Abstract

It is known from recent studies that more than 90% of human multi-exon genes are subject toAlternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a singlegene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiationand pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to thestudy of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon1.0 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 userfriendlybioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a datawarehouse 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 statisticalmethods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples andtuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samplesproduced 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 biochemicalpathways annotations are integrated with exon and gene level expression plots. The user can customize the resultschoosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samplesfor a multivariate AS analysis.Conclusions: Despite exon array chips being widely used for transcriptomics studies, there is a lack of analysistools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendlyplatform for a comprehensive study of AS events in human diseases, displaying the analysis results with easilyinterpretable and interactive tables and graphics.

Autore Pugliese

• Ianigro Massimo , Consiglio Arianna , Grillo Giorgio , Liuni Sabino , Licciulli Vito Flavio

Tutti gli autori

• Consiglio A.; Carella M.; De Caro G.; Delle Foglie G.; Giovannelli C.; Grillo G.; Ianigro M.; Licciulli F.; Palumbo O.; Piepoli A.; Ranieri E.; Liuni S.

Titolo volume/Rivista

BMC bioinformatics

Anno di pubblicazione

2012

ISSN

1471-2105

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile