paper.bib

@article{duhrkop_sirius_2019,
	title = {{SIRIUS} 4: a rapid tool for turning tandem mass spectra into metabolite structure information},
	volume = {16},
	issn = {1548-7091, 1548-7105},
	url = {https://www.nature.com/articles/s41592-019-0344-8},
	doi = {10.1038/s41592-019-0344-8},
	shorttitle = {{SIRIUS} 4},
	pages = {299--302},
	number = {4},
	journaltitle = {Nature Methods},
	shortjournal = {Nat Methods},
	author = {Dührkop, Kai and Fleischauer, Markus and Ludwig, Marcus and Aksenov, Alexander A. and Melnik, Alexey V. and Meusel, Marvin and Dorrestein, Pieter C. and Rousu, Juho and Böcker, Sebastian},
	urldate = {2026-03-23},
	date = {2019-04},
	langid = {english},
}

@article{duhrkop_searching_2015,
	title = {Searching molecular structure databases with tandem mass spectra using {CSI}:{FingerID}},
	volume = {112},
	issn = {0027-8424, 1091-6490},
	url = {https://pnas.org/doi/full/10.1073/pnas.1509788112},
	doi = {10.1073/pnas.1509788112},
	shorttitle = {Searching molecular structure databases with tandem mass spectra using {CSI}},
	abstract = {Significance
            
              Untargeted metabolomics experiments usually rely on tandem {MS} ({MS}/{MS}) to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Recently, several computational approaches were presented for searching molecular structure databases using {MS}/{MS} data. Here, we present {CSI}:{FingerID}, which combines fragmentation tree computation and machine learning. An in-depth evaluation on two large-scale datasets shows that our method can find 150\% more correct identifications than the second-best search method. In comparison with the two runner-up methods, {CSI}:{FingerID} reaches 5.4-fold more unique identifications. We also present evaluations indicating that the performance of our method will further improve when more training data become available. {CSI}:{FingerID} is publicly available at
              www.csi-fingerid.org
              .
            
          , 
            Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem {MS} to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem {MS} data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as {PubChem}. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.},
	pages = {12580--12585},
	number = {41},
	journaltitle = {Proceedings of the National Academy of Sciences},
	shortjournal = {Proc. Natl. Acad. Sci. U.S.A.},
	author = {Dührkop, Kai and Shen, Huibin and Meusel, Marvin and Rousu, Juho and Böcker, Sebastian},
	urldate = {2026-03-23},
	date = {2015-10-13},
	langid = {english},
	file = {Full Text:C\:\\Users\\plouail\\Zotero\\storage\\IMRPX2U6\\Dührkop et al. - 2015 - Searching molecular structure databases with tandem mass spectra using CSIFingerID.pdf:application/pdf},
}

@article{duhrkop_systematic_2021,
	title = {Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra},
	volume = {39},
	issn = {1087-0156, 1546-1696},
	url = {https://www.nature.com/articles/s41587-020-0740-8},
	doi = {10.1038/s41587-020-0740-8},
	pages = {462--471},
	number = {4},
	journaltitle = {Nature Biotechnology},
	shortjournal = {Nat Biotechnol},
	author = {Dührkop, Kai and Nothias, Louis-Félix and Fleischauer, Markus and Reher, Raphael and Ludwig, Marcus and Hoffmann, Martin A. and Petras, Daniel and Gerwick, William H. and Rousu, Juho and Dorrestein, Pieter C. and Böcker, Sebastian},
	urldate = {2026-03-23},
	date = {2021-04},
	langid = {english},
}

@article{ludwig_database-independent_2020,
	title = {Database-independent molecular formula annotation using Gibbs sampling through {ZODIAC}},
	volume = {2},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-020-00234-6},
	doi = {10.1038/s42256-020-00234-6},
	pages = {629--641},
	number = {10},
	journaltitle = {Nature Machine Intelligence},
	shortjournal = {Nat Mach Intell},
	author = {Ludwig, Marcus and Nothias, Louis-Félix and Dührkop, Kai and Koester, Irina and Fleischauer, Markus and Hoffmann, Martin A. and Petras, Daniel and Vargas, Fernando and Morsy, Mustafa and Aluwihare, Lihini and Dorrestein, Pieter C. and Böcker, Sebastian},
	urldate = {2026-03-23},
	date = {2020-10-13},
	langid = {english},
}

