IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis

Abstract

BackgroundNext-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics).ResultsWe designed the Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD, http://bioinfo.hsc.unt.edu/ipad), defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources.IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study.ConclusionsIPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity...

Network-based approaches for drug response prediction and targeted therapy development in cancer

“…Among others, Gene Set enrichment analysis (GSEA) using biological pathways and highthroughput data helps to understand molecular processes implicated in the disease [19]. More global systematic enrichment analysis of cancer high-throughput data, using IPAD resource, helps revealing not only enriched signaling pathways but also associations between the patient data and various parameters as disease type, drug specificity and organ specificity, collectively allowing to classify patients and figure out drug susceptibility [20]. More advanced concordant integrative gene set enrichment analysis takes into account multiple expression data sets and pathways resources for consolidation of enriched processes in different cancers and suggestion of deregulated mechanism for therapeutic intervention [21] (Table 2).…”

Biotechnology Landscape in Cancer Drug Discovery

“…Therefore, systematically integrating pathway – disease – drug – organ is crucial for understanding the interrelationships between signaling regulatory pathways and drug action. This integration is based on high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics) [10,11]. Recently FDA has approved drugs that aim pathologies triggered by germ line DNA variations, these pathologies being mainly from the oncology domain [12].…”

SASD: the S ynthetic A lternative S plicing D atabase for identifying novel isoform from proteomics

“…First, we extracted information on gene structures of all genes in the Ensembl Genes 71 database [19] and incorporated the IPAD database [20]. Then, we compiled artificial splicing transcripts.…”

“…The SASD, located at http://bioinfo.hsc.unt.edu/sasd is a comprehensive database containing 56,630 genes (Ensembl gene IDs), 95,260 transcripts (Ensembl transcript IDs), and 11,919,779 Alternative Splicing peptides (1,200,494 EXON_NM; 1,005,388 E_E_NM; 1,005,368 E_I_AS; 1,005,344 I_E_AS; 6,709,352 E_E_AS; and 993,833 INTRON_AS), and also covering about 1,956 pathways, 6,704 diseases, 5,615 drugs, and 52 organs incorporated from the IPAD [20]. …”