Toward Efficient Variant Calling Inside Main-Memory Database Systems

Dorok, Sebastian; Breß, Sebastian; Saake, Gunter

doi:10.1109/dexa.2014.25

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…The work of Dorok et al [4] is closest related to our work. It uses a column oriented main memory DBMS to perform in-database variant calling using simple SQL.…”

Section: Related Workmentioning

confidence: 69%

“…Moreover, a bigger part of genetic analyses pipeline should be natively supported by MonetDB/BAM. For example, supporting a base-oriented database schema as described by Dorok et al [4], and building in support for loading reference and/or index files, enables users to run in-database variant calling algorithms. Furthermore, implementing indatabase support for VCF files eliminates yet another part of the genetic analyses pipeline that is normally file-based.…”

Section: Future Workmentioning

confidence: 99%

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Genome sequence analysis with MonetDB

et al. 2015

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Next-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but yields terabytes of data to be stored and analyzed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virus genomes.

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“…The work of Dorok et al [4] is closest related to our work. It uses a column oriented main memory DBMS to perform in-database variant calling using simple SQL.…”

Section: Related Workmentioning

confidence: 69%

Section: Future Workmentioning

confidence: 99%

Genome sequence analysis with MonetDB

et al. 2015

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“…In our previous work, we chose the Feb2013-SP2 release of MonetDB to evaluate the query and analysis performance of our approach [10]. We extended MonetDB with a handwritten user-defined aggregation function to support variant calling.…”

Section: Querying and Storing Genome Datamentioning

confidence: 99%

The Relational Way To Dam The Flood Of Genome Data

Dorok

2015

Proceedings of the 2015 ACM SIGMOD on PhD Symposium

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Mutations in genomes can indicate a predisposition for diseases such as cancer or cardiovascular disorder. Genome analysis is an established procedure to determine mutations and deduce their impact on living organisms. The first step in genome analysis is DNA sequencing that makes the biochemically stored hereditary information in DNA digitally readable. The cost and time to sequence a whole genome decreases rapidly and leads to an increase of available raw genome data that must be stored and integrated to be analyzed. Damming this flood of genome data requires efficient and effective analysis as well as data management solutions. State-of-the-art in genome analysis are flat-file-based storage and analysis solutions. Consequently, every analysis application is responsible to manage data on its own, which leads to implementation and process overhead.Database systems have already shown their ability to reduce data management overhead for analysis applications in various domains. However, current approaches using relational database systems for genome-data management lack scalable performance on increasing amounts of genome data. In this thesis, we investigate the capabilities of relational main-memory database systems to store and query genome data efficiently, while enabling flexible data access.

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“…First results on variant calling indicate that an integration of genome analysis tasks into a main-memory DBMS is feasible and can be beneficial regarding performance [5].…”

Section: Addressing Data-management Challengesmentioning

confidence: 99%

“…Such dictionary encoding scheme can be efficiently applied to column-stores [1] that are often used in main-memory DBMSs. In recent work, we have shown that DNA sequencing reads can be efficiently compressed using compression schemes such as dictionary encoding [5].…”

Section: A U T H O R ' S V E R S I O Nmentioning

confidence: 99%

Toward efficient and reliable genome analysis using main-memory database systems

Dorok

Breß

Läpple

et al. 2014

Proceedings of the 26th International Conference on Scientific and Statistical Database Management

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Improvements in DNA sequencing technologies allow to sequence complete human genomes in a short time and at acceptable cost. Hence, the vision of genome analysis as standard procedure to support and improve medical treatment becomes reachable. In this vision paper, we describe important data-management challenges that have to be met to make this vision come true. Besides genome-analysis performance, data-management capabilities such as data provenance and data integrity become increasingly important to enable comprehensible and reliable genome analysis. We argue to meet these challenges by using main-memory database technologies, which combine fast processing capabilities with extensive data-management capabilities. Finally, we discuss possibilities of integrating genome-analysis tasks into DBMSs and derive new research questions.

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Toward Efficient Variant Calling Inside Main-Memory Database Systems

Cited by 3 publications

References 22 publications

Genome sequence analysis with MonetDB

Genome sequence analysis with MonetDB

The Relational Way To Dam The Flood Of Genome Data

Toward efficient and reliable genome analysis using main-memory database systems

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