
@article{benoist_specpeptidoms_2025,
	title = {{SpecPeptidOMS} {Directly} and {Rapidly} {Aligns} {Mass} {Spectra} on {Whole} {Proteomes} and {Identifies} {Peptides} {That} {Are} {Not} {Necessarily} {Tryptic}: {Implications} for {Peptidomics}},
	volume = {24},
	copyright = {https://doi.org/10.15223/policy-029},
	issn = {1535-3893, 1535-3907},
	shorttitle = {\textit{{SpecPeptidOMS}} {Directly} and {Rapidly} {Aligns} {Mass} {Spectra} on {Whole} {Proteomes} and {Identifies} {Peptides} {That} {Are} {Not} {Necessarily} {Tryptic}},
	url = {https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00870},
	doi = {10.1021/acs.jproteome.4c00870},
	language = {en},
	number = {4},
	urldate = {2025-09-12},
	journal = {Journal of Proteome Research},
	author = {Benoist, Émile and Jean, Géraldine and Rogniaux, Hélène and Fertin, Guillaume and Tessier, Dominique},
	month = apr,
	year = {2025},
	pages = {2159--2172},
}

@article{david_specoms_2017,
	title = {{SpecOMS}: {A} {Full} {Open} {Modification} {Search} {Method} {Performing} {All}-to-{All} {Spectra} {Comparisons} within {Minutes}},
	volume = {16},
	issn = {1535-3907},
	shorttitle = {{SpecOMS}},
	doi = {10.1021/acs.jproteome.7b00308},
	abstract = {The analysis of discovery proteomics experiments relies on algorithms that identify peptides from their tandem mass spectra. The almost exhaustive interpretation of these spectra remains an unresolved issue. At present, an important number of missing interpretations is probably due to peptides displaying post-translational modifications and variants that yield spectra that are particularly difficult to interpret. However, the emergence of a new generation of mass spectrometers that provide high fragment ion accuracy has paved the way for more efficient algorithms. We present a new software, SpecOMS, that can handle the computational complexity of pairwise comparisons of spectra in the context of large volumes. SpecOMS can compare a whole set of experimental spectra generated by a discovery proteomics experiment to a whole set of theoretical spectra deduced from a protein database in a few minutes on a standard workstation. SpecOMS can ingeniously exploit those capabilities to improve the peptide identification process, allowing strong competition between all possible peptides for spectrum interpretation. Remarkably, this software resolves the drawbacks (i.e., efficiency problems and decreased sensitivity) that usually accompany open modification searches. We highlight this promising approach using results obtained from the analysis of a public human data set downloaded from the PRIDE (PRoteomics IDEntification) database.},
	language = {eng},
	number = {8},
	journal = {Journal of Proteome Research},
	author = {David, Matthieu and Fertin, Guillaume and Rogniaux, Hélène and Tessier, Dominique},
	month = aug,
	year = {2017},
	pmid = {28660767},
	keywords = {algorithms, Algorithms, bioinformatics, Computational Biology, Databases, Protein, Humans, Methods, MS/MS, open modification search, pattern-matching, peptide identification, Peptides, Protein Processing, Post-Translational, proteomics, Proteomics, PTM, spectra comparison, Tandem Mass Spectrometry, Time Factors},
	pages = {3030--3038},
}

