An optimized data structure for high-throughput 3D proteomics data: mzRTree.
As an emerging field, MS-based proteomics still requires software tools for efficiently storing and accessing experimental data. In this work, we focus on the management of LC-MS data, which are typically made available in standard XML-based portable formats. The structures that are currently employed to manage these data can be highly inefficient, especially when dealing with high-throughput profile data. LC-MS datasets are usually accessed through 2D range queries. Optimizing this type of operation could dramatically reduce the complexity of data analysis. We propose a novel data structure for LC-MS datasets, called mzRTree, which embodies a scalable index based on the R-tree data structure. mzRTree can be efficiently created from the XML-based data formats and it is suitable for handling very large datasets. We experimentally show that, on all range queries, mzRTree outperforms other known structures used for LC-MS data, even on those queries these structures are optimized for. Besides, mzRTree is also more space efficient. As a result, mzRTree reduces data analysis computational costs for very large profile datasets.
Nasso
Sara
S
Department of Information Engineering, University of Padova, Padova Italy. sara.nasso@dei.unipd.it
Silvestri
Francesco
F
Tisiot
Francesco
F
Di Camillo
Barbara
B
Pietracaprina
Andrea
A
Toffolo
Gianna Maria
GM
D000465:Algorithms; D002853:Chromatography, Liquid / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional; D013058:Mass Spectrometry / Q000379:methods; D011381:Programming Languages; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors
D020543:Proteome