CFGP: a web-based, comparative fungal genomics platform

Since the completion of the Saccharomyces cerevisiae genome sequencing project in 1996, the genomes of over 80 fungal species have been sequenced or are currently being sequenced. Resulting data provide opportunities for studying and comparing fungal biology and evolution at the genome level. To support such studies, the Comparative Fungal Genomics Platform (CFGP; http://cfgp.snu.ac.kr), a web-based multifunctional informatics workbench, was developed. The CFGP comprises three layers, including the basal layer, middleware and the user interface. The data warehouse in the basal layer contains standardized genome sequences of 65 fungal species. The middleware processes queries via six analysis tools, including BLAST, ClustalW, InterProScan, SignalP 3.0, PSORT II and a newly developed tool named BLASTMatrix. The BLASTMatrix permits the identification and visualization of genes homologous to a query across multiple species. The Data-driven User Interface (DUI) of the CFGP was built on a new concept of pre-collecting data and post-executing analysis instead of the ‘fill-in-the-form-and-press-SUBMIT’ user interfaces utilized by most bioinformatics sites. A tool termed Favorite, which supports the management of encapsulated sequence data and provides a personalized data repository to users, is another novel feature in the DUI

Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecule with the ability to restore native function to cancerous p53 mutants is a long-held medical goal of cancer treatment. The Cancer Research DataBase (CRDB) was designed in support of a project to find such small molecules. As a cancer informatics project, the CRDB involved small molecule data, computational docking results, functional assays, and protein structure data. As an example of the hybrid strategy for data integration, it combined the mediation and data warehousing approaches. This paper uses the CRDB to illustrate the hybrid strategy as a viable approach to heterogeneous data integration in biomedicine, and provides a design method for those considering similar systems. More efficient data sharing implies increased productivity, and, hopefully, improved chances of success in cancer research. (Code and database schemas are freely downloadable, http://www.igb.uci.edu/research/research.html.)

Implementation Of Mediator-Based Integration System For A Transparent Access To Multiple Biological Databases.

Existing biological databases seem to function independently from each other. Different organizations and research group, build customised databases with different formats for collecting biological data through their research

Biomedical data integration in computational drug design and bioinformatics

[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT- 0366Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-

Comparative evaluation of microarray-based gene expression databases

Microarrays make it possible to monitor the expression of thousands of genes in parallel thus generating huge amounts of data. So far, several databases have been developed for managing and analyzing this kind of data but the current state of the art in this field is still early stage. In this paper, we comprehensively analyze the requirements for microarray data management. We consider the various kinds of data involved as well as data preparation, integration and analysis needs. The identified requirements are then used to comparatively evaluate eight existing microarray databases described in the literature. In addition to providing an overview of the current state of the art we identify problems that should be addressed in the future to obtain better solutions for managing and analyzing microarray data

TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/

FlyMine: an integrated database for Drosophila and Anopheles genomics.

FlyMine is a data warehouse that addresses one of the important challenges of modern biology: how to integrate and make use of the diversity and volume of current biological data. Its main focus is genomic and proteomics data for Drosophila and other insects. It provides web access to integrated data at a number of different levels, from simple browsing to construction of complex queries, which can be executed on either single items or lists

Flexible Integration of Molecular-Biological Annotation Data: The GenMapper Approach

Molecular-biological annotation data is continuously being collected, curated and made accessible in numerous public data sources. Integration of this data is a major challenge in bioinformatics. We present the GenMapper system that physically integrates heterogeneous annotation data in a flexible way and supports large-scale analysis on the integrated data. It uses a generic data model to uniformly represent different kinds of annotations originating from different data sources. Existing associations between objects, which represent valuable biological knowledge, are explicitly utilized to drive data integration and combine annotation knowledge from different sources. To serve specific analysis needs, powerful operators are provided to derive tailored annotation views from the generic data representation. GenMapper is operational and has been successfully used for large-scale functional profiling of genes