Open Access to Proteomics Data: A Valuable Resource for Biology and Medicine
Notice bibliographique
Résumé
C hanging technologies are important drivers of proteomics.Continued improve- ments in mass accuracy, resolution, fragmentation methods, and throughput all have significant impacts on experimental design and outcome.New algorithms and computational approaches also are changing the landscape with improved database search engines and tools for data reduction.This situation raises various issues for the development of analytical software for the valid identification and quantification of proteins and their modifications.For these and several other reasons, it is in the interest of the field that proteomics data sets be made publicly accessible upon publication of manuscripts.Although most proteomics studies are targeted, at least in the sense that they are focused on the identification of changes in protein levels or the protein complement of a cell or tissue, these studies harbor additional information beyond the scope of the original experiment.For example, the data could reveal a novel tissue localization or modification of a particular protein, or a new splice variant.From this perspective, public access to proteomics data sets enables their broader use in other studies and allows them to be a resource for genome annotation.Considerable additional value exists in the analysis of data across projects, species, and tissues, and even for the same sample across laboratories and analytical platforms.Under the best of circumstances, current major search engines will only find modifications specified in the search parameters and proteins that are included in the database.In other words, they cannot find what they are not told to look for.This leaves considerable room for better interpretation of data sets as search engines, databases, and related data-analysis tools improve.This situation is inherent to current proteomics studies in that few labs have access to all data-processing software, and differences in algorithms can result in significant variations in the lists of proteins identified and in the modifications observed.A key point is that search results are implicitly reproducible when identical databases, processing parameters, and software versions are used.Acquisition of data, even from the same sample, is variable at some level because of duty cycle, chemical background, and variations in instrument performance.This fact underscores access to data files as crucial for reproducing results.In addition, the generation of peak lists represents very efficient data compression, but key information can be lost.The final results of a proteomics experiment are dependent not only on the search tools but also on the data-reduction software and its parameters; this suggests that raw data sets may be of particular value in certain cases.Some public resources and commercial tools rely on access to the raw spectra or peak lists.For example, peptideatlas.orgreprocesses all data through the Trans-Proteomic Pipeline.The Global Proteome Machine (www. thegpm.org), the Computational Portal and Analysis System (CPAS; https://proteomics.fhcrc.org/CPAS),and the commercial Scaffold software (www.proteomesoftware.com)reanalyze data sets using X!Tandem and/or other search engines.These resources also provide a range of tools for examining the data.Whereas two of the open-source data analysis and management systems (CPAS and Proteome Research Information Management Environment [known as PRIME]; https://www.prime-sdms.org/prime/index.htm)can provide public access to data, the use of data management systems for high-traffic downloads can be inefficient and can interfere with function.Centralized databases (www.ebi.ac.uk/pride, www.hprd.org,www.thegpm.org,www.peptideatlas.org, and others) are valuable resources, and they typically support a subset of annotation data and peak lists but not raw data.A need still exists in proteomics for a trusted dissemination system.The opensource Tranche project (tranche.proteomecommons.org)represents one approach
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,122 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,002 |
| Bibliométrie | 0,020 | 0,025 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,026 | 0,027 |
| Science ouverte | 0,008 | 0,028 |
| Intégrité de la recherche | 0,009 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,090 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».