A community proposal to integrate proteomics activities in ELIXIR

Meeting “The future of proteomics in ELIXIR”

The meeting took place on March 1^st–2^nd 2017 in Tübingen (Germany). Attendance was widely advertised through ELIXIR dissemination channels (e.g., mailing lists, newsletter) and was open to any interested member of the community. There were 24 attendees representing eleven ELIXIR nodes: Germany (host), Belgium, Czech Republic, Denmark, France, Italy, Netherlands, Sweden, Switzerland, EMBL-EBI, and one representative from the ELIXIR Hub. The detailed minutes of the meeting are available as Supplementary File 1. The meeting started with a presentation given by Rafael Jiménez (ELIXIR Chief Technical Officer) who provided a general overview of the current ELIXIR activities. This initial talk was followed by a series of presentations where the representatives of each node summarized their ongoing activities related to proteomics. All the presentations are freely available at http://tinyurl.com/elixir-proteomics.

The remainder of the meeting was devoted to an open discussion on how to bring together activities, experience, stakeholders, and emerging needs. First of all, ten potential ELIXIR stakeholders in this domain were identified, namely: funding agencies, regulatory bodies, educators, infrastructures, publishers, core facilities, bioinformaticians, life scientists, industries and hospitals/patients. Second, a series of needs and challenges were outlined for each of the stakeholders, and these were then mapped to each of the existing ELIXIR platforms: Data, Tools, Interoperability, Compute and Training (see below). The output of this activity is summarized in Supplementary Table 1. In the second day of the meeting, more concrete topics, derived from the identified needs and challenges were outlined by the attendees, and then organised in wider areas, so called “clusters”. Finally, they were prioritised, with the idea that these could form the basis for future proteomics activities in ELIXIR.

Current ELIXIR internal structure

Here we describe the current status of ELIXIR platforms and use cases, by May 2017. ELIXIR’s activities are structured around platforms and use cases. They bring together resources and expertise from the ELIXIR Nodes and form the basic unit of operation within ELIXIR. The ELIXIR platforms are responsible of the implementation of the ELIXIR programme and are organised in five key areas: Data, Tools, Compute, Interoperability and Training. The platforms are complemented by four use cases that represent four scientific communities: Human data, Rare diseases, Marine metagenomics and Plant sciences (Figure 2). The use cases drive the work of the ELIXIR platforms by defining their bioinformatics needs and requirements. The close collaboration between the ELIXIR use cases and platforms ensures that the services developed by the ELIXIR platforms are fit for purpose. Each platform and use case is led by a group of senior scientists from across the ELIXIR nodes. In addition to the funding available in each national ELIXIR node, the main source of financial support for ELIXIR activities comes from the ELIXIR-EXCELERATE EU H2020 project. Additional activities are funded through other complementary grants as well as ‘Implementation studies’ supported by the ELIXIR Hub.

Figure 2. Overview of current ELIXIR platforms and use cases (by May 2017).

The ELIXIR platforms. The Data platform focuses on sustaining Europe’s life science data infrastructure. This platform is working on guidelines and indicators for data resources to improve their impact and sustainability¹⁷. It also works on improving links between curated data resources and literature data.

The Tools platform is dedicated to services and connectors to drive access and exploitation of bioinformatics research software. The main key activities within this platform are centred to facilitate the discovery, benchmarking and interoperability of software. It does also focus on software development best practices, as well as on a strategy for workflows and software containers.

The Interoperability platform supports the discovery, integration and analysis of biological data. Activities driven by this platform are organised in projects around identifiers, metadata standards and linked data. It also works on the description of interoperability services as well as specialised workshops named BYOD (Bring Your Own Data)¹⁸ to improve the “FAIRness” (Findable, Accessible, Interoperable and Re-usable) of data resources¹⁹.

The Compute platform is dedicated to the compute, storage, transfer, authentication and authorization of biological data relying on services provided by ELIXIR nodes and e-infrastructures. Finally, the Training platform aims to increase the professional skills for managing and exploiting data. Part of activities are meant to train researchers, trainers and service providers, but it also includes other activities related to e-learning, to improve the discovery and availability of training materials and to measure the impact of training.

Current ELIXIR use cases. There are four use cases. First of all, the Human data use case develops long-term strategies for managing and accessing sensitive human data. The Rare diseases use case supports the development of new therapies for rare diseases. The Marine metagenomics use case works towards a sustainable metagenomics infrastructure to nurture research and innovation in the marine domain. Finally, the Plant sciences use case develops an infrastructure to support genotype-phenotype analysis for crop and tree species.

