A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes

Abstract

MOTIVATION\nmicroRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA-mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process. The recent development of single cell data and the booming of the exploration of cell trajectories using pseudo-time concept have inspired us to develop a pseudo-time based method to infer the miRNA-mRNA relationships characterising a biological process by taking into account the temporal aspect of the process.\n\n\nRESULTS\nWe have developed a novel approach, called pseudo-time causality (PTC), to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition (EMT), a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA-mRNA interactions in both single cell and bulk data. The results suggest that utilising the pseudo-temporal information from the data helps reveal the gene regulation in a biological process much better than using the static information.\n\n\nAVAILABILITY\nR scripts and datasets can be found at https://github.com/AndresMCB/PTC.\n\n\nCONTACT\[email protected].\n\n\nSUPPLEMENTARY INFORMATION\nSupplementary data are available at Bioinformatics online.