Abstract The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) has standardized data submission and dissemination of mass spectrometry proteomics data worldwide since 2012. In this paper, we describe the main developments since the previous update manuscript was published in Nucleic Acids Research in 2017. Since then, in addition to the four PX existing members at the time (PRIDE, PeptideAtlas including the PASSEL resource, MassIVE and jPOST), two new resources have joined PX: iProX (China) and Panorama Public (USA). We first describe the updated submission guidelines, now expanded to include six members. Next, with current data submission statistics, we demonstrate that the proteomics field is now actively embracing public open data policies. At the end of June 2019, more than 14 100 datasets had been submitted to PX resources since 2012, and from those, more than 9 500 in just the last three years. In parallel, an unprecedented increase of data re-use activities in the field, including ‘big data’ approaches, is enabling novel research and new data resources. At last, we also outline some of our future plans for the coming years.
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The ProteomeXchange consortium at 10 years: 2023 update
Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.
more » « less- NSF-PAR ID:
- 10379918
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Nucleic Acids Research
- Volume:
- 51
- Issue:
- D1
- ISSN:
- 0305-1048
- Page Range / eLocation ID:
- p. D1539-D1548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.more » « less
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Abstract Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.
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Applying for grants from the National Science Foundation (NSF) requires a paradigm shift at many community and technical colleges, because the primary emphasis at two-year colleges is on teaching. This shift is necessary because of the NSF expectation that a STEM faculty member will lead the project as Principal Investigator. Preparing successful NSF grant proposals also requires knowledge, skills, and strategies that differ from other sources from which two-year colleges seek grant funding. Since 2012, the Mentor-Connect project has been working to build capacity among two-year colleges and leadership skills among their STEM faculty to help them prepare competitive grant proposals for the National Science Foundation’s Advanced Technological Education (NSF-ATE) program. NSF-ATE focuses on improving the education of technicians for advanced technology fields that drive the nation’s economy. As an NSF-ATE-funded initiative, Mentor-Connect has developed a three-pronged approach of mentoring, technical assistance, and digital resources to help potential grantees with the complexities of the proposal submission process. Grant funding makes it possible to provide this help at no cost to eligible, two-year college educators. Mentor-Connect support services for prospective grantees are available for those who are new to ATE (community or technical colleges that have not received an NSF ATE award in 7 or more years), those seeking a larger second grant from the ATE Program after completing a small, new-to-ATE project, and for those whose first or second grant proposal submission to the NSF ATE Program was declined (not funded). The Mentor-Connect project has succeeded in raising interest in the NSF-ATE program. Over a seven-year period more than 80% of the 143 participating colleges have submitted proposals. Overall, the funding rate among colleges that participated in the Mentor-Connect project is exceptionally high. Of the 97 New-to-ATE proposals submitted from Cohorts 1 through 6, 71 have been funded, for a funding rate of 73%. Mentor-Connect is also contributing to a more geographically and demographically diverse NSF-ATE program. To analyze longer-term impacts, the project’s evaluator is conducting campus site visits at the new-to-ATE grantee institutions as their initial ATE projects are being completed. A third-party researcher has contributed to the site-visit protocol being used by evaluators. The researcher is also analyzing the site-visit reports to harvest outcomes from this work. This paper shares findings from seven cohorts that have completed a grant cycle with funding results known, as well as qualitative data from site visits with the first two cohorts of grantees. Recommendations for further research are also included.more » « less
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Applying for grants from the National Science Foundation (NSF) requires a paradigm shift at many community and technical colleges, because the primary emphasis at two-year colleges is on teaching. This shift is necessary because of the NSF expectation that a STEM faculty member will lead the project as Principal Investigator. Preparing successful NSF grant proposals also requires knowledge, skills, and strategies that differ from other sources from which two-year colleges seek grant funding. Since 2012, the Mentor-Connect project has been working to build capacity among two-year colleges and leadership skills among their STEM faculty to help them prepare competitive grant proposals for the National Science Foundation’s Advanced Technological Education (NSF-ATE) program. NSF-ATE focuses on improving the education of technicians for advanced technology fields that drive the nation’s economy. As an NSF-ATE-funded initiative, Mentor-Connect has developed a three-pronged approach of mentoring, technical assistance, and digital resources to help potential grantees with the complexities of the proposal submission process. Grant funding makes it possible to provide this help at no cost to eligible, two-year college educators. Mentor-Connect support services for prospective grantees are available for those who are new to ATE (community or technical colleges that have not received an NSF ATE award in 7 or more years), those seeking a larger second grant from the ATE Program after completing a small, new-to-ATE project, and for those whose first or second grant proposal submission to the NSF ATE Program was declined (not funded). The Mentor-Connect project has succeeded in raising interest in the NSF-ATE program. Over a seven-year period more than 80% of the 143 participating colleges have submitted proposals. Overall, the funding rate among colleges that participated in the Mentor-Connect project is exceptionally high. Of the 97 New-to-ATE proposals submitted from Cohorts 1 through 6, 71 have been funded, for a funding rate of 73%. Mentor-Connect is also contributing to a more geographically and demographically diverse NSF-ATE program. To analyze longer-term impacts, the project’s evaluator is conducting campus site visits at the new-to-ATE grantee institutions as their initial ATE projects are being completed. A third-party researcher has contributed to the site-visit protocol being used by evaluators. The researcher is also analyzing the site-visit reports to harvest outcomes from this work. This paper shares findings from seven cohorts that have completed a grant cycle with funding results known, as well as qualitative data from site visits with the first two cohorts of grantees. Recommendations for further research are also included.more » « less
