LncRNADisease: a database for long-non-coding RNA-associated diseases

TLDR

Long‑non‑coding RNAs are increasingly recognized as key regulators of diverse biological processes, and their dysfunctions are linked to many diseases, underscoring the need to elucidate their disease roles for diagnosis, treatment, and prognosis. This work aims to provide a high‑quality, publicly accessible lncRNA–disease association database. The LncRNADisease database compiles and curates ~480 experimentally supported lncRNA–disease links across 166 diseases, 478 lncRNA interaction partners, and detailed genomic, sequence, reference, and species annotations, with standardized disease names and lncRNA dysfunction types. A bioinformatic prediction model was also developed and its results for 1,564 human lncRNAs were incorporated into the database.

Abstract

In this article, we describe a long-non-coding RNA (lncRNA) and disease association database (LncRNADisease), which is publicly accessible at http://cmbi.bjmu.edu.cn/lncrnadisease. In recent years, a large number of lncRNAs have been identified and increasing evidence shows that lncRNAs play critical roles in various biological processes. Therefore, the dysfunctions of lncRNAs are associated with a wide range of diseases. It thus becomes important to understand lncRNAs’ roles in diseases and to identify candidate lncRNAs for disease diagnosis, treatment and prognosis. For this purpose, a high-quality lncRNA–disease association database would be extremely beneficial. Here, we describe the LncRNADisease database that collected and curated approximately 480 entries of experimentally supported lncRNA–disease associations, including 166 diseases. LncRNADisease also curated 478 entries of lncRNA interacting partners at various molecular levels, including protein, RNA, miRNA and DNA. Moreover, we annotated lncRNA–disease associations with genomic information, sequences, references and species. We normalized the disease name and the type of lncRNA dysfunction and provided a detailed description for each entry. Finally, we developed a bioinformatic method to predict novel lncRNA–disease associations and integrated the method and the predicted associated diseases of 1564 human lncRNAs into the database.