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

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/ .

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.

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.

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.

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.

Advances in the cellular structural biology of nucleic acids.

Conventional biophysical and chemical biology approaches for delineating relationships between the structure and biological function of nucleic acids (NAs) abstract NAs from their native biological context. However, cumulative experimental observations have revealed that the structure, dynamics and interactions of NAs might be strongly influenced by a broad spectrum of specific and nonspecific physical-chemical environmental factors. This consideration has recently sparked interest in the development of novel tools for structural characterization of NAs in the native cellular context. Here, we review the individual methods currently being employed for structural characterization of NA structure in a native cellular environment with a focus on recent advances and developments in the emerging fields of in-cell NMR and electron paramagnetic resonance spectroscopy and in-cell single-molecule FRET of NAs.