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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
Published on: December 22, 2017
Asterias: a parallelized web-based suite for the analysis of expression and aCGH data.
Andreu Alibés1, Edward R Morrissey, Andrés Cañada
1Statistical Computing Team, Structural and Computational Biology Programme, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, Madrid, Spain.
Asterias provides freely accessible web tools for analyzing gene expression and array comparative genomic hybridization (aCGH) data. These parallelized tools accelerate complex disease research, including cancer, by enabling faster data analysis and biomarker discovery.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Complex diseases, particularly cancer, require analysis of gene expression and CGH arrays for diagnosis, prognosis, and understanding disease mechanisms.
- Existing computational tools often lack the speed and integration needed for comprehensive analysis of large-scale genomic data.
Purpose of the Study:
- To introduce Asterias, an integrated suite of web tools for analyzing gene expression and aCGH data.
- To enhance the efficiency and accessibility of genomic data analysis through parallel computing and web-based accessibility.
Main Methods:
- Development of a web-based platform integrating multiple analysis tools.
- Implementation of parallel computing (MPI) on a 60-CPU server for significant speed-up (up to 50x).
- Inclusion of tools for data normalization, identifier conversion, filtering, imputation, differential gene expression analysis, class prediction, survival analysis, and genomic region detection.
Main Results:
- Asterias offers a unique, integrated platform for gene expression and aCGH data analysis.
- Parallelized computation significantly accelerates analysis compared to non-parallelized applications.
- Tools provide access to additional functional gene information via clickable links.
Conclusions:
- Asterias provides a powerful, efficient, and accessible solution for complex genomic data analysis in disease research.
- The integrated and parallelized nature of Asterias offers unique advantages for web-based bioinformatics applications.
- Facilitates biomarker discovery and enhances understanding of cancer development and metastasis.
