BRAIN 2.0 : time and memory complexity improvements in the algorithm for calculating the isotope distribution
BRAIN 2.0 : time and memory complexity improvements in the algorithm for calculating the isotope distribution
Faculty of Sciences. Chemistry

article

2014
New York, N.Y.
, 2014

Chemistry

Biology

Journal of the American Society for Mass Spectrometry. - New York, N.Y.

25(2014)
:4
, p. 588-594

1044-0305

000333058100010

E

English (eng)

University of Antwerp

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 ViSte'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.

https://repository.uantwerpen.be/docman/iruaauth/c845ae/7cf9700.pdf

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