Welcome to the Pancreatic Expression Database Version 3.0
To broaden the range of users and subsequently increase its functionality, we have substantially changed and improved this resource by expanding the -omics, selections, specimens and annotation data types.
The database contains data on 56015 differential expression or expression measurements and 6363 copy number variations extracted from 59 published transcriptomics, proteomics, miRNA or genomics studies of various pancreatic normal, malignant or benign tissues, body fluids, cell lines and mouse models under different treatment conditions. This describes pancreatic related-regulation events in 8938 genes/proteins, 28448 transcripts, and 279 miRNAs as well as 2771 gains, 1073 losses, 347 homozygous deletions, 1297 high-level amplifications and 875 Loss of heterozygosity (LOH) events occurring in distinct genomic areas.
Our database comprises -omics data from a wide range of specimens derived from tissues, fine needle aspirates and body fluids of healthy people and patients with pancreatic malignant or benign diseases.
These are stored alongside information on different treatments and profiling data from cell lines and mouse models.
These samples have been profiled on a wide range of miRNA, transcriptomics, genomics or proteomics platforms.
All the studies were manually processed, checked for accuracy and consistency and loaded into our relational database alongside annotations from several public resources such as Reactome, Ensembl, GO ontologies, dbSNP, multi-species comparisons, UniProt and the Human protein atlas. We imported the available Ensembl human genome annotations (Ensembl release 56) for genes and proteins, SNP information, sequences, gene structure and multi-species data enabling the integration and annotation of heterogeneous pancreatic data. In order to avoid integration and annotations errors, we used the pre-established Ensembl annotations and microarray probe set mapping. Ensembl links to Human Protein Atlas, UniProt/Swiss-Prot, RefSeq and UniProt/TrEMBL databases are made on the basis of sequence similarity. All other subsequent links are inferred from these mappings. Ensembl also establishes mappings to microarray probe set identifiers by matching probe set sequences to Ensembl transcripts. We also added the Reactome data to expand data mining to de-regulated pathways.
The database can be interrogated using combined criteria from pancreatic (disease stages, regulation, differential expression, expression, platform technology, publication) and/or public data (pathways, antibodies, genomic region, gene-related accessions, ontology, expression patterns, multi-species comparisons, protein data, SNPs). Thus, our database enables connections between otherwise disparate data sources and allows relatively simple navigation between all data types and annotations. Users can select to display or download the results to a file as 'HTML', 'CSV' for comma-separated values, 'TSV' for tab-separated values, 'XLS' for Excel, 'ADF' for array description format. One can select a compressed file output and the query will run in the background to be downloaded later. One needs to provide an e-mail address to receive a URL in a notification e-mail that allows the query results to be downloaded.
We believe that interoperability is a key factor in the utility and productive use of any current and future cancer database. This is essential to ensure the sustainability of any cancer database and facilitate its integration with major international efforts in cancer research such as the International Cancer Genome Consortium (ICGC), supported by the Biomart technology platform and The Cancer Genome Atlas (TCGA), supported by the Cancer Biomedical Bioinformatics Grid (caBIGTM) technology platform. This also will allow the design and implementation of more sophisticated analysis portals. The cancer research community needs open source fully interoperable resources allowing information connectivity and data sharing. Only these types of resource can ensure that cancer data generated across different organisations are shared, thereby maximising the impact of cancer research. By using the same BioMart technology for its data management system, our platform is fully interoperable with the ICGC. Through its web service layer, it also is interoperable with The Cancer Genome Atlas (TCGA) through its data mining platform caBIGTM. Similarly, our bioinformatics platform is integrated with other complementary resources such as Ensembl and Reactome.