Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Data-intensive bioinformatics on HPC and Cloud

681 views

Published on

My presentation at the COST-CHARME 'Big Data Training School for Life Sciences', on 18-22 September 2017 in Uppsala, Sweden.

Published in: Science

Data-intensive bioinformatics on HPC and Cloud

  1. 1. Data-intensive bioinformatics on HPC and Cloud Ola Spjuth <ola.spjuth@farmbio.uu.se> Department of Pharmaceutical Biosciences and Science for Life Laboratory Uppsala University
  2. 2. Today: We have access to high-throughput technologies to study biological phenomena
  3. 3. 2017: Human whole genome sequenced in 3 days for ~$1100 …requires supercomputers for analysis and storage Massively parallel sequencing…. 2017: Illumina HiSeq X systems. 15K whole human genomes per year 2016: NGI data velocity 950 Mbp/hour = 16 Mbp/s
  4. 4. Analysis Scientists Sample transfer Mode of operation Platforms Pre-processing (NGI) Research (SNIC) Data delivery
  5. 5. Software + reference data Support Education Compute resources Storage resources Efficiency + automation A national e-infrastructure
  6. 6. What we sequenced at NGI /
  7. 7. National distribution 7 2016
  8. 8. Some statistics Storage usage Projects at SNIC-UPPMAX Data-intensive bioinformatics Other disciplines Support tickets
  9. 9. Biggest challenge: Data growth • Storage is filling up. Projects do not end. Users do not clean up data. WGS projects are very large. • At the heart of the problem: Services are currently free of charge. Our strategy: • Cost of data storage and analysis should be assessed and included in budget before data production • Move data away from expensive storage near clusters – Constrain project lifetimes (shorten allowed time for data on systems) – Move towards tiered storage solutions – Improve efficiency in analyses (education, monitoring, support) • Investigate scaling with other centra and public cloud providers • Long-term storage: Unresolved question… 9
  10. 10. NGS users • Key observations – Storage biggest challenge – Many and inefficient users, lots of software (admin burden, support, education) – Free resources (no cost) does not promote efficient usage • Resource strategies – When investing in computational hardware, it takes a long time from funding decision until the resources are operational (10-12 months on average). – Expansion of resources are done at specific points in time, low flexibility between these. – Decision on resources are made by the SNIC board with limited influence from life science scientists (SciLifeLab) • Can we improve on this? 10
  11. 11. Cloud computing • Purchase a service instead of hardware • Pay-per-use pricing • Scale up and down as you need • Virtual infrastructure/machines/storage 11
  12. 12. Cloud in Bioinformatics How can we take advantage of cloud resources? Simplest example: • Start pre-made VMI • Upload data • Perform scientific task • Download results • Terminate VMI Easy to scale this up to using many instances! Or….. is it? • What if I want to run 100 instances in parallel? • What about if I want a new tool? Later versions? • Do I need to upload data every time? 12
  13. 13. So we want to set up and use a virtual cluster • Multiple compute nodes • Network • Distributed storage • Firewall, DNS, reverse proxy, etc. So, we now have a virtual cluster. And now? Batch-like system – Install a queueing system, e.g. SLURM – Install bioinformatics software on the VMI Big Data system – Install HDFS + Hadoop/Spark on the system (There are tools that can help automating these procedures) 13
  14. 14. Why cloud in the life sciences? • Access to resources – Flexible configurations – On-demand – Cost-efficient? • Collaborate on international level – Publish/federate data – E.g. Large sequencing initiatives, “move compute to the data” • New types of analysis environments – Hadoop/Spark/Flink etc. – Microservices, Docker, Kubernetes, Mesos 14
  15. 15. Challenges with cloud • Tradition: Strong HPC tradition in academia – Sweden: Existing HPC resources funded by Research Council and personnel at 6 centra in Sweden (SNIC) • Economy: Cost model is new – Difficult to assess the costs • Legal: Working with sensitive data • Educational: New technology for many 15
  16. 16. Some SciLifeLab cloud options 16
  17. 17. ● Geographically distributed federated IaaS cloud based on 2nd generation HPC-hardware ● Built using OpenStack SNIC Cloud in Sweden
