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See detailComputational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)
Böcker, Sebastian; Broeckling, Corey; Schymanski, Emma UL et al

in Dagstuhl Reports (2020)

Dagstuhl Seminar 20051 on Computational Metabolomics is the third edition of seminars onthis topic and focused on Cheminformatics and Machine Learning. With the advent of higherprecision instrumentation ... [more ▼]

Dagstuhl Seminar 20051 on Computational Metabolomics is the third edition of seminars onthis topic and focused on Cheminformatics and Machine Learning. With the advent of higherprecision instrumentation, application of metabolomics to a wider variety of small molecules, andever increasing amounts of raw and processed data available, developments in cheminformaticsand machine learning are sorely needed to facilitate interoperability and leverage further insightsfrom these data. Following on from Seminars 17491 and 15492, this edition convened bothexperimental and computational experts, many of whom had attended the previous sessions andbrought much-valued perspective to the week’s proceedings and discussions. Throughout theweek, participants first debated on what topics to discuss in detail, before dispersing into smaller,focused working groups for more in-depth discussions. This dynamic format was found to bemost productive and ensured active engagement amongst the participants. The abstracts inthis report reflect these working group discussions, in addition to summarising several informalevening sessions. Action points to follow-up on after the seminar were also discussed, includingfuture workshops and possibly another Dagstuhl seminar in late 2021 or 2022. [less ▲]

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See detailExpanding the use of spectral libraries in proteomics.
Deutsch, Eric W.; Perez-Riverol, Yasset; Chalkley, Robert J. et al

in Journal of proteome research (2018)

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass ... [more ▼]

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none capture a satisfactory level of metadata; therefore a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly-seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets. [less ▲]

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