Comment on “Computation of Isotopic Peak Center-Mass Distribution by Fourier Transform”

Hu, Han; Dittwald, Piotr; Zaia, Joseph; Valkenborg, Dirk

doi:10.1021/ac402731h

Cited by 4 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sorted by decreasing abundance, i = 1 denotes the monoisotopic variant and often coincides with the lightest isotope of an element in organic compounds. In the given example, k = 3 and i = 1 refers to 16 O. Similarly, n 3,1 = 10 is the monoisotopic subisotopologue of oxygen.…”

Section: ■ Methodsmentioning

confidence: 99%

“…9 Furthermore, both ecipex and enviPat are implemented in the R statistical environment, 15 making a comparison more consistent. 16 For time performance, each approach was averaged over 200 repetitions for each formula, including an initial garbage collection step, a timeout of 2 s for a single isotope pattern calculation, and an upper limit of 2 × 10 7 subisotopologues. Because ecipex does not allow for relative thresholds, an absolute threshold from the above parameter evaluation was selected for both approaches to calculate equal numbers of isotopologues for each formula.…”

Section: ■ Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Accelerated Isotope Fine Structure Calculation Using Pruned Transition Trees

et al. 2015

View full text Add to dashboard Cite

A fast and memory-efficient calculation of theoretical isotope patterns is crucial for the routine interpretation of mass spectrometric data. For high-resolution experiments, calculations must procure the exact masses and probabilities of relevant isotopologues over a wide range of polyisotopic compounds, while pruning low-probable ones. Here, a novel albeit simple treelike structure is introduced to swiftly derive sets of relevant subisotopologues for each element in a molecule, which are then combined to the isotopologues of the full molecule. In contrast to existing approaches, transitions via single replacements of the most abundant isotope per element are used in separable tree branches to derive subisotopologues from each other. Moreover, the underlying transition trees prevent redundant replacements and permit the detection of the most probable isotopologue in a first phase. A relative threshold can then be exploited in a second parallelized phase for a precise prepruning of large fractions of the remaining subisotopologues. The gain in performance from such early pruning and the lower variation in the distortion of simulated data with use of relative rather than absolute thresholds were validated in a large-scale benchmark simulation, unprecedentedly comprising several thousand molecular formulas. Both the algorithm and a wealth of related features are freely available as R-package enviPat and as a user-friendly Web interface.

show abstract

Section: ■ Methodsmentioning

confidence: 99%

Section: ■ Methodsmentioning

confidence: 99%

Accelerated Isotope Fine Structure Calculation Using Pruned Transition Trees

et al. 2015

View full text Add to dashboard Cite

show abstract

“…GAGdecon uses a database search algorithm for structural assignment at the MS level. It maps the isotopic distribution of theoretical species using a python implementation of the BRAIN (Baffling Recursive Algorithm for Isotopic distribution calculations) 13,14 available at (https://github.com/BostonUniversityCBMS/brainpy) to the experimental data.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

Complete Molecular Weight Profiling of Low-Molecular Weight Heparins Using Size Exclusion Chromatography-Ion Suppressor-High-Resolution Mass Spectrometry

et al. 2016

Self Cite

View full text Add to dashboard Cite

Low-molecular weight heparins (LMWH) prepared by partial depolymerization of unfractionated heparin are used globally to treat coagulation disorders on an outpatient basis. Patent protection for several LMWH has expired and abbreviated new drug applications have been approved by the Food and Drug Administration. As a result, reverse engineering of LMWH for biosimilar LMWH has become an active global endeavor. Traditionally, the molecular weight distributions of LMWH preparations have been determined using size exclusion chromatography (SEC) with optical detection. Recent advances in liquid chromatography–mass spectrometry methods have enabled exact mass measurements of heparin saccharides roughly up to degree-of-polymerization 20, leaving the high molecular weight half of the LMWH preparation unassigned. We demonstrate a new LC–MS system capable of determining the exact masses of complete LMWH preparations, up to dp30. This system employed an ion suppressor cell to desalt the chromatographic effluent online prior to the electrospray mass spectrometry source. We expect this new capability will impact the ability to define LMWH mixtures favorably.

show abstract

“…For the quality check on the isotopic pattern, the theoretical isotopic distribution is computed for each peptide . For each isotope peak, the mean absolute deviation of retention time (M.A.D.…”

mentioning

confidence: 99%

“…For the quality check on the isotopic pattern, the theoretical isotopic distribution is computed for each peptide. 10 For each isotope peak, the mean absolute deviation of retention time (M.A.D. RT) for all isotope peaks is computed, along with the Spearman rank correlation between the empirical intensities and the theoretical intensities.…”

mentioning

confidence: 99%

Update on the moFF Algorithm for Label-Free Quantitative Proteomics

et al. 2018

View full text Add to dashboard Cite

moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine’s output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from

show abstract

Comment on “Computation of Isotopic Peak Center-Mass Distribution by Fourier Transform”

Cited by 4 publications

References 24 publications

Accelerated Isotope Fine Structure Calculation Using Pruned Transition Trees

Accelerated Isotope Fine Structure Calculation Using Pruned Transition Trees

Complete Molecular Weight Profiling of Low-Molecular Weight Heparins Using Size Exclusion Chromatography-Ion Suppressor-High-Resolution Mass Spectrometry

Update on the moFF Algorithm for Label-Free Quantitative Proteomics

Contact Info

Product

Resources

About