New Guidelines for Publication of Manuscripts Describing Development and Application of Targeted Mass Spectrometry Measurements of Peptides and Proteins
Bibliographic record
Abstract
Molecular & Cellular Proteomics is pleased to announce new guidelines and requirements for papers describing the development and application of targeted mass spectrometry measurements of peptides, modified peptides and proteins (PDF). These guidelines will be implemented for papers submitted starting June, 2017. Over the past several years, representatives from academia, clinical laboratories, and pharma, who are active developers and users of targeted mass spectrometry methods and analysis tools for quantification of peptides and proteins in complex biological or clinical samples, have worked together to develop a set of guidelines and requirements for authors who want to publish targeted proteomics papers. Our goals were to define what information authors must provide regarding how such analyses were performed and the resulting data analyzed to ensure that reviewers and readers have the ability to independently establish that the measurements being reported using targeted MS methods are reliable—i.e. that they specifically identify and quantify the analytes targeted in a sample—and that the measurements are reproducible. The guidelines and requirements for authors that have been developed grew out of an NCI-sponsored meeting and subsequent publication describing a tiered, fit-for-purpose approach to targeted assay development in mass spectrometry-based proteomics (1.Carr S.A. et al.Targeted peptide measurements in biology and medicine: Best practices for mass spectrometry-based assay development using a fit-for-purpose approach.Mol. Cell. Proteomics. 2014; 13: 907-917Abstract Full Text Full Text PDF PubMed Scopus (402) Google Scholar). We have also incorporated numerous helpful and important suggestions from the community obtained during the 4-month-long public commentary period. The need for establishing guidelines for targeted MS measurements parallels the situation in discovery proteomics prior to 2004 when similar issues relating to lack of ability to ascertain reliability of published results prompted the journal Molecular & Cellular Proteomics to develop and adopt the first set of guidelines for publication of peptide and protein identification data using mass spectrometry (2.Carr S.A. Aebersold R. Baldwin M. Burlingame A. Clauser K. Nesvizhski A. The need for guidelines in publication of peptide and protein identification data.Mol. Cell. Proteomics. 2004; 3: 531-533Abstract Full Text Full Text PDF PubMed Scopus (412) Google Scholar). These guidelines, which have been repeatedly revised and updated over the past several years (3.Bradshaw R.A. Burlingame A.L. Carr S.A. Revised draft guidelines for proteomic data publication.Mol. Cell. Proteomic. 2005; 4: 1223-1225Abstract Full Text Full Text PDF PubMed Google Scholar, 4.Chalkley R.J. Clauser K.R. Carr S.A. Updating the MCP proteomic publication guidelines.ASBMB Today. 2009; : 16-17Google Scholar, 5.http://www.mcponline.org/site/misc/ms_guidelines.xhtml,Google Scholar), have been embraced in whole or in part by other journals. The goal now, as it was then, is to try to ensure that reliable and reproducible high quality data and results are entering the proteomics literature. We have endeavored to avoid making these guidelines overly prescriptive and intend to be flexible so as to allow for new technologies and approaches that will inevitably arise. We also intend to regularly revisit and revise the guidelines as needed, as we have done for our other documents for author guidance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".