Genome sequence analysis with MonetDB - A case study on Ebola virus diversity
Abstract
Next-generation sequencing (NGS) technology has led the life sciences into
the big data era. Today, sequencing genomes takes little time and cost, but results in
terabytes of data to be stored and analysed. 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.
Domains
Quantitative Methods [q-bio.QM]
Origin : Files produced by the author(s)
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