@article{stravs_msnovelist_2022,
	title = {{MSNovelist}: de novo structure generation from mass spectra},
	volume = {19},
	issn = {1548-7091, 1548-7105},
	url = {https://www.nature.com/articles/s41592-022-01486-3},
	doi = {10.1038/s41592-022-01486-3},
	shorttitle = {{MSNovelist}},
	abstract = {Abstract
            
              Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present {MSNovelist}, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry ({MS}
              2
              ) spectra. In an evaluation with 3,863 {MS}
              2
              spectra from the Global Natural Product Social Molecular Networking site, {MSNovelist} predicted 25\% of structures correctly on first rank, retrieved 45\% of structures overall and reproduced 61\% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the {CASMI} 2016 challenge, {MSNovelist} correctly predicted 26\% and retrieved 57\% of structures, recovering 64\% of correct database annotations. Finally, we illustrate the application of {MSNovelist} in a bryophyte {MS}
              2
              dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. {MSNovelist} is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.},
	pages = {865--870},
	number = {7},
	journaltitle = {Nature Methods},
	shortjournal = {Nat Methods},
	author = {Stravs, Michael A. and Dührkop, Kai and Böcker, Sebastian and Zamboni, Nicola},
	urldate = {2026-03-23},
	date = {2022-07},
	langid = {english},
	file = {Full Text:C\:\\Users\\plouail\\Zotero\\storage\\2LETUMX8\\Stravs et al. - 2022 - MSNovelist de novo structure generation from mass spectra.pdf:application/pdf},
}

@article{rainer_modular_2022,
	title = {A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R},
	volume = {12},
	issn = {2218-1989},
	url = {https://www.mdpi.com/2218-1989/12/2/173},
	doi = {10.3390/metabo12020173},
	abstract = {Liquid chromatography-mass spectrometry ({LC}-{MS})-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. {LC}-{MS} instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the {MetaboCoreUtils}, {MetaboAnnotation} and {CompoundDb} packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on {MS}1 properties such as m/z and retention times, followed by an {MS}2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the {CompoundDb} package. The ecosystem supports data from a variety of formats, including, but not limited to, {MSP}, {MGF}, {mzML}, {mzXML}, {netCDF} as well as {MassBank} text files and {SQL} databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted {LC}-{MS}-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on {GitHub} and through Bioconductor.},
	pages = {173},
	number = {2},
	journaltitle = {Metabolites},
	shortjournal = {Metabolites},
	author = {Rainer, Johannes and Vicini, Andrea and Salzer, Liesa and Stanstrup, Jan and Badia, Josep M. and Neumann, Steffen and Stravs, Michael A. and Verri Hernandes, Vinicius and Gatto, Laurent and Gibb, Sebastian and Witting, Michael},
	urldate = {2026-03-23},
	date = {2022-02-11},
	langid = {english},
	file = {Full Text:C\:\\Users\\plouail\\Zotero\\storage\\33TEN8CY\\Rainer et al. - 2022 - A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.pdf:application/pdf},
}

@article{ruttkies_metfrag_2016,
	title = {{MetFrag} relaunched: incorporating strategies beyond in silico fragmentation},
	volume = {8},
	issn = {1758-2946},
	url = {https://jcheminf.biomedcentral.com/articles/10.1186/s13321-016-0115-9},
	doi = {10.1186/s13321-016-0115-9},
	shorttitle = {{MetFrag} relaunched},
	pages = {3},
	number = {1},
	journaltitle = {Journal of Cheminformatics},
	shortjournal = {J Cheminform},
	author = {Ruttkies, Christoph and Schymanski, Emma L. and Wolf, Sebastian and Hollender, Juliane and Neumann, Steffen},
	urldate = {2026-03-23},
	date = {2016-12},
	langid = {english},
	file = {Full Text:C\:\\Users\\plouail\\Zotero\\storage\\WM9PJM2S\\Ruttkies et al. - 2016 - MetFrag relaunched incorporating strategies beyond in silico fragmentation.pdf:application/pdf},
}

