2D-PAGE Database of Escherichia coli
A database integrating physical (protein-protein) and functional interactions within the context of an E. coli knowledgebase. Presently the resource offers access to two types of network: A network of functional interactions derived through exploiting available functional genomic datasets within a Bayesian framework Two networks of experimentally derived protein-protein interactions - a 'core' network consisting of interactions deemed to be of 'high quality'; and an 'extended' network which extends the 'core' network by including interactions for which experimental evidence is less strong.
Collection of 507 Pathway/Genome Databases. Each database in the BioCyc collection describes the genome and metabolic pathways of a single organism.
Biodefense Proteomics Resource Center
A Database of Protein Post Translational Modifications
A database that curates new experimental and bioinformatic information about the genes and gene products of the model bacterium Escherichia coli K-12 strain MG1655. (more info). To compare E. coli K-12 genomes to those of pathogenic E. coli, use coliBase; for general info use the E. coli index.
European Pathogenic Microorganism Proteome Database
Relational database structure based on MS-Access and MySQL to store and manage proteomics data. The system may be used to publish two-dimensional electrophoretic proteomics data, and also may be accessed by external users who want to compare their own data with those in the databases. The producers of proteomics data do not need to construct a database themselves. Users can introduce mass spectra into the database, which allows the searching of peptide mass fingerprints against their own protein sequence databases
Freely available, open source database system and analysis tools for protein interaction data. All interactions are derived from literature curation or direct user submissions and are freely available.
database of extracellular matrix molecules that interact with one another. A total of 1972 proteinΠprotein and proteinΠcarbohydrate interactions are included, and most of these interactions are from mammals. Data from various interaction databases, experiments, and the scientific literature are integrated.
Metabolic and Signaling Pathways
An integrated data warehouse of proteomic data for mitochondria. Data have been integrated to allow the creation of sophisticated data mining queries spanning many different sources. Hosted and maintained by the Bioinformatics group at the MRC Dunn Human Nutrition Unit
Open Proteomics Database
PEP: Predictions for Entire Proteomes
It is multi-organism, publicly accessible compendium of peptides identified in a large set of tandem mass spectrometry proteomics experiments.
Protein Lists Identified in Proteomics Studies accepts as input a list of protein/gene identifiers. Using statistical analyses PLIPS identifies recently published proteomics studies, which report protein lists that significantly intersects with a query list. PLIPS is a web mining tool which allows collects proteomics-derived protein lists by searching through full text papers and automatically selecting tables, which report a list of protein identifiers. By searching through major proteomics journals, PLIPS now has collected data from approximately 800 independent studies, which reported about 1000 different protein lists.
PRoteomics IDEntifications database. Centralized, standards compliant, public data repository for proteomics data. It has been developed to provide the proteomics community with a public repository for protein and peptide identifications together with the evidence supporting these identifications.
Protein complexes in yeast
Protein sequence databases
Existing scientific literature is a rich source of biological information such as disease markers. Integration of this information with data analysis may help researchers to identify possible controversies and to form useful hypotheses for further validations. In the context of proteomics studies, individualized proteomics era may be approached through consideration of amino acid substitutions/modifications as well as information from disease studies. Integration of such information with peptide searches facilitates speedy, dynamic information retrieval that may significantly benefit clinical laboratory studies.RAId_DbS integrates data from various sources annotated single amino acid polymorphisms, post-translational modifications, and their documented disease associations (if they exist) into one enhanced database per organism. We have also augmented our peptide identification software RAId_DbS to take into account this information while analyzing a tandem mass spectrum. In principle, one may choose to respect or ignore the correlation of amino acid polymorphisms/modifications within each protein. The former leads to targeted searches and avoids scoring of unnecessary polymorphism/modification combinations; the latter explores possible polymorphisms in a controlled fashion. To facilitate new discoveries, RAId_DbS also allows users to conduct searches permitting novel polymorphisms as well as to search a knowledge database created by the users.(publication)
The Human Protein Atlas
The Swedish Human Protein Atlas project, funded by the Knut and Alice Wallenberg Foundation, has been set up to allow for a systematic exploration of the human proteome using Antibody-Based Proteomics. This is accomplished by combining high-throughput generation of affinity-purified antibodies with protein profiling in a multitude of tissues and cells assembled in tissue microarrays. Confocal microscopy analysis using human cell lines is performed for more detailed protein localization. The program hosts the Human Protein Atlas portal with expression profiles of human proteins in tissues and cells.
It provides access to proteomics data from the Serum Biomarker group at the Swiss Federal Institute of Technology (ETH) in Zurich Switzerland, and the Institute for Systems Biology (ISB) in Seattle, Washington USA.
Virtual Proteomics Data Analysis Cluster-ViPDAC uses Amazon Web Services to analyze proteomics data. To use ViPDAC you first sign up for access to AmazonΥs Web Services, then you can log in to the Amazon console which is used to start up a personal copy of the ViPDAC server. This starts ViPDAC running on one of AmazonΥs servers from where you can now access the ViPDAC web interface. At this point normal proteomics analysis procedures take over - you can pick an existing set of parameters or configure a new analysis (cleavage enzyme, modifications, protein database to search, etc) and select your data file (spectra in .mgf format) and then submit the job. ViPDAC will then upload the data file to the your area on AmazonΥs S3 storage and run the analysis using the EC2 nodes available in your cluster. Once the analysis is complete ViPDAC will compile the results and save them back onto S3 where they can be stored or downloaded to the desktop for further analysis. At this point you can continue to do more analyses or simply turn the cluster off until you need it again. One of the benefits of the dynamic nature of the cloud is that if you want the analysis to go faster you can add more nodes to your cluster at any time.
Yeast TAP project
The Yeast TAP Project is aimed at elucidating the entire network of protein-protein interactions in a model eukaryotic organism, namely the yeast Saccharomyces cerevisiae. Our principle approach is based on the use of the highly effective tandem affinity purification (TAP) method developed by Seraphin and colleagues (Rigaut et al. Nature Biotech. 1999) to isolate native protein complexes to virtual homogeneity.