Next Generation Sequencing (NGS) is a family of technologies for reading the DNA or RNA, capable of producing whole genome sequences at an impressive speed, and causing a revolution of both biological research and medical practice. In this exciting scenario, while a huge number of specialized bio-informatics programs extract information from sequences, there is an increasing need for a new generation of systems and frameworks capable of integrating such information, providing holistic answers to the needs of biologists and clinicians. To respond to this need, we developed GMQL, a new query language for genomic data management that operates on heterogeneous genomic datasets. In this paper, we focus on three domain-specific operations of GMQL used for the efficient processing of operations on genomic regions, and we describe their efficient implementation; the paper develops a theory of binning strategies as a generic approach to parallel execution of genomic operations, and then describes how binning is embedded into two efficient implementations of the operations using Flink and Spark, two emerging frameworks for data management on the cloud.

Framework for Supporting Genomic Operations

Kaitoua, Abdulrahman;Pinoli, Pietro;Bertoni, Michele;Ceri, Stefano
2017-01-01

Abstract

Next Generation Sequencing (NGS) is a family of technologies for reading the DNA or RNA, capable of producing whole genome sequences at an impressive speed, and causing a revolution of both biological research and medical practice. In this exciting scenario, while a huge number of specialized bio-informatics programs extract information from sequences, there is an increasing need for a new generation of systems and frameworks capable of integrating such information, providing holistic answers to the needs of biologists and clinicians. To respond to this need, we developed GMQL, a new query language for genomic data management that operates on heterogeneous genomic datasets. In this paper, we focus on three domain-specific operations of GMQL used for the efficient processing of operations on genomic regions, and we describe their efficient implementation; the paper develops a theory of binning strategies as a generic approach to parallel execution of genomic operations, and then describes how binning is embedded into two efficient implementations of the operations using Flink and Spark, two emerging frameworks for data management on the cloud.
2017
cloud-based systems; data modeling; Genomic data management; operations for genomics; query languages; Software; Theoretical Computer Science; Hardware and Architecture; Computational Theory and Mathematics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1045267
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