The emerging medical models aim at leveraging on high-throughput genome sequencing technologies to better target drugs to patients' personal profiles so as to increase their effectiveness. However, the huge amount of data made available by these technologies calls for sophisticated and automated analysis techniques. In this direction we present GOLAM, a framework for OLAP analysis and mining of matches between genomic regions extracted from ENCODE, a worldwide-available collection of shared genomic data. The goal of GOLAM is to overcome the current limitations of genome analysis methods, that are normally based on browsing. This is done by partially automating and speeding-up the analysis process on the one hand, by making it more flexible and introducing a multi-resolution view of data on the other. The framework has been partially implemented so far; in this paper we focus on conveying its potential and on describing its functional architecture and the underlying data models.
GOLAM: A Framework for Analyzing Genomic Data / Lorenzo Baldacci; Matteo Golfarelli; Simone Graziani; Stefano Rizzi. - ELETTRONICO. - (2014), pp. 3-12. (Intervento presentato al convegno International Workshop on Data Warehousing and OLAP tenutosi a Shangai nel 7/11/2014) [10.1145/2666158.2666175].
GOLAM: A Framework for Analyzing Genomic Data
BALDACCI, LORENZO;GOLFARELLI, MATTEO;GRAZIANI, SIMONE;RIZZI, STEFANO
2014
Abstract
The emerging medical models aim at leveraging on high-throughput genome sequencing technologies to better target drugs to patients' personal profiles so as to increase their effectiveness. However, the huge amount of data made available by these technologies calls for sophisticated and automated analysis techniques. In this direction we present GOLAM, a framework for OLAP analysis and mining of matches between genomic regions extracted from ENCODE, a worldwide-available collection of shared genomic data. The goal of GOLAM is to overcome the current limitations of genome analysis methods, that are normally based on browsing. This is done by partially automating and speeding-up the analysis process on the one hand, by making it more flexible and introducing a multi-resolution view of data on the other. The framework has been partially implemented so far; in this paper we focus on conveying its potential and on describing its functional architecture and the underlying data models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
