L complexes. We investigate the effect of explicitly computed ligand conformational entropy and ligand deformation energy on SQM P-L scoring in a virtual screening (VS) setting, as well as molecular mechanics (MM) versus hybrid SQM/MM structure optimization prior to re-scoring. Our results proclaim that there is no obvious benefit from computing ligand conformational entropies or deformation energies and that optimizing only the ligand\u27s geometry on the SQM level is sufficient to achieve the best possible scores. Instead, we leverage machine learning (ML) to include implicitly the missing entropy terms to the SQM score using ligand topology, physicochemical, and P-L interaction descriptors. Our new hybrid scoring function, named SQM-ML, is transparent and explainable, and achieves in average 9% higher AUC-ROC than PM6-D3H4X/COSMO and 3% higher than Glide SP, but with consistent and predictable performance across all test sets, unlike the former two SFs, whose performance is considerably target-dependent and sometimes resembles that of a random classifier. The code to prepare and train SQM-ML models is available at https://github.com/tevang/sqm-ml.git and we believe that will pave the way for a new generation of hybrid SQM/ML protein-ligand scoring functions

Characterization of the thermal behaviour of stationary electric contacts using infrared camera

108 σ.Η εργασία αυτή αφορά στη θερμική συμπεριφορά των ηλεκτρικών επαφών όταν διαρρέονται από εναλλασσόμενο ηλεκτρικό ρεύμα. Η συμπεριφορά αυτή μελετάται συναρτήσει της καταπόνησης της επαφής, της έντασης ρεύματος που την διαρρέει, της διατομής του καλωδίου τροφοδοσίας και του είδους του. Στο πρώτο κεφάλαιο γίνεται μια εισαγωγή στις ηλεκτρικές επαφές, τα φυσικά και ηλεκτρικά τους χαρακτηριστικά και την επίδραση των γνωρισμάτων αυτών στην ηλεκτρική συμπεριφορά των επαφών. Στο κεφάλαιο αυτό δίνεται και ο σκοπός της εργασίας. Στο δεύτερο κεφάλαιο δίνεται το κύκλωμα μέτρησης που υλοποιήθηκε. Επίσης, περιγράφεται η διαδικασία μέτρησης που αφορά την εργασία αυτή και συγκεκριμένα είναι η μέτρηση, μέσω κάμερας υπερύθρων, των θερμοκρασιών που αναπτύσσονται σε κλειστές ηλεκτρικές επαφές που έχουν υποστεί διαφορετικού βαθμού καταπόνηση για διάφορες τιμές έντασης ρεύματος και καλώδια τροφοδοσίας διαφορετικών διατομών και τύπων (μονόκλωνα, πολύκλωνα-εύκαμπτα). Στο τρίτο κεφάλαιο δίνονται τα αποτελέσματα των μετρήσεων σε συνοπτική μορφή πινάκων, σε διαγραμματική μορφή και παρατίθενται κάποια θερμογραφικά δεδομένα (εικόνες υπερύθρων). Τα στοιχεία αυτά παρουσιάζονται χωρίς σχόλιο. Στο τέταρτο κεφάλαιο γίνεται μια προσπάθεια για τη θεωρητική διερεύνηση των πειραματικών αποτελεσμάτων. Τέλος, στο παράρτημα δίνεται αναλυτικά το σύνολο των αναλυτικών μετρήσεων θερμοκρασίας σε μορφή πινάκων. Ακολουθεί ο κατάλογος σχημάτων και εικόνων που περιλαμβάνονται στην εργασία καθώς και η βιβλιογραφία που χρησιμοποιήθηκε.The title of this thesis is “Characterization of the Thermal Behaviour of Stationary Electric Contacts using Infrared Camera”. This thesis concerns the thermal behaviour of electric contacts when ac current runs through them. The thermal behaviour is being examined regarding the wear of the electric contacts, the current intensity and the size and type of the cables used. In the first section an introduction on electric contacts, their physical and electrical characteristics is given. At the end of this section the aim of this thesis is given. The second section describes the measurement circuit and the measurement procedure which involves using a thermal camera (infrared camera). The measurements taken concern the temperature of the electric contacts as a function of the contact’s wear, the current intensity and the size and the type of the copper cable used (single-core, flexible). The third section represents the results of the measurements in tables and diagrams and also includes a selection of infrared images. The fourth section gives possible explanations regarding the results. The Appendix, which encompasses all temperature measurements, as well as the table of figures and the references can all be found at the end of the thesis.Ηλέκτρα-Χαρά Χ. Γιασσ

