Retrieval and interpretative computer programs for mass spectrometry

McLafferty, Fred W.; Stauffer, Douglas B.

doi:10.1021/ci00047a021

Cited by 68 publications

(33 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These efforts were originally applied to the interpretation of electron impact (EI) ionization mass spectrometry data through the development of algorithms such as STIRS and SISCOM. 28,29 These original algorithms were further refined by including neutral losses, peak intensity weighing, and similarity of mass spectra. 30,31 Since EI fundamentally differs from the ESI MS/MS fragment ion generation, extrapolating these algorithms to ESI MS/MS data was not immediately possible.…”

Section: Resultsmentioning

confidence: 99%

METLIN: A Technology Platform for Identifying Knowns and Unknowns

Guijas¹,

et al. 2018

View full text Add to dashboard Cite

METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLIN’s high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tool’s capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLIN’s MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.

show abstract

Section: Resultsmentioning

confidence: 99%

METLIN: A Technology Platform for Identifying Knowns and Unknowns

Guijas¹,

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Gerard Salton, professor of computer science at Cornell, has been a key figure in developing statistical methods for document retrieval from libraries [36]. These principles were adapted for the Probability-Based Matching (PBM) algorithm [37,38], which has undergone many improvements in the last fifteen years (Figure 4). Salton has shown that reliable evaluations of retrieval performance must be two-dimensional; from about 400 randomly selected "unknowns," such "recall/reliability" plots [39] show that even early versions of PBM were superior to any other available retrieval algorithm.…”

Section: Computer Algorithms For Retrieval and Interpretationmentioning

confidence: 99%

Analytical information from mass spectrometry, past and future

McLafferty

1990

J. Am. Soc. Mass Spectrom.

Self Cite

View full text Add to dashboard Cite

Two basic reasons are proposed for the tremendous success and future promise of mass spectrometry: (1) the unusually high volume of data obtainable from unusually small samples and (2) the success in converting these data into structural and quantitative information. The ion abundance dimension of mass spectrometric data is remarkable in its pico-to-ttogram sensitivity and >10(6) dynamic range, and the mass scale dimension is uniquely high in the number of resolution increments for larger molecule ionization and high resolution. Additional dimensions of data arise from chromatographic coupling to mass spectrometry and tandem mass spectrometry, as well as from alternative ionization and ion reaction methods. Converting these data into chemical information is equally important. Past progress in these areas has been cyclical; for the immediate future a greater research emphasis is urged to convert data to information through better understanding of the relevant chemistry and better utilization of modern computer methods.

show abstract

“…The development of computer systems now offers possibilities to automate the interpretation of mass spectra [2]. Several computer algorithms such as PBM, STIRS [3,4], SISCOM [5][6][7] and INCOS [8] had been developed to interpret mass spectra. Today, mass spectral libraries such as NIST/EPA/NIH [9] help to interpret and to identify unknown mass spectra.…”

Section: Introductionmentioning

confidence: 99%