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.

Circr, a Computational Tool to Identify miRNA:circRNA Associations

Circular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false positive predictions. To overcome these limitations, we developed Circr, a computational tool for the prediction of associations between circRNAs and miRNAs. Circr combines three publicly available algorithms for de novo prediction of miRNA binding sites on target sequences (miRanda, RNAhybrid, and TargetScan) and annotates each identified miRNA:target pairs with experimentally validated miRNA:RNA interactions and binding sites for Argonaute proteins derived from either ChIPseq or CLIPseq data. The combination of multiple tools for the identification of a single miRNA recognition site with experimental data allows to efficiently prioritize candidate miRNA:circRNA interactions for functional studies in different organisms. Circr can use its internal annotation database or custom annotation tables to enhance the identification of novel and not previously annotated miRNA:circRNA sites in virtually any species. Circr is written in Python 3.6 and is released under the GNU GPL3.0 License at https://github.com/bicciatolab/Circr.

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning

MOTIVATION

Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages.

RESULTS

In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems.

AVAILABILITY

http://sbbi-panda.unl.edu/pin/.

Integrated analysis of directly captured microRNA targets reveals the impact of microRNAs on mammalian transcriptome

MicroRNA (miRNA)-mediated regulation is widespread, relatively mild but functionally important. It remains challenging to unequivocally identify miRNA targeted RNAs at a genomic scale and determine how changes in miRNA levels affect the transcriptome. Here, we captured individual miRNAs and their targeted RNA sites in wild-type, miR-200 family knockout and induced epithelial cells. We detected 1797 miRNAs interacting with 13,830 transcripts at 616,127 sites by sequencing 1,230,019 unique miRNA:RNA chimeras. Although mRNA sites that are bound by miRNAs and contain matches to seed sequences confer the strongest regulation, ∼40%-60% of miRNA bound regions do not contain seed matches. Different miRNAs have different preferences to seed matches and 3' end base-pairing. For individual miRNAs, the effectiveness of mRNA regulation is highly correlated with the number of captured miRNA:mRNA chimeras. Notably, elevated miR-200 expression robustly represses existing targets with little impact on newly recognized targets. Global analysis of directly captured mRNA targets reveals pathways that are involved in cancer and cell adhesion and signaling pathways that are highly regulated by many different miRNAs in epithelial cells. Comparison between experimentally captured and TargetScan predicted targets indicates that our approach is more effective in identifying bona fide targets by reducing false positive and negative predictions. This study reveals the global binding landscape and impact of miRNAs on the mammalian transcriptome.

Translation Efficiency in Upstream Region of microRNA Targets in Arabidopsis thaliana

With respect to upstream regions of microRNA (miRNA) target sites located in protein coding sequences, experimental studies have suggested rare codons, rather than frequent codons, are important for miRNA function, because they slow down the local translational process. But, whether there is a trend of reduced translation efficiency near miRNA targets is still unknown. Using Arabidopsis thaliana, we perform genome-wide analysis of synonymous codon usage in upstream regions of miRNA target sites. At the whole genome level, we find no significant selection signals for decreased translational efficiency. However, the same genome analyses do show substantial variations of translation efficiency reduction among miRNA targets. We find that miRNA conservation level, gene codon usage bias, and the mechanism of miRNA action can account for the differences in translation efficiency. But gene's GC content, gene expression level, and miRNA target's conservation level have no effect on local translation efficiency of miRNA targets. Although local translation efficiency in the upstream region of miRNA targets is related to miRNA function in A. thaliana, the selection signal of rare codon usage in that region is weak. We propose some other biological factors are more important than local translation efficiency in miRNA action when miRNA targets are located in protein coding sequences.