@article{prunier_fast_2023,
	title = {Fast alignment of mass spectra in large proteomics datasets, capturing dissimilarities arising from multiple complex modifications of peptides},
	volume = {24},
	issn = {1471-2105},
	doi = {10.1186/s12859-023-05555-y},
	abstract = {BACKGROUND: In proteomics, the interpretation of mass spectra representing peptides carrying multiple complex modifications remains challenging, as it is difficult to strike a balance between reasonable execution time, a limited number of false positives, and a huge search space allowing any number of modifications without a priori. The scientific community needs new developments in this area to aid in the discovery of novel post-translational modifications that may play important roles in disease.
RESULTS: To make progress on this issue, we implemented SpecGlobX (SpecGlob eXTended to eXperimental spectra), a standalone Java application that quickly determines the best spectral alignments of a (possibly very large) list of Peptide-to-Spectrum Matches (PSMs) provided by any open modification search method, or generated by the user. As input, SpecGlobX reads a file containing spectra in MGF or mzML format and a semicolon-delimited spreadsheet describing the PSMs. SpecGlobX returns the best alignment for each PSM as output, splitting the mass difference between the spectrum and the peptide into one or more shifts while considering the possibility of non-aligned masses (a phenomenon resulting from many situations including neutral losses). SpecGlobX is fast, able to align one million PSMs in about 1.5 min on a standard desktop. Firstly, we remind the foundations of the algorithm and detail how we adapted SpecGlob (the method we previously developed following the same aim, but limited to the interpretation of perfect simulated spectra) to the interpretation of imperfect experimental spectra. Then, we highlight the interest of SpecGlobX as a complementary tool downstream to three open modification search methods on a large simulated spectra dataset. Finally, we ran SpecGlobX on a proteome-wide dataset downloaded from PRIDE to demonstrate that SpecGlobX functions just as well on simulated and experimental spectra. We then carefully analyzed a limited set of interpretations.
CONCLUSIONS: SpecGlobX is helpful as a decision support tool, providing keys to interpret peptides carrying complex modifications still poorly considered by current open modification search software. Better alignment of PSMs enhances confidence in the identification of spectra provided by open modification search methods and should improve the interpretation rate of spectra.},
	language = {eng},
	number = {1},
	journal = {BMC bioinformatics},
	author = {Prunier, Grégoire and Cherkaoui, Mehdi and Lysiak, Albane and Langella, Olivier and Blein-Nicolas, Mélisande and Lollier, Virginie and Benoist, Emile and Jean, Géraldine and Fertin, Guillaume and Rogniaux, Hélène and Tessier, Dominique},
	month = nov,
	year = {2023},
	pmid = {37940845},
	pmcid = {PMC10631047},
	keywords = {Algorithms, Databases, Protein, Dynamic programming, Mass Spectrometry, Open modification search, Peptide identification, Peptides, Post translational modification, Proteomics, Software},
	pages = {421},
	file = {Texte intégral:/home/langella/office/publications/zotero/storage/3GPV8Q7Q/Prunier et al. - 2023 - Fast alignment of mass spectra in large proteomics datasets, capturing dissimilarities arising from.pdf:application/pdf},
}

@article{lysiak_evaluation_2021,
	title = {Evaluation of open search methods based on theoretical mass spectra comparison},
	volume = {22},
	issn = {1471-2105},
	doi = {10.1186/s12859-021-03963-6},
	abstract = {BACKGROUND: Mass spectrometry remains the privileged method to characterize proteins. Nevertheless, most of the spectra generated by an experiment remain unidentified after their analysis, mostly because of the modifications they carry. Open Modification Search (OMS) methods offer a promising answer to this problem. However, assessing the quality of OMS identifications remains a difficult task.
METHODS: Aiming at better understanding the relationship between (1) similarity of pairs of spectra provided by OMS methods and (2) relevance of their corresponding peptide sequences, we used a dataset composed of theoretical spectra only, on which we applied two OMS strategies. We also introduced two appropriately defined measures for evaluating the above mentioned spectra/sequence relevance in this context: one is a color classification representing the level of difficulty to retrieve the proper sequence of the peptide that generated the identified spectrum ; the other, called LIPR, is the proportion of common masses, in a given Peptide Spectrum Match (PSM), that represent dissimilar sequences. These two measures were also considered in conjunction with the False Discovery Rate (FDR).
RESULTS: According to our measures, the strategy that selects the best candidate by taking the mass difference between two spectra into account yields better quality results. Besides, although the FDR remains an interesting indicator in OMS methods (as shown by LIPR), it is questionable: indeed, our color classification shows that a non negligible proportion of relevant spectra/sequence interpretations corresponds to PSMs coming from the decoy database.
CONCLUSIONS: The three above mentioned measures allowed us to clearly determine which of the two studied OMS strategies outperformed the other, both in terms of number of identifications and of accuracy of these identifications. Even though quality evaluation of PSMs in OMS methods remains challenging, the study of theoretical spectra is a favorable framework for going further in this direction.},
	language = {eng},
	number = {Suppl 2},
	journal = {BMC bioinformatics},
	author = {Lysiak, Albane and Fertin, Guillaume and Jean, Géraldine and Tessier, Dominique},
	month = apr,
	year = {2021},
	pmid = {33902435},
	pmcid = {PMC8073971},
	keywords = {Algorithms, Blind search, Databases, Protein, Mass spectrometry, Open Modification Search, Peptide identification, Peptides, Proteomics, Software, Tandem Mass Spectrometry},
	pages = {65},
	file = {Texte intégral:/home/langella/office/publications/zotero/storage/U3DXRABH/Lysiak et al. - 2021 - Evaluation of open search methods based on theoretical mass spectra comparison.pdf:application/pdf},
}
