Lnc2Meth (http://www.bio-bigdata.com/Lnc2Meth/), an interactive resource to identify regulatory relationships between human long non-coding RNAs (lncRNAs) and DNA methylation, is not only a manually curated collection and annotation of experimentally supported lncRNAs-DNA methylation associations but also a platform that effectively integrates tools for calculating and identifying the differentially methylated lncRNAs and protein-coding genes (PCGs) in diverse human diseases. The resource provides: (i) advanced search possibilities, e.g. retrieval of the database by searching the lncRNA symbol of interest, DNA methylation patterns, regulatory mechanisms and disease types; (ii) abundant computationally calculated DNA methylation array profiles for the lncRNAs and PCGs; (iii) the prognostic values for each hit transcript calculated from the patients clinical data; (iv) a genome browser to display the DNA methylation landscape of the lncRNA transcripts for a specific type of disease; (v) tools to re-annotate probes to lncRNA loci and identify the differential methylation patterns for lncRNAs and PCGs with user-supplied external datasets; (vi) an R package (LncDM) to complete the differentially methylated lncRNAs identification and visualization with local computers. Lnc2Meth provides a timely and valuable resource that can be applied to significantly expand our understanding of the regulatory relationships between lncRNAs and DNA methylation in various human diseases.

We describe LincSNP 2.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), an updated database that is used specifically to store and annotate disease-associated single nucleotide polymorphisms (SNPs) in human long non-coding RNAs (lncRNAs) and their transcription factor binding sites (TFBSs). In LincSNP 2.0, we have updated the database with more data and several new features, including (i) expanding disease-associated SNPs in human lncRNAs; (ii) identifying disease-associated SNPs in lncRNA TFBSs; (iii) updating LD-SNPs from the 1000 Genomes Project; and (iv) collecting more experimentally supported SNP-lncRNA-disease associations. Furthermore, we developed three flexible online tools to retrieve and analyze the data. Linc-Mart is a convenient way for users to customize their own data. Linc-Browse is a tool for all data visualization. Linc-Score predicts the associations between lncRNA and disease. In addition, we provided users a newly designed, user-friendly interface to search and download all the data in LincSNP 2.0 and we also provided an interface to submit novel data into the database. LincSNP 2.0 is a continually updated database and will serve as an important resource for investigating the functions and mechanisms of lncRNAs in human diseases.