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Recently, the attention of the research community has been focused on long non-coding RNAs (lncRNAs) and their physiological/pathological implications. As the number of experiments increase in a rapid rate and transcriptional units are better annotated, databases indexing lncRNA properties and function gradually become essential tools to this process. Aim of DIANA-LncBase (www.microrna.gr/LncBase) is to reinforce researchers' attempts and unravel microRNA (miRNA)-lncRNA putative functional interactions.

Research interests in microRNAs have increased rapidly in the past decade. Many studies have showed that microRNAs have close relationships with various human cancers, and they potentially could be used as cancer indicators in diagnosis or as a suppressor for treatment purposes. There are several databases that contain microRNA-cancer associations predicted by computational methods but few from empirical results.

Ultra-deep sampling of small RNA libraries by next-generation sequencing has provided rich information on the microRNA (miRNA) transcriptome of various plant species. However, few computational tools have been developed to effectively deconvolute the complex information.

RNA silencing is a complex, highly conserved mechanism mediated by small RNAs (sRNAs), such as microRNAs (miRNAs), that is known to be involved in a diverse set of biological functions including development, pathogen control, genome maintenance and response to environmental change. Advances in next generation sequencing technologies are producing increasingly large numbers of sRNA reads per sample at a fraction of the cost of previous methods.

MicroRNAs (miRNAs) are short, non-protein coding RNAs that direct the widespread phenomenon of post-transcriptional regulation of metazoan genes. The mature approximately 22-nt long RNA molecules are processed from genome-encoded stem-loop structured precursor genes. Hundreds of such genes have been experimentally validated in vertebrate genomes, yet their discovery remains challenging, and substantially higher numbers have been estimated.

By broad literature survey, we have developed a leaf senescence database (LSD, http://www.eplantsenescence.org/) that contains a total of 1145 senescence associated genes (SAGs) from 21 species. These SAGs were retrieved based on genetic, genomic, proteomic, physiological or other experimental evidence, and were classified into different categories according to their functions in leaf senescence or morphological phenotypes when mutated.