Essential Non-coding Genes: A New Playground of Bioinformatics

The essentiality of a gene can be defined at different levels and is context-dependent. Essential protein-coding genes have been well studied. However, the essentiality of non-coding genes is not well characterized. Although experimental technologies, like CRISPR-Cas9, can provide insights into the essentiality of non-coding regions of the genome, scoring the essentiality of non-coding genes in different contexts is still challenging. With machine learning algorithms, the essentiality of protein-coding genes can be estimated well. But the development of these algorithms for non-coding genes was very early. Based on several recent studies, we believe the essentiality of non-coding genes will be a new and fertile ground in bioinformatics. We pointed out some possible research topics in this perspective article.

Automated summary

The essentiality of genes can be defined at different levels and is context-dependent. While the essentiality of protein-coding genes has been well studied, the essentiality of non-coding genes remains poorly characterized. Experimental technologies like CRISPR-Cas9 provide insights into non-coding regions, but assessing the essentiality of non-coding genes in different contexts is still challenging. Machine learning algorithms have been successful in estimating the essentiality of protein-coding genes, but the development of algorithms for non-coding genes is in its early stages. Recent studies suggest that the essentiality of non-coding genes will be a new and fertile ground in bioinformatics, and this perspective article outlines potential research topics.