The mission of the Workflow Interest Network (WIN) is to help scientists and resource facilities improve reproducibility of scientific data by optimizing analytical parameters and providing recommendations for reproducible inter-laboratory workflows.
The WIN is composed of volunteer scientists who believe in inter-laboratory repeatability and reproducibility.
It aims to:
- Collaborate with other ABRF members and research groups to identify key factors that contribute to poor reproducibility and inter-laboratory variability.
- Capture information about best practices and protocols to control these factors and improve reproducibility.
- Promote awareness of reproducible science and share feedback about these key factors through a centralized ABRF online resource.
- Conduct studies leading to recommendation of more integrative and goal-oriented workflows.
Initially, the group will focus on the reproducibility of qualitative and quantitative experiments using mass spectrometry based technology. Ultimately, we welcome scientists from other types of technological platform to join our efforts and share their experiences of developing reproducible workflows in their fields.
Emily Chen - Sr. Director Thermo Fisher Precision Medicine Science Center (Chair)
Allis Chien - Stanford Univerisity (EB Liaison)
Theresa McLaughlin - Stanford University
Achim Treumann - Newcastle University
LeeAnn Higgins - University of Minnesota
Alexandre Rosa Campos - Sanford Burnham Prebys Medical Discovery Institute
Sheng Zhang - Cornell University
WIN presentation in ABRF annual meeting 2017
ABRF WIN 2017 annual meeting ppt 2
WIN ABRF Annual Meeting 2017 Poster
ABRF Phase 2 Study Announcement
Re: ABRF WIN2017 Phase 2 Study: Developing Procedures to Optimize Inter-Laboratory Reproducibility of LC-MS/MS-based Proteomic Analyses
Key words: Quality Control, reproducibility, inter-laboratory, intra-laboratory, LC-MS/MS, proteomics
June 27, 2017
The 2017 ABRF Workflow Interest Network (WIN) is pleased to announce initiation of Phase 2 of a study to promote inter-laboratory reproducibility of quantitative proteomic LC-MS/MS analyses. Past studies of proteomic performance metrics have focused on the retrospective evaluation of collected data. In this study we seek to identify data processing tools, including ID-free quality metrics, to support a proactive approach.
Participating laboratories will receive 2 samples: a mixture of peptide internal standards and a HeLa cell lysate digest. A detailed protocol on how to run these samples will be provided when the samples are shipped. We estimate it should take no more than 24 hours to complete the analysis. Participating laboratories will submit raw files and complete a questionnaire of self-reporting parameters according to our instructions. Results derived from the raw data will be de-identified to maintain the anonymity of the participating laboratories. Each participant will receive a unique identification number allowing them to compare their results with those of other participating laboratories.
The timeline for the study is as follows:
· Sample requests accepted until August 1, 2017
· Samples ship to participants starting August 1, 2017
· Raw data files uploaded to the online repository by October 31, 2017
· Results and analysis presented at the ABRF 2018 conference April 22-25, 2018, in Myrtle Beach, South Carolina, USA, then posted on the ABRF website.
To request a sample, please copy the link to a browser (https://www.surveymonkey.com/r/H8XVSF3) and provide your shipping information before August 1, 2017. The WIN is eager to obtain data from a variety of different laboratories and mass spectrometry platforms, and encourages all mass spectrometry laboratories with an interest in proteomics and data quality metrics to participate. However, because sample preparation and shipping involve a significant investment of time and money, Phase 2 is limited to 40 participants. The research group asks that you only request a sample if you are confident that you will be able to provide your raw files by the end of October. We thank you for your support of the ABRF and look forward to your participation in this study.
The ABRF Workflow Interest Network
Emily Chen (chair)
Achim Treumann, Alex Campos, LeeAnn Higgins, Sheng Zhang, Theresa McLaughlin
Allis Chien (EB liaison)
2018 ABRF Annual Meeting Presentation
Interactive data visualization (it may take 1 minute or so to open the document):