| Abstract | Background: It 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 userfriendly
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.
Results: BEAT 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.
Conclusions: Despite 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. |
| Authors | Arianna Consiglio 1, Massimo Carella 2, Giorgio De Caro 1, Gianfranco Delle Foglie 1, Candida Giovannelli 3, Giorgio Grillo 1, Massimo Ianigro 3, Flavio Licciulli 1, Orazio Palumbo 2, Ada Piepoli 4, Elena Ranieri 5, Sabino Liuni 1 |
| Text | 213700 2012 10.1186/1471 2105 13 S4 S21 PubMed 22536968 bioinformatics exon array BEAT Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments Arianna Consiglio 1, Massimo Carella 2, Giorgio De Caro 1, Gianfranco Delle Foglie 1, Candida Giovannelli 3, Giorgio Grillo 1, Massimo Ianigro 3, Flavio Licciulli 1, Orazio Palumbo 2, Ada Piepoli 4, Elena Ranieri 5, Sabino Liuni 1 1 Institute for Biomedical Technologies of Bari ITB, National Research Council, Bari, 70126, Italy. 2 Medical Genetics Unit, Casa Sollievo della Sofferenza IRCCS, San Giovanni Rotondo Foggia, 71013, Italy. 3 Institute of Intelligent Systems for Automation ISSIA, National Research Council, Bari, 70126, Italy. 4 Department and Laboratory of Gastroenterology Unit, Casa Sollievo della Sofferenza IRCCS, San Giovanni Rotondo Foggia, 71013, Italy. 5 Department of Biomedical Science, University of Foggia, Foggia, 71122, Italy Background It 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 userfriendly 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. Results BEAT 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. Conclusions Despite 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. 13 Suppl. 4 Published version Articolo in rivista BioMed Central, 1471 2105 BMC bioinformatics BMC bioinformatics BMC bioinformatics BMC bioinformatics BioMed Central bioinformatics Bioinformatics arianna.consiglio CONSIGLIO ARIANNA DE CARO GIORGIO massimo.ianigro IANIGRO MASSIMO vitoflavio.licciulli LICCIULLI VITO FLAVIO giorgio.grillo GRILLO GIORGIO sabino.liuni LIUNI SABINO candida.giovannelli GIOVANNELLI CANDIDA |
