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 \textit{easy}, \textit{flexible}, and \textit{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.

Additional Metadata
Keywords MonetDB BAM
THEME Information (theme 2), Life Sciences (theme 5)
Publisher Springer Verlag
Stakeholder Unspecified
Journal Datenbank Spektrum
Project The SciLens-II Infrastructure, Big Data at work
Grant This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/621.016.201 - The Scilens-II Infrastructure, Big Data at work
Cijvat, C.P, Manegold, S, Kersten, M.L, Klau, G.W, Schönhuth, A, Marschall, T, & Zhang, Y. (2015). Genome sequence analysis with MonetDB - A case study on Ebola virus diversity. Datenbank Spektrum, 15(5), 185–191.