News

• 10/10/2009: Verso un nuovo marcatore tumorale, il micro RNA (comunicato stampa).
• 19/7/2009: A-MADMAN paper tagged as highly accessed in BMC Bioinformatics
• March 2009: Chiogna et al. paper tagged as highly accessed in BMC Bioinformatics
• January 2009: Calura et al. paper tagged as highly accessed in BMC Bioinformatics
• 13/7/2009: A-MADMAN paper reaches the most viewed papers of BMC Bioinformatics.
• 3/4/2009: Metastasi, scoperto il gene che la blocca (comunicato stampa).

Description of the project

The research project aims at establishing the methodological bases and developing the computational infrastructures to investigate, through a systems biology approach, the networks of molecular interactions characterizing skeletal muscle plasticity in different physio-pathological conditions.
As a first pilot-study, previously available gene expression signatures have been analyzed through a meta-analysis approach to decipher gene interaction networks in early and late stages of muscle atrophy. In parallel, A-MADMAN (Annotation-based MicroArray Data Meta ANalysis tool) web application was developed to retrieve, annotate, manage and analyse the muscle project data. By using this facility, data pertaining to more that 608 gene expression experiments, retrieved from GEO public repository, were used to populate a custom-created muscle database. This accounts for expression data of muscle tissues from healthy individuals of different age (infant, young, adult and old), in normal conditions, comprising people untrained and trained with different kinds of exercises, or after immobilisation; different muscle diseases are also represented. The skeletal muscle tissue constitutes the experimental model and the biological focus of the project, with the applicative aim of investigating and modeling networks of molecular and regulatory interactions in healthy and diseased muscle cells. To solve these biological questions and from a methodological point of view we:

• Enhanced a methodology to detect density variations of specific features along the genome sequence and of chromosomal regions with structural and transcriptional imbalances from the integrated analysis of gene expression and other types of genomic information (e.g., gene dosage).
• Updated previously developed computational tool to the identification of locally enriched genomic features (REEF, REgionally Enriched Features, software).
• Expanded the Locally Adaptive Statistical procedure (LAP) for the identification of differentially expressed chromosomal regions, producing a bioinformatics procedure integrating copy number, obtained from SNP mapping arrays, with transcriptional data, the identification of genome-wide, concurrent alterations of copy number and regional gene expression in single samples, and the extension of the integrative analysis to entire datasets.
• Built a custom chip definition files for platform annotation and matching.
• Developed a framework for the integrated study of co-expression, co-regulation, co-localization and functional similarity, for understanding basic and general rules governing genomic expression.
• Defined novel strategies for the combination of miRNA and mRNA expression signals for the reconstruction of miRNA-based regulatory networks.
• Evaluated methods and algorithms for network reconstruction and analysis.