Bioinformatics Computational Biology Computational Databases

Computational Databases

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Computational Databases or Bioinformatics has been defined as the application of mathematical and Computer Science methods to solving problems in Molecular Biology that require large scale data, computation, and analysis. As expected, Molecular Biology databases play an essential role in Computational Biology research and development.

Bioinformatics is growing rapidly and has found many new applications and its progress caused the interaction with other fields. Thus presently, the Computational Intelligence based on IT in bioinformatics is used to archive, search, display, analyze and interpret biological data. Development of bioinformatics and growing need of actuarial science, computer, and programming are resulting in the growth of new Computational Intelligence methods, tools, computer algorithms, and programming solutions. 

Bioinformatics or computational databases refers to the development of new database methods to store genomic information, computational software programs, and methods to extract, process, and evaluate this information, and the refinement of existing techniques to acquire the genomic data. Finding genes and determining their function, predicting the structure of proteins and RNA sequences from the available DNA sequence, and determining the evolutionary relationship of proteins and DNA sequences are also part of bioinformatics.

BIND (Biomolecular Interaction Network Database)

It stores descriptions of interactions, molecular complexes and pathways.

MIPS (Munich Information center for Protein Sequences)

It provides whole genome protein sequence-based information for various model organisms, integrating a number of databases (each devoted to a specific organism or contextual focus).

DIP (Database of Interacting Proteins)

It stores protein-protein interactions, including physical associations and chemical reactions and chemical states of those proteins. 

CYGD (Comprehensive Yeast Genome Database)

It summarizes current knowledge (i.e., chromosomes/genes, and functional interaction therein) regarding the 6,200+ open reading frames (ORFs) within the Saccharomyces cerevisiae genome, with each ORF having a length of more than 99 amino acid residues. 


It is an NIH-funded database reporting metabolic and signaling pathway information for Escherischia coli. 

MINT (Molecular Interaction Network Database) 

It is a molecular interactions database assembled from the literature and manually input. In addition to a simple relational schema for showing binary relations, MINT records information about protein post-translational modifications, experimental metadata, cellular location, pathway participation and known complexes. 


It reports published, peer-reviewed, quantitative models of biochemical and cellular systems, with manually curated annotations and cross-references to other relevant data resources. 

DOQCS (Database of Quantitative Cellular Signaling)

It is a database documenting chemical kinetic models of signaling networks. The objective underlying the DOCKS project was to aggregate experimental data and to facilitate collaborations between biologists and modelers to unravel key mechanistic aspects of cell signalling.


It is a database component of the UniProtKB/Swiss-Prot collaboration designed to store metabolic pathway records. It provides a controlled vocabulary for describing the pathways and their associated proteins. 


It is a database and also an information management system through which to analyze chemical interactions and metabolic pathways. STITCH draws upon reported crystal structure information, as well as experimentally obtained drug-target interactions. It can also generate predictions about chemical activity through comparisons using text mining and structural similarity.


It is a drug discovery database combining detailed target information with pharmaceutically relevant drug information. With regards to drug targets, DrugBank stores raw sequence data, as well as conformational structures and links to associated pathways and splice variants. The drug information records contain a number of properties relevant to drug discovery such as mode of action and pharmacokinetic profile. 

TTD (The Therapeutic Target Database)

It is a drug-target database focused on therapeutic applications. It indexes protein and nucleic acid targets, annotating them with information about pathway participation, disease association, corresponding drugs and other relevant properties. These records are cross-referenced with other databases noting sequences, structures, binding affinities, and clinical results, among other details. 


It is a web-based database for computer-aided drug design implemented in the University of Tokyo. It provides information on binding affinities, chemical and target protein structures. 2D and 3D structure visualizations are accessible for most compounds. 

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