The lab of Prof. Jo Vandesompele and Prof. Pieter Mestdagh at Ghent University
The OncoRNALab is supervised by Prof. Jo Vandesompele and Prof. Pieter Mestdagh at Ghent University (Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences). It is part of the Center for Medical Genetics and the Cancer Research Institute Ghent.
The lab’s research aims to exploit RNA for diagnostic and therapeutic purposes. The various research lines converge on studying the role of non-coding RNA in cancer and on the utility of extracellular RNA in liquid biopsies. Through a combination of high-throughput (functional) genomics technologies and bio-informatics tools, we seek to answer various fundamental and translational research questions.
circular RNAs in cancer
controlling pre-analytical variables of RNA sequencing in blood-based precision medicine
digital PCR data-analysis tools developed by the Ghent University Digital PCR Consortium
crowdsourcing knowledgebase with experimental parameters of public extracellular vesicles (EV) studies
antisense oligonucleotide design for long non-coding RNAs
a comprehensive compendium of human long non-coding RNAs
easy-to-use online database that keeps track of all historical and current miRNA annotation present in the miRBase database
microRNA target prediction
power Calculations for digital PCR
state-of-the-art PCR assay design for quantitative and digital PCR, targeted resequencing, double-mismatch allele specific PCR (DMAS), and 4C
public database for sharing of quantitative and digital PCR using the RDML data exchange format
visualization and analysis method to evaluate human transcription start sites in relation to publicly available CAGE-sequencing, ChIP-sequencing and DNase-sequencing datasets
Non-coding RNA, Extracellular RNA, Liquid biopsies, Pan-cancer, Method development
Non-coding RNA, Cancer, Extracellular RNA, Liquid biopsies, Bioinformatics, Immune oncology
Cancer, Extracellular RNA, Liquid biopsies, Pediatric tumors, Cancer diagnostics, detection and prognosis
Computational deconvolution, Transcriptomics, Liquid biopsies, Biostatistics
Cancer, Extracellular RNA, Liquid biopsies, Immune checkpoint inhibitors, Melanoma
Cancer, Non-coding RNA, Melanoma, Lung cancer, High-throughput screening, CRISPR-Cas, Tumor immunology and immunotherapy, Immune checkpoint inhibitors
Non-coding RNA, Cancer, Extracellular RNA, Liquid biopsies, Melanoma
Non-coding RNA, Cancer, Extracellular RNA, Liquid biopsies, Melanoma, Pediatric tumors, Lung cancer
Circular RNAs (circRNAs) are covalently closed RNA molecules that have been linked to various diseases, including cancer. However, a precise function and working mechanism are lacking for the larger majority. Following many different experimental and computational approaches to identify circRNAs, multiple circRNA databases were developed as well. Unfortunately, there are several major issues with the current circRNA databases, which substantially hamper progression in the field. First, as the overlap in content is limited, a true reference set of circRNAs is lacking. This results from the low abundance and highly specific expression of circRNAs, and varying sequencing methods, data-analysis pipelines, and circRNA detection tools. A second major issue is the use of ambiguous nomenclature. Thus, redundant or even conflicting names for circRNAs across different databases contribute to the reproducibility crisis. Third, circRNA databases, in essence, rely on the position of the circRNA back-splice junction, whereas alternative splicing could result in circRNAs with different length and sequence. To uniquely identify a circRNA molecule, the full circular sequence is required. Fourth, circRNA databases annotate circRNAs’ microRNA binding and protein-coding potential, but these annotations are generally based on presumed circRNA sequences. Finally, several databases are not regularly updated, contain incomplete data or suffer from connectivity issues. In this review, we present a comprehensive overview of the current circRNA databases and their content, features, and usability. In addition to discussing the current issues regarding circRNA databases, we come with important suggestions to streamline further research in this growing field.
Here, we present the fifth release of the human lncRNA database LNCipedia (https://lncipedia.org). The most notable improvements include manual literature curation of 2482 lncRNA articles and the use of official gene symbols when available. In addition, an improved filtering pipeline results in a higher quality reference lncRNA gene set.
