Mass Spectral Classifiers for Supporting Systematic Structure Elucidation

and, K. Varmuza; Werther, W.

doi:10.1021/ci9501406

Cited by 89 publications

(73 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless the LDF analysis is a robust multivariate technique that has been proven to be a valuable chemometric tool in similar applications, e.g. the elucidation of chemical structure of organic compounds from mass spectrometry data (36). The main advantage of the LDF approach and the rationale for its use in this work over more sophisticated nonlinear methods was the ease of its implementation as a part of the dynamic quality scoring algorithm.…”

Section: Spectrum Quality Scorementioning

confidence: 99%

Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data

Nesvizhskii

Roos

Grossmann

et al. 2006

Molecular & Cellular Proteomics

179

View full text Add to dashboard Cite

In mass spectrometry-based proteomics, frequently hundreds of thousands of MS/MS spectra are collected in a single experiment. Of these, a relatively small fraction is confidently assigned to peptide sequences, whereas the majority of the spectra are not further analyzed. Spectra are not assigned to peptides for diverse reasons. These include deficiencies of the scoring schemes implemented in the database search tools, sequence variations (e.g. single nucleotide polymorphisms) or omissions in the database searched, post-translational or chemical modifications of the peptide analyzed, or the observation of sequences that are not anticipated from the genomic sequence (e.g. splice forms, somatic rearrangement, and processed proteins). To increase the amount of information that can be extracted from proteomic MS/MS datasets we developed a robust method that detects high quality spectra within the fraction of spectra unassigned by conventional sequence database searching and computes a quality score for each spectrum. We also demonstrate that iterative search strategies applied to such detected unassigned high quality spectra significantly increase the number of spectra that can be assigned from datasets and that biologically interesting new insights can be gained from existing data. Molecular & Cellular Proteomics 5:652-670, 2006.Proteomics, the systematic identification and characterization of all proteins expressed in a cell, has become a key analytical approach in the life sciences (1). The dramatic progress of proteomic research over the last decade has been catalyzed by several, seemingly independent developments. First, the wealth of genomic sequence information generated by large scale sequencing projects and the development of computational gene prediction and annotation tools have produced sequence databases that are expected to contain most coding gene regions. These databases can be searched with proteomic data and constrain the proteomic search space (2). Second, technological improvements in mass spectrometry and peptide and protein separation techniques allow rapid and sensitive protein identification from minute amounts of complex biological samples (for reviews, see Refs. 1, 3, and 4). Third, the development of computational tools for the assignment of MS/MS spectra to peptide sequences and the statistical validation of these assignments support the consistent analysis of large datasets with no or minimal human intervention (5). Collectively these developments resulted in the emergence of shotgun proteomics, a strategy based on the combination of tandem mass spectrometry-based peptide sequencing and sequence database searching, which now routinely permits the identification of hundreds to thousands of proteins in a single experiment.Shotgun proteomics creates significant computational challenges (5-8). Large numbers (on the order of 10 5 ) of MS/MS spectra acquired in each experiment need to be computationally processed to identify peptides that produced them and to infer what proteins were present i...

show abstract

Section: Spectrum Quality Scorementioning

confidence: 99%

Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data

Nesvizhskii

Roos

Grossmann

et al. 2006

Molecular & Cellular Proteomics

179

View full text Add to dashboard Cite

show abstract

“…10 The theory of GA-PLS, concerning applications to chemistry and particularly to feature selection has been extensively described, and will not be repeated here. 9 GA usually assumes that there is no autocorrelation among the features. While this is true in the case of non-spectral data sets, it does not hold in the case of spectral data.…”

Section: Ga-plsmentioning

confidence: 99%

Predicting cis/trans Structure of Alkene Based on Infrared Spectra

et al. 2007

View full text Add to dashboard Cite

“…Further, using the tools developed by Varmuza [27,28], MOLGEN-MS is able to identify from the mass spectroscopic peak patterns substructures that are either present or absent.…”

Section: Structure Elucidationmentioning

confidence: 99%

Molecules in Silico: The Generation of Structural Formulae and Its Applications

Kerber

Laue

Meringer

et al. 2004

J. Comput. Chem. Jpn.

View full text Add to dashboard Cite

In information processing, in combinatorial chemistry, in structure elucidation, and in several other fields of chemistry, the computer-aided generation of all structures (constitutional formulae) within a defined structure space has become increasingly important. In this brief review the mathematical foundations of the classical molecular model and thus of the generation process are outlined, and the current state of structure generation as applied in software developed by the Bayreuth group is discussed.

show abstract

Mass Spectral Classifiers for Supporting Systematic Structure Elucidation

Cited by 89 publications

References 34 publications

Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data

Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data

Predicting cis/trans Structure of Alkene Based on Infrared Spectra

Molecules in Silico: The Generation of Structural Formulae and Its Applications

Contact Info

Product

Resources

About