miRBind: A Deep Learning Method for miRNA Binding Classification

The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding ‘seeds’, i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on ‘canonical’ seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are ‘canonical’. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available

Analysis of chimeric reads characterises the diverse targetome of AGO2-mediated regulation

Abstract Argonaute proteins are instrumental in regulating RNA stability and translation. AGO2, the major mammalian Argonaute protein, is known to primarily associate with microRNAs, a family of small RNA ‘guide’ sequences, and identifies its targets primarily via a ‘seed’ mediated partial complementarity process. Despite numerous studies, a definitive experimental dataset of AGO2 ‘guide’–’target’ interactions remains elusive. Our study employs two experimental methods—AGO2 CLASH and AGO2 eCLIP, to generate thousands of AGO2 target sites verified by chimeric reads. These chimeric reads contain both the AGO2 loaded small RNA ‘guide’ and the target sequence, providing a robust resource for modeling AGO2 binding preferences. Our novel analysis pipeline reveals thousands of AGO2 target sites driven by microRNAs and a significant number of AGO2 ‘guides’ derived from fragments of other small RNAs such as tRNAs, YRNAs, snoRNAs, rRNAs, and more. We utilize convolutional neural networks to train machine learning models that accurately predict the binding potential for each ‘guide’ class and experimentally validate several interactions. In conclusion, our comprehensive analysis of the AGO2 targetome broadens our understanding of its ‘guide’ repertoire and potential function in development and disease. Moreover, we offer practical bioinformatic tools for future experiments and the prediction of AGO2 targets. All data and code from this study are freely available at https://github.com/ML-Bioinfo-CEITEC/HybriDetector/

HERMES – A Software Tool for the Prediction and Analysis of Magnetic-Field-Induced Residual Dipolar Couplings in Nucleic Acids

Field-Induced Residual Dipolar Couplings (fiRDC) are a valuable source of long-range information on structure of nucleic acids (NA) in solution. A web application (HERMES) was developed for structure-based prediction and analysis of the (fiRDCs) in NA. fiRDC prediction is based on input 3D model structure(s) of NA and a built-in library of nucleobase-specific magnetic susceptibility tensors and reference geometries. HERMES allows three basic applications: (i) the prediction of fiRDCs for a given structural model of NAs, (ii) the validation of experimental or modeled NA structures using experimentally derived fiRDCs, and (iii) assessment of the oligomeric state of the NA fragment and/or the identification of a molecular NA model that is consistent with experimentally derived fiRDC data. Additionally, the program's built-in routine for rigid body modeling allows the evaluation of relative orientation of domains within NA that is in agreement with experimental fiRDCs

HERMES – A Software Tool for the Prediction and Analysis of Magnetic-Field-Induced Residual Dipolar Couplings in Nucleic Acids

Field-Induced Residual Dipolar Couplings (fiRDC) are a valuable source of long-range information on structure of nucleic acids (NA) in solution. A web application (HERMES) was developed for structure-based prediction and analysis of the (fiRDCs) in NA. fiRDC prediction is based on input 3D model structure(s) of NA and a built-in library of nucleobase-specific magnetic susceptibility tensors and reference geometries. HERMES allows three basic applications: (i) the prediction of fiRDCs for a given structural model of NAs, (ii) the validation of experimental or modeled NA structures using experimentally derived fiRDCs, and (iii) assessment of the oligomeric state of the NA fragment and/or the identification of a molecular NA model that is consistent with experimentally derived fiRDC data. Additionally, the program's built-in routine for rigid body modeling allows the evaluation of relative orientation of domains within NA that is in agreement with experimental fiRDCs