iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to microbes
•
0 j'aime•1,848 vues
The document discusses extending the iPlant cyberinfrastructure to support microbes in addition to plants. It provides an overview of iPlant, including its funding from NSF, collaborations, resources like data storage and computing platforms, and applications for analysis. Future plans are outlined to build tools and streamline workflows for metagenomics and enable high-throughput computing for microbial data.
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to microbes
Similaire à iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to microbes (20)
Dernier
Dernier (20)
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to microbes
- 1. Bonnie Hurwitz, PhD Arizona Health Sciences Center Extending the iPlant Cyberinfrastructure: From Plants to Microbes
- 2. The iPlant Collabora,ve Community Cyberinfrastructure for Life Science hEp://www.iplantcollaboraIve.org
- 3. iVirus and iMicrobe Joaquin Ruiz, PhD Dean, College of Science Darren Boss Devesh Chourasiya Funding Staff Ma= Sullivan, PhD Shane Burgess, PhD Dean, CALS
- 4. The iPlant Collaborative Vision Enable life science researchers and educators to use and extend cyberinfrastructure to understand and ultimately predict the complexity of biological systems
- 5. How iPlant CI Enables Discovery Challenge: Create an easy-‐to-‐use plaNorm powerful enough to handle data-‐intensive biology Many bioinformaIcs tools “off limits” to those without specialized computaIonal backgrounds.
- 6. iPlant is a collaborative virtual organization The iPlant Collaborative Who makes up iPlant?
- 7. The iPlant Collaborative How is iPlant funded? iPlant Renewed by NSF September 2013 begins next 5 year period Scientific Advisory Board Focus on Genotype-Phenotype science NSF Recommended expansion of scope beyond plants
- 8. iPlant collaborates to enable access to the solutions that work the best for the community… The iPlant Collaborative Who does iPlant collaborate with?
- 9. How iPlant CI Enables Discovery Overview of resources End Users Computa0onal Users Teragrid XSEDE ü Storage ü Computa0on ü Hos0ng ü Web Services ü Scalability Building a plaNorm that can support diverse and constantly evolving needs.
- 10. iPlant Data Store ü Initial 100 GB allocation – TB allocations available ü Automatic data backup ü Easy upload /download and sharing The resources you need to share and manage data with your lab, colleagues and community
- 11. Discovery Environment Hundreds of bioinformatics Apps in an easy-to- use interface ü A platform that can run almost any bioinformatics application ü Seamlessly integrated with data and high performance computing ü User extensible – add your own applications
- 12. Agave API Fully customize iPlant resources ü Science-as-a-service platform ü Define your own compute, and storage resources (local and iPlant) ü Build your own app store of scientific code and workflows
- 13. Atmosphere Cloud computing for the life sciences ü Simple: One-click access to more than 100 virtual machine images ü Flexible: Fully customize your software setup ü Powerful: Integrated with iPlant computing and data resources
- 14. DNA Subway Educational workflows for Genomes, DNA Barcoding, RNA-Seq ü Commonly used bioinformatics tools in streamlined workflows ü Teach important concepts in biology and bioinformatics ü Inquiry-based experiments for novel discovery and publication of data
- 15. Bisque Image analysis, management, and metadata ü Secure image storage, analysis, and data management ü Integrate existing applications or create new ones ü Custom visualization and image handling routines and APIs
- 16. Typical End Users Computa0onal Users Teragrid XSEDE iMicrobe and iVirus Leverage the iPlant Cyberinfrastructure ü Storage ü Computa0on ü Analysis ü App dev. ü Pipeline dev. ü Code distrib. ü Data Discoverability Using iPlant for:
- 17. What’s Under the Hood? Stampede -‐ High Level Overview • Base Cluster (Dell/Intel/Mellanox): – Intel Sandy Bridge processors – Dell dual-‐socket nodes w/32GB RAM (2GB/core) – 6,400 nodes – 56 Gb/s Mellanox FDR InfiniBand interconnect – More than 100,000 cores, 2.2 PF peak performance • Co-‐Processors: – Intel Xeon Phi “MIC” Many Integrated Core processors – Special release of “Knight’s Corner” (61 cores) – All MIC cards are on site at TACC more than 6000 installed final installa0on ongoing for formal summer acceptance – 7+ PF peak performance • Max Total Concurrency: – exceeds 500,000 cores – 1.8M threads • Entered produc,on opera,ons on January 7, 2013
