PubMed Health⌕ Search

PubMed · 14668218

JXP4BIGI: a generalized, Java XML-based approach for biological information gathering and integration.

Abstract

MOTIVATION: In the post-genomic era, biologists interested in systems biology often need to import data from public databases and construct their own system-specific or subject-oriented databases to support their complex analysis and knowledge discovery. To facilitate the analysis and data processing, customized and centralized databases are often created by extracting and integrating heterogeneous data retrieved from public databases. A generalized methodology for accessing, extracting, transforming and integrating the heterogeneous data is needed. RESULTS: This paper presents a new data integration approach named JXP4BIGI (Java XML Page for Biological Information Gathering and Integration). The approach provides a system-independent framework, which generalizes and streamlines the steps of accessing, extracting, transforming and integrating the data retrieved from heterogeneous data sources to build a customized data warehouse. It allows the data integrator of a biological database to define the desired bio-entities in XML templates (or Java XML pages), and use embedded extended SQL statements to extract structured, semi-structured and unstructured data from public databases. By running the templates in the JXP4BIGI framework and using a number of generalized wrappers, the required data from public databases can be efficiently extracted and integrated to construct the bio-entities in the XML format without having to hard-code the extraction logics for different data sources. The constructed XML bio-entities can then be imported into either a relational database system or a native XML database system to build a biological data warehouse. AVAILABILITY: JXP4BIGI has been integrated and tested in conjunction with the IKBAR system (http://www.ikbar.org/) in two integration efforts to collect and integrate data for about 200 human genes related to cell death from HUGO, Ensembl, and SWISS-PROT (Bairoch and Apweiler, 2000), and about 700 Drosophila genes from FlyBase (FlyBase Consortium, 2002). The integrated data has been used in comparative genomic analysis of x-ray induced cell death. Also, as explained later, JXP4BIGI is a middleware and framework to be integrated with biological database applications, and cannot run as a stand-alone software for end users. For demonstration purposes, a demonstration version is accessible at (http://www.ikbar.org/jxp4bigi/demo.html).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yihua Huang, Tianyun Ni, Lei Zhou, Stanley Su. 2003-12-12. JXP4BIGI: a generalized, Java XML-based approach for biological information gathering and integration.. https://doi.org/10.1093/bioinformatics%2Fbtg327

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

BACKGROUND: Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. RESULTS: Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. CONCLUSION: PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Database Management Systems↗

TaxMan: a taxonomic database manager.

BACKGROUND: Phylogenetic analysis of large, multiple-gene datasets, assembled from public sequence databases, is rapidly becoming a popular way to approach difficult phylogenetic problems. Supermatrices (concatenated multiple sequence alignments of multiple genes) can yield more phylogenetic signal than individual genes. However, manually assembling such datasets for a large taxonomic group is time-consuming and error-prone. Additionally, sequence curation, alignment and assessment of the results of phylogenetic analysis are made particularly difficult by the potential for a given gene in a given species to be unrepresented, or to be represented by multiple or partial sequences. We have developed a software package, TaxMan, that largely automates the processes of sequence acquisition, consensus building, alignment and taxon selection to facilitate this type of phylogenetic study. RESULTS: TaxMan uses freely available tools to allow rapid assembly, storage and analysis of large, aligned DNA and protein sequence datasets for user-defined sets of species and genes. The user provides GenBank format files and a list of gene names and synonyms for the loci to analyse. Sequences are extracted from the GenBank files on the basis of annotation and sequence similarity. Consensus sequences are built automatically. Alignment is carried out (where possible, at the protein level) and aligned sequences are stored in a database. TaxMan can automatically determine the best subset of taxa to examine phylogeny at a given taxonomic level. By using the stored aligned sequences, large concatenated multiple sequence alignments can be generated rapidly for a subset and output in analysis-ready file formats. Trees resulting from phylogenetic analysis can be stored and compared with a reference taxonomy. CONCLUSION: TaxMan allows rapid automated assembly of a multigene datasets of aligned sequences for large taxonomic groups. By extracting sequences on the basis of both annotation and BLAST similarity, it ensures that all available sequence data can be brought to bear on a phylogenetic problem, but remains fast enough to cope with many thousands of records. By automatically assisting in the selection of the best subset of taxa to address a particular phylogenetic problem, TaxMan greatly speeds up the process of generating multiple sequence alignments for phylogenetic analysis. Our results indicate that an automated phylogenetic workbench can be a useful tool when correctly guided by user knowledge.

Database Management Systems↗