Download to read offline
Uploaded byJuan Antonio Vizcaino
Developing open data analysis pipelines in the cloud: Enabling the ‘big data’ revolution in proteomics
Dr. Juan Antonio Vizcaíno presented on developing open data analysis pipelines in the cloud to enable large-scale analysis of proteomics data. He introduced PRIDE and ProteomeXchange as repositories for proteomics data that are seeing substantial growth. Moving analysis pipelines to the cloud will facilitate public reuse of large datasets, improve scalability, and ensure reproducibility. Initial pipelines have been created for identification, quantification, and quality control of mass spectrometry data and deployed on the EMBL-EBI cloud platform. Future work includes optimizing access to PRIDE data and developing pipelines for analysis of DIA and proteogenomics data.
More Related Content
Similar to Developing open data analysis pipelines in the cloud: Enabling the ‘big data’ revolution in proteomics
Developing open data analysis pipelines in the cloud: Enabling the ‘big data’ revolution in proteomics
- 1. Developing open dataanalysis pipelines in the cloud: Enabling the ‘big data’ revolution in proteomics Dr. Juan Antonio Vizcaíno EMBL-European Bioinformatics Institute (EMBL-EBI) Hinxton, Cambridge, UK E-mail: juan@ebi.ac.uk
- 2. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Overview • Short introduction to PRIDE and ProteomeXchange • Enabling public data re-use • Open analysis pipelines for proteomics data
- 3. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 •PRIDE stores mass spectrometry (MS)- based proteomics data: •Peptide and protein expression data (identification and quantification) •Post-translational modifications •Mass spectra (raw data and peak lists) •Technical and biological metadata •Any other related information •Full support for tandem MS approaches •Any type of data can be stored •From July 2017, an ELIXIR core resource PRIDE (PRoteomics IDEntifications) database http://www.ebi.ac.uk/pride/archive Martens et al., Proteomics, 2005 Vizcaíno et al., NAR, 2016
- 4. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Raw data ID& Quant Meta data ProteomeXchange: A global, distributed proteomics database Vizcaíno et al., Nat Biotechnol, 2014 Deutsch et al., NAR, 2017 http://www.proteomexchange.org Standard data submission and data dissemination practises between the main proteomics repositories New member Mandatory data deposition
- 5. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 PRIDE data submissions and data growth May 2018 (320 datasets) was again a record month in terms of datasets submitted Datasets submitted per month PRIDE contains >85% of all ProteomeXchange datasets Dataset PXD010000 reached on June 1st
- 6. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Stats: Data growth in EMBL-EBI resources Sequence data Micro-array Metabolomics Proteomics
- 7. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 We are already in a “Big data” age for proteomics ”Big data” is a term used to describe data collections where processing becomes problematic due to any combination of their size (Volume), frequency of update (Velocity), diversity (Variety), or level or inaccuracy (Veracity). Slide from: http://www.ibmbigdatahub.com/
- 8. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Overview • Short introduction to PRIDE and ProteomeXchange • Enabling public data re-use • Open analysis pipelines for proteomics data
- 9. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Public Data Reuse in Proteomics Vaudel et al., Proteomics, 2016
- 10. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Data re-use in proteomics keeps increasing Data download volume for PRIDE in 2017: 295 TB 0 50 100 150 200 250 300 350 2013 2014 2015 2016 2017 Downloads in TBs
- 11. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 How can public data be re-used? Martens & Vizcaíno, Trends Bioch Sci, 2017 Vaudel et al., Proteomics, 2016
- 12. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Overview • Short introduction to PRIDE and ProteomeXchange • Enabling public data re-use • Open analysis pipelines for proteomics data
- 13. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Motivation • Make analysis scalable • Facilitate re-use of public data (e.g. PRIDE datasets) • Also applicable to datasets produced by individual labs, since they are becoming larger and larger • Make analysis reproducible • Make pipelines openly available to everyone in the community