- 18. iMicrobe/ iVirus: New App Development June 2013 – May 2014: 13: New Apps 1: High-throughput analysis pipeline
- 19. Forging Ahead with iPlant • Build a metegenomics toolkit • Streamline metagenomics workflows • Enable high-‐ throughput compuIng • Provide key datasets for computaIon
- 20. iPlant Data Store The resources you need to share and manage data with your lab, colleagues and community
- 21. Overview of the iPlant Data Store Some Complica0ons of Big Data • Difficult/slow transfers • Expense for storage/backup • Difficult to share and publish • Metadata • Analysis
- 22. iPlant Supports the Life Cycle of Data Store Markup Search Transfer Analyze Visualize Collaborate Share Data Results A Results B Algo1 Algo2 Pre-‐ PublicaIon Post-‐ PublicaIon
- 23. Teragrid XSEDE Overview of the iPlant Data Store Scalable, Reliable, Redundant, High-‐performance • Access your data from mul0ple iPlant services • Automa0c data backup (redundant between University of Arizona and University of Texas) • Mul0ple ways to share data with collaborators • Mul0-‐threaded high speed transfers • Default 100GB alloca0on. >1TB alloca0ons available with jus0fica0on
- 24. Overview of the iPlant Data Store Some important items we won’t see Source DesInaIon Copy Method Time (seconds) CD My Computer cp 320 Berkeley Server My Computer scp 150 External Drive My Computer cp 36 USB2.0 Flash My Computer cp 30 iDS MyComputer iget 18 My Computer My Computer cp 15 Close to op0mum condi0ons; transfer between Univ. of Arizona and UC Berkeley 100GB: 29m15s 1 GB / 17.5 seconds
- 25. Discovery Environment Hundreds of bioinformatics Apps in an easy-to-use interface
- 26. Overview of the iPlant Discovery Environment Through the Discovery Environment you have: • High-‐powered compu0ng • iPlant data store • Easy to use interface • Virtually limitless apps • Analysis history (provenance)
- 27. What you can do in the iPlant DE? Scalable plajorm for powerful compu0ng, data, and applica0on resources • Navigate the components of the DE • Access and manipulate data • Start and complete an analysis • Track your analysis and see your results
- 28. Why is iPlant DE Scalable? Democra0ze your code • Rich plajorm for bioinforma0cs ~400 apps (and coun0ng) • Data co-‐localized with analysis • Easy to use interface, with access to support • Easy to integrate and customize your own tools
- 29. Goal: Create a metagenomic assembly. Task 1: Upload metagenomic fasta file to your personal data store Task 2: Run quality control on your raw sequence reads Task 3: Find and select an assembly tool (e.g. Metavelvet) Task 4: Specify parameters and your input files. Run the assembly App. Task 5: Monitor the progress of your analysis and save parameters. Task 6: View your results. Discovery Environment Example
- 30. Sequence Quality Control in the iPlant DE
- 31. Genome, Metagenome, and Transcriptome Assembly Genome and Metagenome Assembly ALLPATHS-LG Newbler SOAPdenovo Velvet MetaVelvet ABySS SPA Digital Norm. IDBA-UD Transciptome Assembly Trinity De novo: Reference-guided: SOAPdenovo-Trans Velvet/Oasis Trans-ABySS Tophat Cufflinks In the DEKey:
- 32. Where is the sample data?
- 33. Where is the Assembly App?
- 34. Specify Data and Assembly Parameters
- 36. Track Analyses and Results
- 37. What about Annotations? • Annotations are descriptions of features on contigs in a genome / metagenome – Ab initio gene predictions – Protein homology (Genbank nr, SIMAP) – Curated protein resources (COG, Kegg, …) • Secondary annotations – InterPro Scan (Pfam, PIR, Prosite, …) – GO and other ontologies – Pathway Mapping (Kegg, Metacyc, Ecocyc)
- 38. Genome and Metagenome Assembly ALLPATHS-LG Newbler SOAPdenovo Velvet MetaVelvet ABySS SPA Digital Norm. IDBA-UD Ab initio Gene Prediction Glimmer Prodigal FragGeneScan Metagene MetaGenmark Transciptome Assembly Trinity De novo: Reference-guided: SOAPdenovo-Trans Velvet/Oasis Trans-ABySS Tophat Cufflinks Meta- Genome input Evidence input Conversion Tools Annotation Primary: Secondary: BLAST tophat2gff cufflinks2gff Visualization k-mer based InterProScan InterPro2GO JBrowse Web-Apollo Data Commons: Genomes and Metagenomes Proteins / Genes Reference Annotations Metadata (in irods) At TACCIn the DE Under DevelopmentKey: Assembly & Annotation at iPlant ü Storage ü Computa0on ü Analysis ü Data Access ü Code Distr. ü Query by metadata
- 40. The Louis Pasteur Method: We can’t “see” all bacteria using culture-‐based approaches Razumov (1932) “The Great Plate Anomaly.”
