A large proportion of the human genome contains non-coding regions that were once considered "junk DNA". However, with the advent of advanced high-throughput sequencing technologies and computational tools, it has become increasingly evident that non-coding regions play critical roles in the regulation of gene expression, genome organization and plasticity and have a profound impact on physiology and pathology. Systems biology approaches that integrate experimental and computational methods have given precious insights on complex biological systems. By analyzing the non-coding genome in a systems biology framework, one can unravel the intricate interactions and functional roles of non-coding elements, such as long non-coding RNAs (lncRNAs), microRNAs, transposable elements in various physiological and pathological processes.

This Research Topic aims to provide a framework for integrative approaches that study the occurrence and functions of the non-coding regions of the genome. This topic invites scientists to share their research that aims to unravel the complexity of the non-coding genome and provide novel insights into its functional roles in human health and disease. Such research has the potential to revolutionize our understanding of the human genome and open new avenues for the development of personalized medicine and targeted therapies for various diseases.

This research topic welcomes original research articles, review manuscripts and methods articles covering, but not restricted to, the following aspects:
1) the use of omics data, such as transcriptomics, epigenomics, and proteomics, to comprehensively analyze the non-coding genome.
2) interactions between non-coding elements as well as key hubs and regulatory nodes in regulatory networks that allows us to gain insights into the underlying mechanisms by which non-coding elements modulate gene expression and impact cellular processes.
3) integrative approaches that study non-coding genome occurence and function.
4) computational modeling and simulation techniques to predict the dynamic behavior of non-coding elements and their interactions under different physiological or pathological conditions.
5) statistical and machine learning approaches to analyze large-scale datasets to identify potential functions of non-coding genome, biomarkers or therapeutic targets associated with specific physiological or pathological conditions.
6) integration of experimental state-of-the-art techniques such as CRISPR/Cas9 genome editing, RNA interference, and functional assays to validate the functional roles of non-coding elements in vitro and in vivo.

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Frontiers in Systems Biology

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