  18. 18. Needs in bioinformatics • Primarily resources with a lot of RAM and storage (high I/O) • Preferably transparent system, users don’t want to deal with e- infrastructure at all • How to work with storage (tiered?) 18
  19. 19. Virtual Machines and Containers Virtual machines • Package entire systems (heavy) • Completely isolated • Suitable in cloud environments Containers: • Share OS • Smaller, faster, portable • Docker 19
  20. 20. MicroServices • Decompose functionality into smaller, loosely coupled, on-demand services communicating via an API – “Do one thing and do it well” • Services are easy to replace, language-agnostic – Minimize risk, maximize agility – Suitable for loosely coupled teams – Portable - easy to scale – Multiple services can be chained into larger tasks Software containers (e.g. Docker) are ideal for microservices!
  21. 21. Orchestrating containers • Origin: Google • A declarative language for launching containers • Start, stop, update, and manage a cluster of machines running containers in a consistent and maintainable way • Suitable for microservices Containers Scheduled and packed containers on nodes
  22. 22. Connecting the microservices • A suitable way of using containers are connecting them into a (scientific) workflow. • Tools like Pachyderm (http://pachyderm.io/), Luigi (https://github.com/spotify/lui gi) and Galaxy (https://galaxyproject.org/) can assist with this. • Goal: Reproducible, fault- tolerant, scalable execution. 22
  23. 23. How can regular users take advantage of these technologies? • Virtual Research Environment (VRE) is an appealing option – Easy and user-friendly access to computational resources, tools and data, commonly for a scientific domain – Usually access from a browser • Multi-tenant VRE – log into shared system • Private VRE - deploy on your favorite cloud provider 23
  24. 24. Tools Tools Data Data VREs aim to bridge this gap! Researcher Other researchers Virtual Research Environments
  25. 25. Researcher Tools Data Compute and storage resources Virtual Research Environment! Other researchers Virtual Research Environments
  26. 26. PhenoMeNal • Horizon 2020 project, 2015-2018 • Virtual Research Environments (VRE), Microservices, Workflows • Towards interoperable and scalable Metabolomics data analysis • Private environments for sensitive data http://phenomenal-h2020.eu/ DockerHub Virtual Infrastructure GitHub
  27. 27. Cloudflare kubeadm Terraform kubectl Packer • Enable users to deploy their own virtual infrastructure on an IaaS provider • Containerize tools, orchestrate microservices with workflow systems on top of Kubernetes PhenoMeNal approach and stack KubeNow
  28. 28. Users should not see this…
  29. 29. Users should see this! 29
  30. 30. Start-to-end MS-analysis 30
  31. 31. Research focus in my group e-Science methods development Smart data management, predictive modeling Applied e-Science research Drug discovery and individualized diagnostics e-infrastructure development Automation, Big Data
  32. 32. Privacy preservation Workflows Big Data frameworks Data management and predictive modeling Data federation Compute federation
  33. 33. ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● NGS projects 2014 2015 2016 2017 Efficiency feedback to users began 0 20 40 60 80 100 Efficiency(%) Date ●● ● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●●●●●●●● ● ● ●● ●●● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ●●● ●● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ● ● ●● ● ●●● ●●●●●● Other projects 2014 2015 2016 2017 0 20 40 60 80 100 Selected research questions How can we improve efficiency on shared HPC for data- intensive bioinformatics? 1. M. Dahlö, D. Schofield, Wesley Schaal and O. Spjuth, Tracking the NGS revolution: Usage and system support of bioinformatics projects on shared high-performance computing clusters. In Preparation. 2. O. Spjuth, E. Bongcam-Rudloff, J. Dahlberg, M. Dahlö, A. Kallio, L. Pireddu, F. Vezzi, and E. Korpelainen, Recommendations on e- infrastructures for next-generation sequencing. Gigascience, 2016, 5:26 3. S. Lampa, M. Dahlö, P. I. Olason, J. Hagberg, and O. Spjuth, Lessons learned from implementing a national infrastructure in sweden for storage and analysis of next-generation sequencing data. Gigascience, 2013, 2:9 Data locality? Outsourcing? Martin Dahlö
