Albane Lysiak1,2, Guillaume Fertin3, Géraldine Jean1, Dominique Tessier4,2. 1. CNRS, LS2N, Université de Nantes, 44000, Nantes, France. 2. UR BIA, INRAE, 44316, Nantes, France. 3. CNRS, LS2N, Université de Nantes, 44000, Nantes, France. guillaume.fertin@ls2n.fr. 4. BIBS Facility, INRAE, 44316, Nantes, France.
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.
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.
Entities:
Keywords:
Blind search; Mass spectrometry; Open Modification Search; Peptide identification
Authors: Andrew D Yates; Premanand Achuthan; Wasiu Akanni; James Allen; Jamie Allen; Jorge Alvarez-Jarreta; M Ridwan Amode; Irina M Armean; Andrey G Azov; Ruth Bennett; Jyothish Bhai; Konstantinos Billis; Sanjay Boddu; José Carlos Marugán; Carla Cummins; Claire Davidson; Kamalkumar Dodiya; Reham Fatima; Astrid Gall; Carlos Garcia Giron; Laurent Gil; Tiago Grego; Leanne Haggerty; Erin Haskell; Thibaut Hourlier; Osagie G Izuogu; Sophie H Janacek; Thomas Juettemann; Mike Kay; Ilias Lavidas; Tuan Le; Diana Lemos; Jose Gonzalez Martinez; Thomas Maurel; Mark McDowall; Aoife McMahon; Shamika Mohanan; Benjamin Moore; Michael Nuhn; Denye N Oheh; Anne Parker; Andrew Parton; Mateus Patricio; Manoj Pandian Sakthivel; Ahamed Imran Abdul Salam; Bianca M Schmitt; Helen Schuilenburg; Dan Sheppard; Mira Sycheva; Marek Szuba; Kieron Taylor; Anja Thormann; Glen Threadgold; Alessandro Vullo; Brandon Walts; Andrea Winterbottom; Amonida Zadissa; Marc Chakiachvili; Bethany Flint; Adam Frankish; Sarah E Hunt; Garth IIsley; Myrto Kostadima; Nick Langridge; Jane E Loveland; Fergal J Martin; Joannella Morales; Jonathan M Mudge; Matthieu Muffato; Emily Perry; Magali Ruffier; Stephen J Trevanion; Fiona Cunningham; Kevin L Howe; Daniel R Zerbino; Paul Flicek Journal: Nucleic Acids Res Date: 2020-01-08 Impact factor: 16.971