The database of the disease-gene-drug connectivity system^* was recompiled using the latest version of multiple popular computational biology data sources, including HPRD, BioGrid, IntAct, Reactome, Kegg, BioCarta, UniProt and NCBI molecule annotations.

In addition, a list of Intellectual Disability (ID) genes, which once mutated can result in ID, compiled in Feb 2014 by Gardiner’s Lab based on novel gene mutations reported in recently published research articles is also updated to the database to provide integrated information on known and candidate ID genes, and their protein features, protein interactions and associated pathways. One of the goals of this database system is to aid both basic science and clinical researchers in new ID gene knowledge discovery and to facilitate hypothesis generation in the molecular basis of ID. The database is searchable by gene name, disease name, genomic location, among other features. Sample query types include:

1. is gene X a known ID gene? if so, what disease is it associated with, what are the known mutations, and is ID a primary or secondary feature of the disease?
2. what are the protein interaction partners of the gene X protein product? are any of these known ID genes?
3. given the genomic coordinates of a specific patient’s CNV, are there any ID genes within the region? If not, what are the protein interaction pathways that lead from genes within the CNV to ID genes?

Another goal of the database system is to facilitate research on human chromosome 21 and Down syndrome, and in this way, facilitate the development of therapeutics for the prevention or amelioration of phenotypic features.

Down syndrome is the most common genetic cause of intellectual disability. The complete phenotype is both complex, affecting multiple organs and organ systems, and highly variable in severity among individuals. Down syndrome is due to an extra copy of all or part of a normal chromosome 21 and the increased expression, due to gene dosage, of the normal genes encoded by it. The large number of genes involved (>500), their functions and interactions, and the perturbations of cellular pathways and processes their increased expression causes, are challenges in determining gene-phenotype correlations.

The database and associated knowledge discovery tools are designed for researchers interested in individual chromosome 21 genes, groups of genes, their orthologs in models organisms, as well as Down syndrome and mouse models of Down syndrome. By using this approach, the system is to:

• provide curated, annotated comprehensive information on chromosome 21 genes
• reduce redundant efforts in database and literature searches
• provide links to primary data sources for user evaluation
• provide new tools for data mining
• develop pathway annotation for chr21 proteins

The system of this database and associated tools is available online at bioc.tinyray.com. Please refer this report for additional information on the database update. Thank you for reading this article. REFERENCES

• ^*Thanh Le (2013) A Machine Learning approach for Gene Expression analysis and applications.