useBRAIN2 {BRAIN} | R Documentation

## Function computing probabilities of aggregated isotopic variants using heuristics.

### Description

Function computing probabilities of isotopic variants using heuristics, for chemical components composed of carbon, hydrogen, oxygen, nitrogen and sulfur (e.g. peptides). Additionally the function returns also the monoisotopic mass and the average mass of given chemical component.

### Usage

    useBRAIN2(aC, stopOption = "nrPeaks", nrPeaks, approxStart = 1, approxParam = NULL))

### Arguments

`aC` | List with fields C, H, N, O, S of integer non-negative values (if any field is ommited, then its value is set to 0).
`stopOption` | only option "nrPeaks" allowed
`nrPeaks` | Integer indicating the number of consecutive isotopic variants to be calculated, starting from the monoisotopic one. This value can always be provided, even if <stop.option> is not a default setting. In the latter case it is a hard stopping criterion.
`approxStart` | Integer indicating the number of first isotopic peak to be calculated
`approxParam` | Integer indicating the length of recurrence (see RCL in [Ditt2])

### Details

Function uses RCL and LSP heuristics from [Ditt2].

### Value

Named list with the following fields:

- isoDistrProbabilities of aggregated isotopic variants (numeric vector)

### Note

Remember that the isotopic variants starts from monoisotopic one. For large chemical molecules, first masses may have very low abundances. So sufficient number of peaks should be calculated to reach most abundant isotopic variant.

If only isotopic probabilities are needed, then the function calculateIsotopicProbabilities should be used.

### Author(s)

Piotr Dittwald <piotr.dittwald@mimuw.edu.pl>

### References

[Ditt2] Dittwald P., Valkenborg D. BRAIN 2.0: time and memory complexity improvements in the algorithm for calculating the isotope distribution. JASMS, 2014, doi: 10.1007/s13361-013-0796-5.

### See Also

`calculateIsotopicProbabilities`

### Examples

      nrPeaks = 1000
      aC <-  list(C=23832, H=37816, N=6528, O=7031, S=170)  # Human dynein heavy chain
      res <-  useBRAIN(aC = aC, stopOption="nrPeaks", nrPeaks = nrPeaks)
      res2 <-  useBRAIN2(aC = aC, stopOption="nrPeaks", nrPeaks = nrPeaks, approxStart = 10)
      old = res$iso[10:109]/res$iso[11:110]
      new  = res2$iso[1:100]/res2$iso[2:101]
      max(old - new)
      max((old - new)/old)
      res3 <-  useBRAIN2(aC = aC, stopOption="nrPeaks", nrPeaks = nrPeaks, approx=TRUE, approxParam = 10)
      max(res3$iso - res$iso)

[Package BRAIN version 1.38.0 Index]