Zehr, S. | Search bioRxiv

bioRxiv2024

Detection of statistically robust interactions from diverse RNA-DNA ligation data

Chromatin-localized RNAs play diverse roles in gene regulation and nuclear architecture. Mapping genome-wide RNA-DNA interactions is possible using a variety of molecular methods, including using bridging oligonucleotides to ligate RNA and DNA in proximity. While molecular methods have progressed, a robust computational method for calling biologically meaningful RNA-DNA interactions from these data is lacking. Herein, we present RADIAnT, a reads-to-interactions pipeline for analyzing RNA-DNA ligation data. RADIAnT calls interactions against a dataset-specific, unified background which considers RNA binding site-TSS distance and genomic region bias. By scaling the background by RNA abundance, RADIAnT is sensitive enough to detect specific interactions of lowly expressed transcripts, while remaining specific enough to discount false positive interactions of highly abundant RNAs. RADIAnT outperforms previously proposed methods in the accurate recall of genome-wide Malat1-DNA interactions, and in a use case, was utilized to identify dynamic chromatin-associated RNAs in the physiologically- and pathologically-relevant process of endothelial-to-mesenchymal transition.

bioinformatics↗

bioRxiv2024

GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data

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. The co-expression between the gene-of-interest 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 gene-of-interest is controlled. Despite its relative simplicity, GeneCOCOA outperforms similar methods in the accurate recall of known gene-disease associations. GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA. Author summaryUnderstanding the biological functions of different genes and their respective products is a key element of modern biological research. While one can examine the relative abundance of a gene product in transcriptomics data, this alone does not provide any clue to the biological relevance of the gene. Using a type of analysis called co-expression, it is possible to identify other genes which have similar patterns of regulation to a gene-of-interest, but again, this cannot tell you what a gene does. Genes whose function has previously been studied are often assembled into groups (e.g. pathways, ontologies), which can be used to annotate gene sets of interest. However, if a gene has not yet been characterized, it will not appear in these gene set enrichment analyses. Here, we propose a new method - GeneCOCOA - which uses co-expression of a single gene with genes in functional groups to identify which functional group a gene is most similar too, resulting in a putative function for the gene, even if it has not been studied before. We tested GeneCOCOA by using it to find gene-disease links which have already been scientifically studied, and showed that GeneCOCOA can do this more effectively than other available methods.

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