@article{wang_sharing_2016,
	title = {Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking},
	volume = {34},
	issn = {1087-0156, 1546-1696},
	url = {https://www.nature.com/articles/nbt.3597},
	doi = {10.1038/nbt.3597},
	pages = {828--837},
	number = {8},
	journaltitle = {Nature Biotechnology},
	shortjournal = {Nat Biotechnol},
	author = {Wang, Mingxun and Carver, Jeremy J and Phelan, Vanessa V and Sanchez, Laura M and Garg, Neha and Peng, Yao and Nguyen, Don Duy and Watrous, Jeramie and Kapono, Clifford A and Luzzatto-Knaan, Tal and Porto, Carla and Bouslimani, Amina and Melnik, Alexey V and Meehan, Michael J and Liu, Wei-Ting and Crüsemann, Max and Boudreau, Paul D and Esquenazi, Eduardo and Sandoval-Calderón, Mario and Kersten, Roland D and Pace, Laura A and Quinn, Robert A and Duncan, Katherine R and Hsu, Cheng-Chih and Floros, Dimitrios J and Gavilan, Ronnie G and Kleigrewe, Karin and Northen, Trent and Dutton, Rachel J and Parrot, Delphine and Carlson, Erin E and Aigle, Bertrand and Michelsen, Charlotte F and Jelsbak, Lars and Sohlenkamp, Christian and Pevzner, Pavel and Edlund, Anna and {McLean}, Jeffrey and Piel, Jörn and Murphy, Brian T and Gerwick, Lena and Liaw, Chih-Chuang and Yang, Yu-Liang and Humpf, Hans-Ulrich and Maansson, Maria and Keyzers, Robert A and Sims, Amy C and Johnson, Andrew R and Sidebottom, Ashley M and Sedio, Brian E and Klitgaard, Andreas and Larson, Charles B and Boya P, Cristopher A and Torres-Mendoza, Daniel and Gonzalez, David J and Silva, Denise B and Marques, Lucas M and Demarque, Daniel P and Pociute, Egle and O'Neill, Ellis C and Briand, Enora and Helfrich, Eric J N and Granatosky, Eve A and Glukhov, Evgenia and Ryffel, Florian and Houson, Hailey and Mohimani, Hosein and Kharbush, Jenan J and Zeng, Yi and Vorholt, Julia A and Kurita, Kenji L and Charusanti, Pep and {McPhail}, Kerry L and Nielsen, Kristian Fog and Vuong, Lisa and Elfeki, Maryam and Traxler, Matthew F and Engene, Niclas and Koyama, Nobuhiro and Vining, Oliver B and Baric, Ralph and Silva, Ricardo R and Mascuch, Samantha J and Tomasi, Sophie and Jenkins, Stefan and Macherla, Venkat and Hoffman, Thomas and Agarwal, Vinayak and Williams, Philip G and Dai, Jingqui and Neupane, Ram and Gurr, Joshua and Rodríguez, Andrés M C and Lamsa, Anne and Zhang, Chen and Dorrestein, Kathleen and Duggan, Brendan M and Almaliti, Jehad and Allard, Pierre-Marie and Phapale, Prasad and Nothias, Louis-Felix and Alexandrov, Theodore and Litaudon, Marc and Wolfender, Jean-Luc and Kyle, Jennifer E and Metz, Thomas O and Peryea, Tyler and Nguyen, Dac-Trung and {VanLeer}, Danielle and Shinn, Paul and Jadhav, Ajit and Müller, Rolf and Waters, Katrina M and Shi, Wenyuan and Liu, Xueting and Zhang, Lixin and Knight, Rob and Jensen, Paul R and Palsson, Bernhard Ø and Pogliano, Kit and Linington, Roger G and Gutiérrez, Marcelino and Lopes, Norberto P and Gerwick, William H and Moore, Bradley S and Dorrestein, Pieter C and Bandeira, Nuno},
	urldate = {2026-03-23},
	date = {2016-08},
	langid = {english},
	file = {Full Text PDF:C\:\\Users\\plouail\\Zotero\\storage\\JL6KJ3FH\\Wang et al. - 2016 - Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecul.pdf:application/pdf},
}

