libfbi: a C++ implementation for fast box intersection and application to sparse mass spectrometry data.
| Autor: | Kirchner M; Proteomics Center, Department of Pathology, Children's Hospital Boston, Harvard Medical School, Boston, MA 02115, USA. marc.kirchner@childrens.harvard.edu, Xu B, Steen H, Steen JA |
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| Jazyk: | angličtina |
| Zdroj: | Bioinformatics (Oxford, England) [Bioinformatics] 2011 Apr 15; Vol. 27 (8), pp. 1166-7. Date of Electronic Publication: 2011 Feb 16. |
| DOI: | 10.1093/bioinformatics/btr084 |
| Abstrakt: | Motivation: Algorithms for sparse data require fast search and subset selection capabilities for the determination of point neighborhoods. A natural data representation for such cases are space partitioning data structures. However, the associated range queries assume noise-free observations and cannot take into account observation-specific uncertainty estimates that are present in e.g. modern mass spectrometry data. In order to accommodate the inhomogeneous noise characteristics of sparse real-world datasets, point queries need to be reformulated in terms of box intersection queries, where box sizes correspond to uncertainty regions for each observation. Results: This contribution introduces libfbi, a standard C++, header-only template implementation for fast box intersection in an arbitrary number of dimensions, with arbitrary data types in each dimension. The implementation is applied to a data aggregation task on state-of-the-art liquid chromatography/mass spectrometry data, where it shows excellent run time properties. Availability: The library is available under an MIT license and can be downloaded from http://software.steenlab.org/libfbi. Contact: marc.kirchner@childrens.harvard.edu Supplementary Information: Supplementary data are available at Bioinformatics online. |
| Databáze: | MEDLINE |
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