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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16729

Title: BRAIN 2.0: Time and Memory Complexity Improvements in the Algorithm for Calculating the Isotope Distribution
Authors: Dittwald, Piotr
VALKENBORG, Dirk
Issue Date: 2014
Citation: JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY, 25 (4), p. 588-594
Abstract: Abstract. Recently, an elegant iterative algorithm called BRAIN (Baffling Recursive Algorithm for Isotopic distributioN calculations) was presented. The algorithm is based on the classic polynomial method for calculating aggregated isotope distributions, and it introduces algebraic identities using Newton-Girard and Viète’s formulae to solve the problem of polynomial expansion. Due to the iterative nature of the BRAIN method, it is a requirement that the calculations start from the lightest isotope variant. As such, the complexity of BRAIN scales quadratically with the mass of the putative molecule, since it depends on the number of aggregated peaks that need to be calculated. In this manuscript, we suggest two improvements of the algorithm to decrease both time and memory complexity in obtaining the aggregated isotope distribution. We also illustrate a concept to represent the element isotope distribution in a generic manner. This representation allows for omitting the root calculation of the element polynomial required in the original BRAIN method. A generic formulation for the roots is of special interest for higher order element polynomials such that root finding algorithms and its inaccuracies can be avoided.
Notes: Dittwald, P (reprint author), Univ Warsaw, Coll Interfac Individual Studies Math & Nat Sci, Warsaw, Poland. piotr.dittwald@mimuw.edu.pl
URI: http://hdl.handle.net/1942/16729
DOI: 10.1007/s13361-013-0796-5
ISI #: 000333058100010
ISSN: 1044-0305
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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