@article{stravs_automatic_2013,
	title = {Automatic recalibration and processing of tandem mass spectra using formula annotation},
	volume = {48},
	rights = {http://onlinelibrary.wiley.com/{termsAndConditions}\#vor},
	issn = {1076-5174, 1096-9888},
	url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/jms.3131},
	doi = {10.1002/jms.3131},
	abstract = {High accuracy, high resolution tandem mass spectrometry ({MS}/{MS}) is becoming more common in analytical applications, yet databases of these spectra remain limited. Databases require good quality spectra with sufficient compound information, but processing, calibration, noise reduction and retrieval of compound information are time‐consuming tasks that prevent many contributions. We present a comprehensive workflow for the automatic processing of {MS}/{MS} using formula annotation for recalibration and cleanup to generate high quality spectra of standard compounds for upload to {MassBank} (
              www.massbank.jp
              ). Compound information is retrieved via Internet services. Reference standards of 70 pesticides were measured at various collision energies on an {LTQ}‐Orbitrap {XL} to develop and evaluate the workflow. A total of 944 resulting spectra are now available on {MassBank}. Evidence of nitrogen adduct formation during {MS}/{MS} fragmentation processes was found, highlighting the benefits high accuracy {MS}/{MS} offers for spectral interpretation. A database of recalibrated, cleaned‐up spectra resulted in the most correct spectra ranked in first place, regardless of whether the search spectra were recalibrated or not, whereas the average rank of the correct molecular formula was improved from 2.55 (uncalibrated) to 1.53 when using recalibrated {MS}/{MS} data. The workflow is available as an R package {RMassBank} capable of generating {MassBank} records from raw {MS} and {MS}/{MS} data and can be adjusted to process data acquired with different settings and instruments. This workflow is a vital step towards addressing the need for more high quality, high accuracy {MS}/{MS} spectra in spectral databases and provides important information for spectral interpretation. Copyright © 2012 John Wiley \& Sons, Ltd.},
	pages = {89--99},
	number = {1},
	journaltitle = {Journal of Mass Spectrometry},
	shortjournal = {J. Mass. Spectrom.},
	author = {Stravs, Michael A. and Schymanski, Emma L. and Singer, Heinz P. and Hollender, Juliane},
	urldate = {2026-03-23},
	date = {2013-01},
	langid = {english},
}

@article{huber_orchestrating_2015,
	title = {Orchestrating high-throughput genomic analysis with Bioconductor},
	volume = {12},
	issn = {1548-7091, 1548-7105},
	url = {https://www.nature.com/articles/nmeth.3252},
	doi = {10.1038/nmeth.3252},
	pages = {115--121},
	number = {2},
	journaltitle = {Nature Methods},
	shortjournal = {Nat Methods},
	author = {Huber, Wolfgang and Carey, Vincent J and Gentleman, Robert and Anders, Simon and Carlson, Marc and Carvalho, Benilton S and Bravo, Hector Corrada and Davis, Sean and Gatto, Laurent and Girke, Thomas and Gottardo, Raphael and Hahne, Florian and Hansen, Kasper D and Irizarry, Rafael A and Lawrence, Michael and Love, Michael I and {MacDonald}, James and Obenchain, Valerie and Oleś, Andrzej K and Pagès, Hervé and Reyes, Alejandro and Shannon, Paul and Smyth, Gordon K and Tenenbaum, Dan and Waldron, Levi and Morgan, Martin},
	urldate = {2026-03-23},
	date = {2015-02},
	langid = {english},
}

@article{louail_xcms_2025,
	title = {\textit{xcms} in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem},
	volume = {97},
	rights = {https://creativecommons.org/licenses/by/4.0/},
	issn = {0003-2700, 1520-6882},
	url = {https://pubs.acs.org/doi/10.1021/acs.analchem.5c04338},
	doi = {10.1021/acs.analchem.5c04338},
	shorttitle = {\textit{xcms} in Peak Form},
	pages = {27639--27645},
	number = {50},
	journaltitle = {Analytical Chemistry},
	shortjournal = {Anal. Chem.},
	author = {Louail, Philippine and Brunius, Carl and Garcia-Aloy, Mar and Kumler, William and Storz, Norman and Stanstrup, Jan and Treutler, Hendrik and Vangeenderhuysen, Pablo and Witting, Michael and Neumann, Steffen and Rainer, Johannes},
	urldate = {2026-03-23},
	date = {2025-12-23},
	langid = {english},
	file = {Full Text PDF:C\:\\Users\\plouail\\Zotero\\storage\\TRIQK429\\Louail et al. - 2025 - xcms in Peak Form Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Soft.pdf:application/pdf},
}