Areas prioritised in the meeting

In an attempt to assess the relative priorities of the various areas of proteomics that might steer integration into ELIXIR, attendees voted on the relative importance of the topics. The following list shows the top-ranked areas for future ELIXIR related proteomics activities (called “Clusters” from now on), sorted in descending order by the number of votes received:

Cluster 1. Multi-omics approaches. It includes topics such as data integration of proteomics and other types of omics data, correlation between gene and protein expression, and development of data standards for “multi-omics” data types. A closely related group of activities was “Cancer proteomics”, comprising topics such as support for clinical proteomics data (including large patient cohorts) and cancer “multi-omics” (proteogenomics) studies.

Cluster 2. Proteoforms and PTMs. The term proteoform²⁰ represents the different molecular forms in which the protein product of a single gene can be found, including changes due to genetic variations, alternatively spliced RNA transcripts and PTMs, among other events. This cluster of activities included topics such as the handling, validation of proteoforms and creation of standards for their description, improvement of the existing connection between proteoforms, genes and metabolites (topic related to Cluster 1 above), and activities devoted to explain unidentified spectral signals.

Cluster 3. Quality Control (QC) activities. In any analytical discipline, QC is essential. Due to the fact that proteomics is a newer and still rapidly developing field, QC has historically not been as well-developed in proteomics as in, for instance, the more established small molecule mass spectrometry field¹⁴. This cluster is therefore focused on activities to develop automatic and reliable pipelines for QC of proteomics data at different levels.

Cluster 4. Data analysis workflows and cloud computing. The concrete activities outlined here could be summarized as the development of robust, reproducible, scalable, user-friendly, integrated, QC-controlled, data analysis pipelines, ideally enabling the use of compatible cloud infrastructures, which in addition, could also be used for data storage. Infrastructure supporting efficient development of such pipelines and workflows is also important, including tool repositories and documentation, workflow management systems and interfaces for accessing computational resources.

Cluster 5. Protein quantification and statistics. This topic encompasses the improvement of protein inference in shotgun proteomics approaches, the use of peptides that match to more than one protein precursor, and enhanced data integration/harmonisation for quantitative proteomics. It was perceived by many attendees that, although many of these issues are often considered solved, there are still many improvements possible in this area.

Cluster 6. Metadata, standardisation, annotation, and data management. This topic also includes the improvement in the annotation of proteomics datasets, in particular in data repositories like PRIDE (to facilitate public data reuse by third parties), the development and/or extension of existing Laboratory Information Management Systems (LIMS), standard data formats, and guidelines summarising best practises for data management, following FAIR principles.

The rest of the areas discussed got only one or two votes from the attendees, and included activities related to interactomics, structural proteomics, metabolomics, metaproteomics, the development of benchmarking datasets, and training efforts. Finally, it is worth highlighting an additional proposal for the creation of a repository for tool-related ideas.

Alignment between ELIXIR activities and the needs of the proteomics community

As mentioned in the ‘Methods’ section, ELIXIR activities are currently structured in five platforms (Data, Tools, Interoperability, Compute and Training) and four longitudinal use cases (Human data, Rare diseases, Marine metagenomics and Plant sciences) (Figure 2). Our preferred option is that proteomics becomes the main focus of one additional ELIXIR use case in the near future. If there is no scope for proteomics to have its own use case, other options could be possible, for instance the integration of proteomics activities into the existing ELIXIR use cases. We think that the current use cases, heavily focused on genomics data, would also benefit from having a “multi-omics” perspective. Out of the existing cases, Human data would be an obvious choice. The other three could also benefit from proteomics activities: Rare diseases (clinical proteomics), Marine metagenomics (metaproteomics), and Plant sciences (plant proteomics).

In any case, and without considering specific use cases, it is clear that there are several topics that, in our opinion, would fit very well into the scope of the current five ELIXIR platforms:

1- Data platform. Metadata, standardisation, annotation and data management activities (Cluster 6), and “multi-omics” approaches (Cluster 1), involving data integration efforts from different omics data types, would be highly relevant in this context. Moreover, QC efforts (Cluster 3) are essential for all such types of data re-use. Indeed, data re-use is a very important aspect in proteomics data, as only 30–40% of the acquired data is typically exploited¹⁴. This creates extremely exciting opportunities for proteomics data re-use with specialized tools that can lead to the discovery of new biological information²¹.

