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1. Propagating annotations of molecular networks using in silico fragmentation

   https://doi.org/https://doi.org/10.1371/journal.pcbi.1006089  

   da Silva, Ricardo R.; Wang, Mingxun; Nothias, Louis-Félix; van der Hooft, Justin J.; Caraballo-Rodríguez, Andrés Mauricio; Fox, Evan; Balunas, Marcy J.; Klassen, Jonathan L.; Lopes, Norberto Peporine; Dorrestein, Pieter C. (April 2018, PLOS computational biology)

   The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp. 

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2. TIMSCONVERT: a workflow to convert trapped ion mobility data to open data formats

   https://doi.org/10.1093/bioinformatics/btac419  

   Luu, Gordon T.; Freitas, Michael A.; Lizama-Chamu, Itzel; McCaughey, Catherine S.; Sanchez, Laura M.; Wang, Mingxun; Vitek, ed., Olga (June 2022, Bioinformatics)

   Abstract MotivationAdvances in mass spectrometry have led to the development of mass spectrometers with ion mobility spectrometry capabilities and dual-source instrumentation; however, the current software ecosystem lacks interoperability with downstream data analysis using open-source software and pipelines. ResultsHere, we present TIMSCONVERT, a data conversion high-throughput workflow from timsTOF Pro/fleX mass spectrometer raw data files to mzML and imzML formats that incorporates ion mobility data while maintaining compatibility with data analysis tools. We showcase several examples using data acquired across different experiments and acquisition modalities on the timsTOF fleX MS. Availability and implementationTIMSCONVERT and its documentation can be found at https://github.com/gtluu/timsconvert and is available as a standalone command-line interface tool for Windows and Linux, NextFlow workflow and online in the Global Natural Products Social (GNPS) platform. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. SpecEncoder: deep metric learning for accurate peptide identification in proteomics

   https://doi.org/10.1093/bioinformatics/btae220  

   Liu, Kaiyuan; Tao, Chenghua; Ye, Yuzhen; Tang, Haixu (June 2024, Bioinformatics)

   Abstract MotivationTandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification. ResultsWe evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%–2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%–15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%–12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder’s potential to enhance peptide identification for proteomic data analyses. Availability and ImplementationThe source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu. 

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4. Accelerating open modification spectral library searching on tensor core in high-dimensional space

   https://doi.org/10.1093/bioinformatics/btad404  

   Kang, Jaeyoung; Xu, Weihong; Bittremieux, Wout; Moshiri, Niema; Rosing, Tajana; Kelso, ed., Janet (June 2023, Bioinformatics)

   Abstract MotivationDriven by technological advances, the throughput and cost of mass spectrometry (MS) proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications (PTMs), will remain unknown. Open modification searching (OMS) is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of MS proteomics datasets. ResultsWe propose an OMS algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering PTMs. We accelerate HOMS-TC on NVIDIA’s tensor core units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31× faster on average than alternative search engines and provides comparable accuracy to competing search tools. Availability and implementationHOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc. 

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5. Analysis of oil-based ignitable liquid residues by GC–MS and DART–MS

   https://doi.org/10.1016/j.forc.2025.100701  

   Dong, Wen; Chong, Ngee Sing; Thanni, Qudus Ayodeji; de_Boves_Harrington, Peter; Zhang, Mengliang (September 2025, Forensic Chemistry)

   Fatty acid-based ignitable liquids (ILs), such as biodiesels and bio-based lighter fluids, represent a growing class of accelerants with limited forensic characterization. In this study, we applied gas chromatography–mass spectrometry (GC–MS) and direct analysis in real time mass spectrometry (DART–MS) to analyze plant oil-derived IL residues on wood and fabric substrates. ILs were prepared from ten different plant oils, subjected to burning, and extracted from fire debris using the ASTM E1412 activated charcoal method. GC–MS analysis resolved characteristic fatty acid methyl esters (FAMEs) and identified diagnostic fragment ions (m/z 55, 67, 74, 79). The fragmentation patterns of unsaturated and saturated FAMEs were systematically examined and compared against experimental data and reference spectra from online databases, demonstrating strong agreement and validating the reliability of these ion ratios as qualitative indicators of FAME saturation. DART–MS enabled rapid confirmation of major unsaturated FAMEs through the detection of protonated molecular ions, offering complementary identification without chromatographic separation. Chemometric analysis using principal component analysis (PCA) and analysis of variance-PCA revealed that FAME profiles were strongly dependent on the IL sources and remained reliable across replicate preparations and synthesis conditions, while substrate and combustion effects were mitigated using targeted ion extraction. These findings demonstrate the practical casework relevance of combining GC–MS and DART–MS for the detection and classification of fatty acid–based ILs in fire debris, providing robust chemical evidence to support arson investigations and to guide the inclusion of these emerging accelerants in forensic ignitable-liquid classification schemes. 

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