- 14. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Infrastructure as a Service (IaaS): • Compute power is remote • Still from compute centres, but on a larger scale • Large local IT infrastructure is then not required • Provides scalability • Will enable wide-availability of the pipelines Moving pipelines to a cloud environment
- 15. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Cloud computing is increasingly used in genomics Simplified workflow launcher: • For AWS • Enabling co-analysis with the larger PanCancer dataset http://icgc.org/working-pancancer-data-aws
- 16. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Supporting Reproducible Science http://www.nature.com/nature/focus/reproducibility/
- 17. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Making data analysis pipelines reproducible • That means using: • Exactly the same software (including the same version), in the same order. • The same protein sequence database (including the same version). • If we use the same files as input to the software, we will get EXACTLY the same results. • If that’s not the case, something has gone wrong. • Computers are much more reliable than people. Picture from: http://www.drugdesigntech.com/how-maximize-the-reproducibility-of-your-research/
- 18. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Develop exemplary proteomics data analysis workflows and deploy them in the EMBL-EBI "Embassy Cloud”: (1) Standard identification workflow (2) Identification workflow for PTMs (3) Quantification (label-free/label-based approaches) (4) Quality Control (to aid data set interpretation/reanalysis evaluation) (5) Versions of quantification approaches (including PTMs) è Connected to public proteomics data from Starting with DDA data
- 19. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 We are using the framework Features: • Tool modularisation • Solutions for data handover between tools with standardised (PSI) formats • Adapters for integrating third-party software (search engines, LuciPHOr, FIDO, percolator, etc.) • Integration into various workflow systems as a basis Analysis software used so far (more to come)
- 20. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Infrastructure Open source infrastructure: Galaxy, Docker, Kubernetes, R, OpenStack, etc Open source analysis software: OpenMS as a starting point
- 21. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Simple interface planned for parameter selection
- 22. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 • The workflows have been deployed into the EMBL-EBI “Embassy Cloud” Portal and fitted with a dashboard as a proof of concept • PRIDE data connection into the cloud is being optimised (not URL available yet) • Only a first step. Lots of work to do: benchmarking, interface for users, technical optimisations, etc. Current status of the DDA pipelines
- 23. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Open analysis pipelines: DIA approaches • We have started working in the development of open data analysis pipelines for the analysis of DIA experiments (the main focus is put in SWATH-MS experiments). • In collaboration with the Stoller Center (Manchester) (B. Graham, T. Hubbard & P. Townsend). • Initial pipeline is based on OpenSWATH, developed in the Stoller. • We are currently migrating it to the EBI “Embassy Cloud” (same infrastructure developed for the DDA pipelines).
- 24. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Open analysis pipelines: proteogenomics • Work in collaboration with J. Choudhary (Institute of Cancer Research, London). Project just started on June 2018. • Two types of pipelines to be developed: • To improve genome annotation. • To enable personalised medicine approaches. • Pipelines based on the OpenMS framework. Weisser et al., JPR, 2016
- 25. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Summary • Public proteomics datasets are on the rise! Reliable (widely used) infrastructure now exists: PRIDE and ProteomeXchange • A lot of possibilities open for reuse of this data • Development of data analysis pipelines in the cloud: • Enable public data reuse, pipelines connected to PRIDE • Scalability • Reproducibility • Make them available to everyone in the community
- 26. Juan A. Vizcaíno juan@ebi.ac.uk BSPRAnnual Scientific Meeting 2018 Bradford, 9 July 2018 Aknowledgements: People Yasset Perez-Riverol Johannes Griss Suresh Hewapathirana Tobias Ternent Jingwen Bai Attila Csordas Deepti Jaiswal Andrew Jarnuczak Mathias Walzer Gerhard Mayer (de.NBI) Acknowledgements All data submitters !!! @pride_ebi @proteomexchange Bobby Graham, Simon Hubbard, Paul Brack and others (Stoller Centre) Jyoti Choudhary & James Wright (ICR)