Welcome to circlncRNAnet

In recent years, deep sequencing technologies have unraveled the non-coding constituents of the transcriptome, most notably lncRNAs and circRNAs. Despite the lack of protein-coding potential, these once uncharted parts have emerged as a key determinant in gene regulation, acting as critical switches that fine-tune transcriptional and signaling output. At the mechanistic level, regardless of the forms, these non-coding RNA transcripts have been known to impact expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation – scaffolding transcriptional regulation in cis or trans, or sponging/altering the intracellular stoichiometry of microRNAs (miRNAs) or RNA-binding factors. Given the potentially widespread target spectrum of these ncRNAs as well as their extensive modes of action, a complete understanding of their biological relevance will depend on integrative analyses of systems data at various levels. To this end, while a handful of publicly available databases have been reported, they are quite limited in the scope of reference data and analytic modules, relying on existing datasets in public archives and annotating pre-selected regulatory features of ncRNAs. Thus, existing tools do not fully capture from a network perspective the functional implications of lncRNAs or circRNAs of interest.

Our new webserver has the following distinguishing features that represent advances in the bioinformatics of ncRNAs:

1. A flexible framework that accepts and processes user-defined NGS-based expression data.
With the expansion of transcriptome sequencing datasets, focusing on a select set of publicly available, but potentially irrelevant, sequencing data does not sufficiently address users’ research needs. This prompted us to build a completely new system that allows upload of expression matrix data by the users. Expression correlations can be first extracted from the uploaded data, from which ncRNA-mRNA networks as well as regulatory features (cis vs. trans, target ontology, etc.) can be profiled.

2. Multiple analytic modules that assigns the regulatory networks of user-selected ncRNAs.
We also implemented analytic modules that, by cross-referencing with databases, provide productive assessment the regulatory modes of user-selected ncRNAs. In this capacity, the functional consequences of ncRNAs may be deduced in terms of their interaction with RNA-binding proteins or involvement in miRNA targeting/sponge.

3. An all-purpose, information-rich workflow design that is tailored to all types of ncRNAs.
Despite their differences in structure and biosynthesis steps, lncRNAs and circRNAs are much more common in terms of their roles and mechanisms in gene regulation. The wide range of analytic tools we installed in circlncRNAnet is thus applicable to studies of both types of ncRNAs.