@article{bocker_fragmentation_2016,
	title = {Fragmentation trees reloaded},
	volume = {8},
	issn = {1758-2946},
	url = {https://jcheminf.biomedcentral.com/articles/10.1186/s13321-016-0116-8},
	doi = {10.1186/s13321-016-0116-8},
	pages = {5},
	number = {1},
	journaltitle = {Journal of Cheminformatics},
	shortjournal = {J Cheminform},
	author = {Böcker, Sebastian and Dührkop, Kai},
	urldate = {2026-04-07},
	date = {2016-12},
	langid = {english},
	file = {Full Text:C\:\\Users\\plouail\\Zotero\\storage\\9IJKIZ5D\\Böcker and Dührkop - 2016 - Fragmentation trees reloaded.pdf:application/pdf},
}

@software{philippine_louail_rformassspectrometrymetabonaut_2026,
	title = {rformassspectrometry/Metabonaut: v1.5.0},
	rights = {Creative Commons Attribution 4.0 International},
	url = {https://zenodo.org/doi/10.5281/zenodo.19450619},
	doi = {10.5281/ZENODO.19450619},
	shorttitle = {rformassspectrometry/Metabonaut},
	abstract = {Metabonaut v1.5.0 🧑‍🚀

Welcome to the latest release of Metabonaut! This version expands the annotation toolkit with two new vignettes — one for working with public mass spectral libraries and one for advanced feature annotation using {SIRIUS} — bringing the total to eight comprehensive workflows for untargeted metabolomics analysis.

🚀 What's New in v1.5.0?

🧪 New Vignettes \& Workflow Enhancements



Using and Creating Metabolomics Data Annotation Resources : A new vignette demonstrating how to build and use custom annotation databases in R, including downloading data from {GNPS} and {MassBank} libraries.

Advanced Feature Annotation using {RuSirius} : A pre-computed vignette showcasing the {RuSirius} package for {SIRIUS}-powered prediction, including formula identification, structure database search, and de novo structure generation via {MSNovelist} for low-confidence database matches.


⚙️ Technical Updates \& Stability



Website Reorganization : Vignettes have been restructured into clearer thematic sections (End-to-End Workflow, Data Preprocessing, Annotation, and Other) for easier navigation.


📚 Updated Vignette Overview



Complete End-to-End {LC}-{MS}/{MS} Analysis : From raw data preprocessing to statistical analysis and final metabolite annotation.

Dataset Investigation : Critical first steps to examine your data and prevent downstream troubleshooting.

{QC} \& Feature Selection (using notame) : Robust normalization, quality control, and feature selection for clean, analysis-ready data.

Large Scale Processing (using xcms) : Practical guide to handling {\textgreater}4,000 files on standard hardware.

Using and Creating Annotation Resources ⭐ New : Build and integrate custom annotation databases from {GNPS} and {MassBank}.

{LC}-{MS}/{MS} Data Annotation (R \& Python) : Leveraging the {SpectriPy} package to combine Python and R {MS} libraries for comprehensive annotation.

Advanced Feature Annotation using {RuSirius} ⭐ New : {SIRIUS}-powered annotation including de novo structure generation.

Seamless Alignment : Integrating new datasets with existing preprocessed data using a flexible alignment algorithm.


🤝 Community \& Contributions

A huge thank you to all contributors for this release!

Full Changelog: [v1.4.0...v1.5.0](https://github.com/rformassspectrometry/Metabonaut/compare/v1.4.0...v1.5.0)

Acknowledgment

Funded by the European Union under the {HORIZON}-{MSCA}-2021 project 101073062: {HUMAN} — Harmonising and Unifying Blood Metabolic Analysis Networks.},
	version = {v1.5.0},
	publisher = {Zenodo},
	author = {Philippine Louail and Suksi, Vilhelm and Nishida, Kozo and Marilyn De Graeve and Rainer, Johannes},
	urldate = {2026-04-07},
	date = {2026-04-07},
}

@software{philippine_louail_philouailhuman_ring_trial_2026,
	title = {philouail/{HUMAN}\_Ring\_Trial: pre-release ringtrial {HUMAN} {DN}},
	rights = {Creative Commons Attribution 4.0 International},
	url = {https://zenodo.org/doi/10.5281/zenodo.19454163},
	doi = {10.5281/ZENODO.19454163},
	shorttitle = {philouail/{HUMAN}\_Ring\_Trial},
	abstract = {{HUMAN} Ring Trial — v1.0.0

Part of the {HUMAN} Doctoral Network · Labs: Afekta, Cembio, {HMGU}, {ICL}

This is the first public release of the {HUMAN} Ring Trial analysis pipeline and supporting data, created to obtain a citable {DOI} for use in associated publications.

