Evidence map›Paper›PMID 29202576›Full record

ArticleJournal of proteome research2018

Poisson Model To Generate Isotope Distribution for Biomolecules.

Rovshan G Sadygov

Abstract read
In one paragraph

Article in Journal of proteome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Metabolomic and proteomic applications to exercise biomedicine.Translational exercise biomedicine · 2024
    Review
  5. Flexible Quality Control for Protein Turnover Rates Using d2ome.International journal of molecular sciences · 2023
    Article
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  7. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Rovshan G SadygovDepartment of Biochemistry and Molecular Biology, Sealy Center for Molecular Medicine, The University of Texas Medical Branch , Galveston, Texas 77555, United States.
The University of Texas Medical Branch at Galveston · US

Funding

Novel computational methods for in vivo proteome dynamics estimation using heavy water metabolic labeling and LC-MSR01GM112044 · NIGMS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI SADYGOV, ROVSHAN G · 2015 to 2023
$2.7M
NIGMS NIH HHS R01 GM112044
6 · The paper itself

Abstract

We introduce a simplified computational algorithm for computing isotope distributions (relative abundances and masses) of biomolecules. The algorithm is based on Poisson approximation to binomial and multinomial distributions. It leads to a small number of arithmetic operations to compute isotope distributions of molecules. The approach uses three embedded loops to compute the isotope distributions, as compared with the eight embedded loops in exact calculations. The speed improvement is about 3-fold compared to the fast Fourier transformation-based isotope calculations, often termed as ultrafast isotope calculation. The approach naturally incorporates the determination of the masses of each molecular isotopomer. It is applicable to high mass accuracy and resolution mass spectrometry data. The application to tryptic peptides in a UniProt protein database revealed that the mass accuracy of the computed isotopomers is better than 1 ppm. Even better mass accuracy (below 1 ppm) is achievable when the method is paired with the exact calculations, which we term a hybrid approach. The algorithms have been implemented in a freely available C/C++ code.

Indexed as

Databases, ProteinIsotope LabelingPoisson DistributionAlgorithmsMass Spectrometryisotope envelopemass spectrometryPoisson distributionstable isotope labeling

Identifiers

PMID29202576
PMCPMC5789464
OpenAlexW2772122194

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.