Title 



BRAIN 2.0 : time and memory complexity improvements in the algorithm for calculating the isotope distribution
 
Author 



 
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 NewtonGirard 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.   
Language 



English
 
Source (journal) 



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



New York, N.Y. : 2014
 
ISSN 



10440305
 
Volume/pages 



25:4(2014), p. 588594
 
ISI 



000333058100010
 
Full text (Publisher's DOI) 


  
Full text (publisher's version  intranet only) 


  