What is included

This release captures the complete, peer-reviewed analysis pipeline used to assess inter-laboratory variability in {LC}-{MS}-based metabolomics across four participating laboratories, covering all five workflow stages:



Preprocessing — {XCMS} peak picking and {NAPS} alignment scripts, per-lab and per-mixture

Automatic annotation — {MS}1 adduct/isotope matching and {MS}2 {GNPS} spectral matching

Manual curation — lab reports, consensus merging, and refinement loop outputs

Library generation — final .csv library tables and .mgf {MS}/{MS} spectral files for the {HE} (Human Extract) mixture set

Downstream analysis — comparative metrics including {TIC}/{BPC} similarity, peak feature analysis, and {PCA} across labs (work in progress; partially included)


Study design



83 mixtures from the {MetaSci} metabolite standard library (ground truth known)

Each lab contributed both a lab-specific method and a {HUMAN} reference method (common column and gradient)

Participating institutions: Afekta, Cembio, {HMGU}, {ICL}


Key outputs



4\_library\_generation/ring\_trial\_library\_HE.csv — final annotated metabolite library

4\_library\_generation/std\_spectra\_HE.mgf — curated {MS}/{MS} spectra

3\_annotation\_manual/2\_combine\_annotation/consensus\_summary.xlsx — final consensus annotation table


How to cite

If you use this repository or its outputs in your work, please cite it using the {DOI} assigned to this release.

Notes



Intermediate files (e.g. peak\_evidence.csv in 2\_annotation\_auto/) are not final results — refer to 4\_library\_generation/ for citable outputs

The downstream analysis in 5\_downstream\_analysis/ is a work in progress and will be updated in a future release

To reproduce the analysis, follow the numbered folders in order and refer to the {README} for per-lab instructions},
	version = {v0.99.0},
	publisher = {Zenodo},
	author = {Philippine Louail and Johannes Rainer},
	urldate = {2026-04-07},
	date = {2026-04-07},
}

@misc{rainer_open_2026,
	title = {An Open Infrastructure for Mass Spectrometry Data in R},
	rights = {https://creativecommons.org/licenses/by/4.0/legalcode},
	url = {https://osf.io/cwt2v_v3},
	doi = {10.31219/osf.io/cwt2v_v3},
	abstract = {We present the Spectra software, an R/Bioconductor package defining an efficient infrastructure for storing, handling and visualising mass spectrometry data. We describe how Spectra adapts to different data types, sizes, and use cases,addressing scalability while keeping a consistent user interface. We also demonstrate how it can integrate with third party Python tools and follows modern tidyomics principles,seamlessly integrating with more general tidy data science tools.},
	publisher = {Open Science Framework},
	author = {Rainer, Johannes and De Graeve, Marilyn and Louail, Philippine and Gibb, Sebastian and Gatto, Laurent},
	urldate = {2026-04-07},
	date = {2026-02-10},
}

@article{graeve_spectripy_2025,
	title = {{SpectriPy}: Enhancing Cross-Language Mass Spectrometry Data Analysis with R and Python},
	volume = {10},
	rights = {http://creativecommons.org/licenses/by/4.0/},
	issn = {2475-9066},
	url = {https://joss.theoj.org/papers/10.21105/joss.08070},
	doi = {10.21105/joss.08070},
	shorttitle = {{SpectriPy}},
	pages = {8070},
	number = {109},
	journaltitle = {Journal of Open Source Software},
	shortjournal = {{JOSS}},
	author = {Graeve, Marilyn De and Bittremieux, Wout and Naake, Thomas and Huber, Carolin and Anagho-Mattanovich, Matthias and Hoffmann, Nils and Marchal, Pierre and Chrone, Victor and Louail, Philippine and Hecht, Helge and Witting, Michael and Rainer, Johannes},
	urldate = {2026-04-07},
	date = {2025-05-19},
	file = {Full Text:C\:\\Users\\plouail\\Zotero\\storage\\4W8J7F4Y\\Graeve et al. - 2025 - SpectriPy Enhancing Cross-Language Mass Spectrometry Data Analysis with R and Python.pdf:application/pdf},
}