  34. 34. Selected research questions Can Big Data frameworks aid data-intensive bioinformatics? 1. A. Siretskiy, L. Pireddu, T. Sundqvist, and O. Spjuth. A quantitative assessment of the Hadoop framework for analyzing massively parallel DNA sequencing data. Gigascience. 2015; 4:26. 2. L. Ahmed, A. Edlund, E. Laure, and O. Spjuth. Using Iterative MapReduce for Parallel Virtual Screening. Cloud Computing Technology and Science (Cloud-Com), 2013 IEEE 5th International Conference on , vol.2, no., pp.27,32, 2-5, 2013 3. M. Capuccini, L. Carlsson, U. Norinder and O. Spjuth. Conformal Prediction in Spark: Large-Scale Machine Learning with Confidence. EEE/ACM 2nd International Symposium on Big Data Computing (BDC), Limassol, 2015, pp. 61-67. 4. M. Capuccini, L.Ahmed, W. Schaal, E. Laure and O. Spjuth Large-scale virtual screening on public cloud resources with Apache Spark Journal of Cheminformatics 2017 9:15 Laeeq Valentin Marco Efficient Virtual Screening with Apache Spark and Machine Learning Hadoop pipeline scales better than HPC and is economical for current data sizes
  35. 35. “EasyMapReduce: Leverage the power of Spark And Docker To scale scientific tools in MapReduce fashion“ 35 https://spark-summit.org/east-2017/events/easymapreduce-leverage-the- power-of-spark-and-docker-to-scale-scientific-tools-in-mapreduce-fashion/
  36. 36. Selected research questions How useful are Scientific Workflows in data-intensive research? O. Spjuth et al. Experiences with workflows for automating data-intensive bioinformatics. Biology Direct. 2015 Aug 19;10(1):43. S. Lampa, J. Alvarsson and O. Spjuth. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. Journal of Cheminformatics, 2016, 8:67 SamuelJon • Streamline analysis on high- performance e-infrastructures • Support reproducible data analysis • Enable large-scale data analysis
  37. 37. Selected research questions How can we deploy smart, high-availability services with APIs? http://www.openrisknet.org • Horizon 2020 project, 2017-2020 • E-Infrastructure for chemical safety assessment • Multi-tenant Virtual Environments, microservices • APIS, “Semantic interoperability” • Academia – industry • Much focus on standardizing chemical data and predictive modeling Staffan Jonathan Arvid
  38. 38. Research questions around the corner • Public and private data sources are not static. How can we continuously improve predictive models as data changes? • We can generate too much data. Can predictive modeling aid data acquisition, storage and analysis? 38
  39. 39. Reactive/continuous modeling Data sources Coordinate Integrate Version Monitor Publish models Archive models User Train and assess model
  40. 40. HASTE Hierarchical Analysis of Spatial and TEmporal and image data From intelligent data acquisition via smart data management to confident predictions PI, Aim1: Carolina Wählby Aim 3: Andreas HellanderAim 2: Ola Spjuth 29 MSEK 2017-2022
  41. 41. . . . Training data Can we use privileged information to improve machine learning models? Training Can we make a valid ranking and guide data acquisition? . . . Is something interesting happening? Can we assign valid probabilities for that? Collect more data Online setting Aim 2: Guiding data acquisition with machine learning
  42. 42. Aim3: Explore a hierarchical model based on Information Layers Data warehouse, distributed storage Edge Cloudlet, private cloud
  43. 43. European Open Science Cloud (EOSC) • The vast majority of all data in the world (in fact up to 90%) has been generated in the last two years. • Scientific data is in direct need of openness, better handling, careful management, machine actionability and sheer re-use. • European Open Science Cloud: A vision of a future infrastructure to support Open Research Data and Open Science in Europe – It should enable trusted access to services, systems and the re-use of shared scientific data across disciplinary, social and geographical borders – research data should be findable, accessible, interoperable and re- usable (FAIR) – provide the means to analyze datasets of huge sizes 43http://ec.europa.eu/research/openscience/index.cfm?pg=open-science-cloud
  44. 44. Acknowledgements Wes Schaal Jonathan Alvarsson Staffan Arvidsson Arvid Berg Samuel Lampa Marco Capuccini Martin Dahlö Valentin Georgiev Anders Larsson Polina Georgiev Maris Lapins Jon-Ander Novella 44 Lars Carlsson Ernst Ahlberg Ola Engqvist SNIC Science Cloud Andreas Hellander Salman Toor Caramba.clinic Kim Kultima Stephanie Herman Payam Emami
  45. 45. Research group website: http://pharmb.io Thank you

×