Dataset Information

Fragment correlation mass spectrometry: Determining the structures of biopolymers in a complex mixture without isolating individual components.

ABSTRACT: Fragment correlation mass spectrometry correlates ion pairs generated from the same fragmentation pathway, achieved by covariance mapping of tandem mass spectra generated with an unmodified linear ion trap without preseparation. We enable the identification of different precursors at different charge states in a complex mixture from a large isolation window, empowering an analytical approach for data-independent acquisition. The method resolves and matches isobaric fragments, internal ions, and disulfide bond fragments. We suggest that this method represents a major advance for analyzing structures of biopolymers in mixtures.

SUBMITTER: Li Y

PROVIDER: S-EPMC11317569 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Fragment correlation mass spectrometry: Determining the structures of biopolymers in a complex mixture without isolating individual components.

Li Yangjie Y Cavet Guy G Zare Richard N RN Driver Taran T

Proceedings of the National Academy of Sciences of the United States of America 20240729 32

Fragment correlation mass spectrometry correlates ion pairs generated from the same fragmentation pathway, achieved by covariance mapping of tandem mass spectra generated with an unmodified linear ion trap without preseparation. We enable the identification of different precursors at different charge states in a complex mixture from a large isolation window, empowering an analytical approach for data-independent acquisition. The method resolves and matches isobaric fragments, internal ions, and ...[more]

PMID: 39074273

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Project description:BackgroundIdentifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs.ResultsOur results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted "MS/MS score", on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention.ConclusionOur method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon.Scientific contributionDeveloped, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Project description:BackgroundHaplotype information is useful for various genetic analyses, including genome-wide association studies. Determining haplotypes experimentally is difficult and there are several computational approaches that infer haplotypes from genomic data. Among such approaches, single individual haplotyping or haplotype assembly, which infers two haplotypes of an individual from aligned sequence fragments, has been attracting considerable attention. To avoid incorrect results in downstream analyses, it is important not only to assemble haplotypes as long as possible but also to provide means to extract highly reliable haplotype regions. Although there are several efficient algorithms for solving haplotype assembly, there are no efficient method that allow for extracting the regions assembled with high confidence.ResultsWe develop a probabilistic model, called MixSIH, for solving the haplotype assembly problem. The model has two mixture components representing two haplotypes. Based on the optimized model, a quality score is defined, which we call the 'minimum connectivity' (MC) score, for each segment in the haplotype assembly. Because existing accuracy measures for haplotype assembly are designed to compare the efficiency between the algorithms and are not suitable for evaluating the quality of the set of partially assembled haplotype segments, we develop an accuracy measure based on the pairwise consistency and evaluate the accuracy on the simulation and real data. By using the MC scores, our algorithm can extract highly accurate haplotype segments. We also show evidence that an existing experimental dataset contains chimeric read fragments derived from different haplotypes, which significantly degrade the quality of assembled haplotypes.ConclusionsWe develop a novel method for solving the haplotype assembly problem. We also define the quality score which is based on our model and indicates the accuracy of the haplotypes segments. In our evaluation, MixSIH has successfully extracted reliable haplotype segments. The C++ source code of MixSIH is available at https://sites.google.com/site/hmatsu1226/software/mixsih.

Dataset Information

Fragment correlation mass spectrometry: Determining the structures of biopolymers in a complex mixture without isolating individual components.

Publications

Fragment correlation mass spectrometry: Determining the structures of biopolymers in a complex mixture without isolating individual components.

Similar Datasets

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