Biological Data Warehousing System for Identifying Transcriptional Regulatory Sites From Gene Expressions of Microarray Data

Tsou, Ann-Ping; Sun, Yiming; Liu, Chia‐Lin; Huang, Hsien-Da; Horng, Jorng-Tzong; Tsai, Meng‐Feng; Liu, Baw-Juine

doi:10.1109/titb.2005.862466

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…The data in the warehouse are filtered, aggregated and stored in smaller data storages, usually called data marts (DM), properly designed for specialised purposes. A DW is frequently used in business applications but in the last years it is often used also in the biomedical (especially clinical) domain [41-44]. The choice of a DW for BEAT data management was driven by the following aspects:…”

Section: Methodsmentioning

confidence: 99%

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments

et al. 2012

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BackgroundIt is known from recent studies that more than 90% of human multi-exon genes are subject to Alternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a single gene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiation and pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to the study of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon 1.0 ST Arrays offer a more detailed view of the gene expression profile providing information on the AS patterns. The exon array technology, with more than five million data points, can detect approximately one million exons, and it allows performing analyses at both gene and exon level. In this paper we describe BEAT, an integrated user-friendly bioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a data warehouse approach with some rigorous statistical methods for assessing the AS of genes involved in diseases. Meta statistics are proposed as a novel approach to explore the analysis results. BEAT is available at http://beat.ba.itb.cnr.it.ResultsBEAT is a web tool which allows uploading and analyzing exon array datasets using standard statistical methods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples and tuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samples produced at the Medical Genetics Unit of IRCCS Casa Sollievo della Sofferenza.To highlight all possible AS events, alternative names, accession Ids, Gene Ontology terms and biochemical pathways annotations are integrated with exon and gene level expression plots. The user can customize the results choosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samples for a multivariate AS analysis.ConclusionsDespite exon array chips being widely used for transcriptomics studies, there is a lack of analysis tools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendly platform for a comprehensive study of AS events in human diseases, displaying the analysis results with easily interpretable and interactive tables and graphics.

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Section: Methodsmentioning

confidence: 99%

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments

et al. 2012

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“…In general, data warehousing is suited for applications with predictable queries, applications requiring high query performance, and applications needing private copies of the data. Biomedical database integration systems that use the data warehousing approach include IGD (Ritter et al 1994), GIMS (Cornell et al 2001), GUS (Davidson et al 2001), DoTS (http://www.allgenes.org/), Qu et al (2002), and Tsou et al (2006).…”

Section: Data Warehousingmentioning

confidence: 99%

Heterogeneous Biomedical Database Integration using a Hybrid Strategy: A P53 Cancer Research Database

et al. 2006

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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.)

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“…Analysis of genetic patient data helps in guiding diagnostic and therapeutic work, especially in determining between different causes of diseases with rather similar findings and signs on macroscopic level [1,13,[2][3][4]. Substantial work based on differential coexpression analysis of microarray data [5,6,13] will increase the understanding of cancer and will hopefully lead to improvements in diagnosis and treatment. However, with the increasing use of vast amounts of existing data researchers have to pay attention to several traps caused by misfits of original and current purposes of referred data.…”

Section: Introductionmentioning

confidence: 99%

Section 2: Patient Records: Integrating Bioinformatics into Clinical Practice: Progress and Evaluation

Lang¹

2007

Yearb Med Inform

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SummaryTo summarize current excellent research in the field of bioinformatics.Synopsis of the articles selected for the IMIA Yearbook 2007.Current research in the field of bioinformatics is characterized by careful evaluation of methods and by improved integration of methods into clinical workflows. Ongoing research on genetic causes of diseases is performed on more and better sources of reference data (genome sets and respective annotations), but is still hampered by insufficient, lacking or biased patient data. The application area of bioinformatics has been broadened, leading to amendment or even replacement of traditional methods in fields like characterization of microorganisms. Researchers carry out thorough statistical analyses in order to ensure quality and methodological correctness of new methods based on bioinformatic approaches which are more and more competitive compared to well-established techniques.The best paper selection of articles on bioinformatics shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of reliable and comprehensive data sources is affirmed, while technical development draws attention to the increasing problem of comparability of data derived some years ago with weaker equipment and those that are of up-to-date quality.

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Biological Data Warehousing System for Identifying Transcriptional Regulatory Sites From Gene Expressions of Microarray Data

Cited by 3 publications

References 42 publications

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments

Heterogeneous Biomedical Database Integration using a Hybrid Strategy: A P53 Cancer Research Database

Section 2: Patient Records: Integrating Bioinformatics into Clinical Practice: Progress and Evaluation

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