2- Tools platform. In this platform, the overall aim would be to increase the visibility, quality and sustainability of proteomics software developed following best practices. First of all, more proteomics tools should be effectively included into the ELIXIR tool registry, and highlighted there appropriately. However, it is worth highlighting that there are around 350 tools proteomics tools represented in this resource already. In addition, the development of improved and user-friendly quantification algorithms and tools, along with direct coupling to dedicated and performant statistical analysis (Cluster 5), also connects directly to the ELIXIR tools platform. Other possible activities would be related to Cluster 4, and would involve the improvement of the description and sharing of proteomics workflows, facilitating the encapsulation of workflows, data and tools into proteomics software containers that could be shared across ELIXIR, taking advantage of existing resources. Finally, the proposed idea in the meeting to create of a repository for tool-related ideas, would also be applicable in the wider ELIXIR context.

3- Interoperability platform. Obviously, the activities of this platform are very relevant in the case of “multi-omics” approaches (Cluster 1), for instance the development of data standards for these type of approaches, e.g. to enable better data integration and visualisation. A close connection can also be made to metabolomics with regards to QC (Cluster 3), as the main technology of choice (mass spectrometry) is shared between these two fields.

4- Compute platform. Workflow analysis pipelines and activities related to the development of cloud infrastructures (Cluster 4) and QC activities (Cluster 3) should be highlighted here. This would involve different pieces of infrastructure supporting the efficient development of such workflows, e.g. the EDAM ontology and the ELIXIR tool registry¹⁵, open data formats, and workflow management systems, among others.

It is important to note that, although originally set up in the Data platform, the proteomics ELIXIR implementation study mentioned in the ‘Introduction’ section aims to make a first step forward in this direction, for the popular shotgun (MS/MS) approaches. As a proof of concept, these pipelines will be deployed first in the EMBL-EBI “Embassy Cloud”, a cloud infrastructure based in the Open Stack operating system, with the idea that in the future they can be made available in other cloud systems (e.g. Amazon EC2, Google Cloud, Microsoft Azure), so that the developed pipelines can be freely reused by any interested researcher in the community. In the context of the implementation study, the pipelines are connected to the PRIDE database, bringing the analysis tools closer to the data as datasets become larger in size and complexity. This trend is ongoing for other omics technologies in the context of ELIXIR: for instance, the pipelines developed in the context of the Marine metagenomics use case²².

5- Training platform. ELIXIR is already working actively in coordinating training activities across Europe (e.g. the already mentioned TeSS portal). Although training was not specifically highlighted as one of the main areas for future development during the meeting, the reason is that everyone assumes that this is implicitly a key need in all bioinformatics fields. While excellent training courses and workshops have already been created in the field, a higher degree of coordination across Europe should be achieved for bioinformatics training activities, and in particular for computational proteomics topics, possibly in coordination with other fields such as metabolomics (see next section).

Connection to metabolomics and other ESFRIs

As mentioned already, metabolomics and proteomics share a common experimental platform: mass spectrometry. A parallel effort is currently ongoing to integrate metabolomics activities in ELIXIR. There are many topics of common interest where both fields could benefit from a closer interaction: e.g. the development of common software (for QC, data visualisation, signal processing, to name but a few) and open data standards. Some initiatives are already working towards this goal, e.g. the computational mass spectrometry group (http://compms.org/), and some recent activities recently carried out by the PSI, but a lot of work remains to be done. One concrete proposal would be to coordinate efforts concerning training activities. ELIXIR would be an ideal platform to enable this coordination effort.

Coming back to the proteomics field, PRIME-XS (http://www.primexs.eu) was a four-year Infrastructure project funded by the EU FP7 Programme (2011–2015), coordinated by Prof. Albert Heck. The main aim of PRIME-XS was to provide funded access to an infrastructure of state-of-the-art proteomics technology to the European biological and biomedical research community. It is anticipated that future tailored EU H2020 infrastructure grant calls might provide an opportunity to create a second iteration of this successful project. The potential synergies with ELIXIR, considering the outlined activities in this white paper, would be obvious at different levels, for instance the provision of a cloud computing infrastructure to store and analyse the acquired data in a scalable and reproducible way, and the development of pipelines to allow data integration between proteomics and other omics data types.