miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

BACKGROUND

MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.

METHODS

Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G 1, G 2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G 1 (G 2) is a gene/protein interacting with T (G 1). Each sequence (T, G 1, G 2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.

RESULTS

A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer's disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

Computational methodologies for studying non-coding RNAs relevant to central nervous system function and dysfunction

Non-coding RNAs (ncRNAs) are a large and diverse group of transcripts that span the eukaryotic genome, of which less than 2% encodes proteins. Several distinct families of ncRNAs have been described and implicated in many aspects of central nervous system (CNS) function including translation, RNA metabolism, gene regulation, and development. The need to distinguish ncRNAs from sequence data, as well as potentially uncovering novel ncRNA families, has ignited the development of customized computational approaches and bioinformatic resources to handle these tasks. In this review, we provide an overview of the numerous procedures developed to predict ncRNAs based on their primary sequence and predicted secondary structure. These methodologies are broadly grouped into genome scanning algorithms, mixed approaches, and machine learning algorithms. Regulatory ncRNAs, particularly microRNAs (miRNAs), are a major focus of current research efforts and this review will therefore center on the prediction of miRNAs and the putative gene targets they act upon. With the advent of ultra high-throughput sequencing technologies 'deep sequencing' has emerged as the cutting-edge method for ncRNA identification and we will also touch on some computational resources that play a key role in analysis of this type of data.