title: libfbi: a C++ implementation for fast box intersection and application to sparse mass spectrometry data.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The library is available under an MIT license and can be downloaded from http://software.steenlab.org/libfbi.
  
  
  BACKGROUND
  marc.kirchner@childrens.harvard.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Proteomics Center, Department of Pathology, Children's Hospital Boston, Harvard Medical School, Boston, MA 02115, USA. marc.kirchner@childrens.harvard.edu
authors.forename: Marc
authors.initials: M
authors.lastname: Kirchner
authors.forename: Buote
authors.initials: B
authors.lastname: Xu
authors.forename: Hanno
authors.initials: H
authors.lastname: Steen
authors.forename: Judith A J
authors.initials: JA
authors.lastname: Steen
mesh_terms: D000465:Algorithms; D002853:Chromatography, Liquid; D013058:Mass Spectrometry / Q000379:methods*; D012984:Software