MOTIVATION: Non-coding RNA (ncRNA) plays a crucial role in gene regulation, including by forming sequence-specific RNA-DNA interactions at gene regulatory elements. One form of interaction takes place via the formation of RNA:DNA:DNA triple helices (triplexes). Accurate computational prediction of triplex formation from nucleotide sequences is an important tool in ncRNA research but remains somewhat inaccessible and complex. To address this, we created TripLexicon, a web-based interface for accessing and analyzing predicted gene regulatory RNA-DNA interactions in human and mouse. RESULTS: Predicted interactions can be accessed from RNA-, DNA-, and region-centric perspectives. For each RNA transcript, visualizations at genome and nucleotide resolution are available, providing insight into target genes and regions, as well as putative functional domains of the transcript. Predicted target genes can immediately be subjected to ontology and pathway enrichment analysis, providing rapid insight into potential functions mediated by the RNA-DNA interactions of the queried transcript. DNA and region queries are designed to identify potentially important ncRNA interactors at sites of interest. AVAILABILITY AND IMPLEMENTATION: TripLexicon is accessible at https://triplexicon.uni-frankfurt.de. This website is free and open to all users and there is no login requirement. All data and code is uploaded to Zenodo: https://zenodo.org/records/17143608 and the code for the webserver is available on Github: https://github.com/SchulzLab/TripLexicon.

Extraction of meaningful biological insight from gene expression profiling often focuses on the identification of statistically enriched terms or pathways. These methods typically use gene sets as input data, and subsequently return overrepresented terms along with associated statistics describing their enrichment. This approach does not cater to analyses focused on a single gene-of-interest, particularly when the gene lacks prior functional characterization. To address this, we formulated GeneCOCOA, a method which utilizes context-specific gene co-expression and curated functional gene sets, but focuses on a user-supplied gene-of-interest (GOI). The co-expression between the GOI and subsets of genes from functional groups (e.g. pathways, GO terms) is derived using linear regression, and resulting root-mean-square error values are compared against background values obtained from randomly selected genes. The resulting p values provide a statistical ranking of functional gene sets from any collection, along with their associated terms, based on their co-expression with the gene of interest in a manner specific to the context and experiment. GeneCOCOA thereby provides biological insight into both gene function, and putative regulatory mechanisms by which the expression of the GOI is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. We furthermore include a differential GeneCOCOA mode, thus presenting the first implementation of a gene-focused approach to experiment-specific gene set enrichment analysis. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA.

RNA.DNA:DNA triple helix (triplex) formation is a form of RNA-DNA interaction which regulates gene expression but is difficult to study experimentally in vivo. This makes accurate computational prediction of such interactions highly important in the field of RNA research. Current predictive methods use canonical Hoogsteen base pairing rules, which whilst biophysically valid, may not reflect the plastic nature of cell biology. Here, we present the first optimization approach to learn a probabilistic model describing RNA-DNA interactions directly from motifs derived from triplex sequencing data. We find that there are several stable interaction codes, including Hoogsteen base pairing and novel RNA-DNA base pairings, which agree with in vitro measurements. We implemented these findings in TriplexAligner, a program that uses the determined interaction codes to predict triplex binding. TriplexAligner predicts RNA-DNA interactions identified in all-to-all sequencing data more accurately than all previously published tools in human and mouse and also predicts previously studied triplex interactions with known regulatory functions. We further validated a novel triplex interaction using biophysical experiments. Our work is an important step towards better understanding of triplex formation and allows genome-wide analyses of RNA-DNA interactions.