- 42. Environmental Sample Extract DNA High throughput sequencing Assemble reads Gene Prediction library creation Making Sense of Metagenomes Function Taxonomy Compare to known proteins
- 43. Viromes are dominated by the Unknown PhoIc AphoIc Hurwitz BL & Sullivan MB. The Pacific Ocean Virome (POV). PLoS One. 8: e57355. Bacteria 5% Eukaryota 1% Archaea 0% Viruses 3% Viruses 7% Bacteria 4% Eukaryota 1% Archaea 0% Unknown 88% Unknown 91% We need new tools!
- 44. Phage FuncIon based on Environment PcPipe: a VigneEe in Viral Metagenomics
- 45. Assemble Find Genes Protein Clusters Input reads Input reads Cluster Genes BIN Organizing the Unknown Yooseph S, et al. (2007) The Sorcerer II Global Ocean Sampling expedi0on: expanding the universe of protein families. PLoS Biol 5(3):e16.
- 46. 27K High-‐Confidence Viral Protein Clusters GOS 50% POV + GOS 22% POV 28% Isolate Phage 1% 2X environmental viral protein clusters 70% of data now included Hurwitz BL & Sullivan MB. (2013) The Pacific Ocean Virome (POV). PLoS One. 8: e57355.
- 47. Ocean Microbial CommuniIes Vary by Environmental Factors Pacific Ocean Virome: Geographic Region LocaIon on a Transect Season Depth Hurwitz BL & Sullivan MB. (2013) The Pacific Ocean Virome (POV). PLoS One. 8: e57355.
- 48. GDS GFS M5OD M4OS M2MS LF26S LA26S LJ26S LJ12S LJ4S M1CS STCS SFCS SFSS SFDS M3MD LJ12D LJ26D LJ4O LJ12A LJ4D LJ4A M6O1K M7O4K LF26D LF26O LJ12O LF26A LA26A LA26O LJ26O LA26D LJ4O LJ12A LJ4D LJ4A M6O1K M7O4K LF26D LJ12O LF26O LF26A LJ26O LA26A LA26O LA26D LJ26D LJ12D M3MD GDS GFS M4OS M5OD LJ4S LJ12S LJ26S LA26S LF26S M2MS M1CS SFSS SFDS SFCS STCS Aphotic Photic AphoticPhotic Hurwitz BL, Brum J. and Sullivan MB. Depth Stra0fied Func0onal and Taxonomic Niche Specializa0on in the ‘Core’ and ‘Flexible’ Pacific Ocean Virome . In Review. Photic vs Photic Aphotic vs Photic Aphotic vs Aphotic Photic vs Aphotic Protein Clusters group by phoIc zone Many PCs shared Some PCs shared Few PCs shared
- 49. Host Genes that Promote Viral ReplicaIon Fe-‐S cluster biogenesis and funcIon DNA/Protein biosynthesis and repair Host “wake-‐up” Energy producIon in photosynthesis Niche Defining PhoIc Core: Hurwitz BL, Hallam S., Sullivan MB. (2013) Metabolic Reprogramming by Viruses in the Sunlit and Dark Ocean. Genome Biology, 14, R123. Hurwitz BL, Brum J. and Sullivan MB. Depth Stra0fied Func0onal and Taxonomic Niche Specializa0on in the ‘Core’ and ‘Flexible’ Pacific Ocean Virome . In Review.
- 50. AdapIve for High Pressure Environments DNA replicaIon iniIaIon DNA repair MoIlity Energy producIon in the TCA cycle Niche Defining AphoIc Core: Hurwitz BL, Hallam S., Sullivan MB. (2013) Metabolic Reprogramming by Viruses in the Sunlit and Dark Ocean. Genome Biology, 14, R123. Hurwitz BL, Brum J. and Sullivan MB. Depth Stra0fied Func0onal and Taxonomic Niche Specializa0on in the ‘Core’ and ‘Flexible’ Pacific Ocean Virome. In Review.
- 51. QC sequences • FASTQ_ shrinker Assembly part 1 • Velveth pcpipe part 1 • Cd-‐hit-‐2d Input to Analyses • Blastx to nr • QIIME • RarefacMon New.fastq Find Genes • Meta-‐ Gene-‐Mark POV PCs pcpipe part 2 • Cd-‐hit Assembly part 2 • Velvetg New.a.faa iPlant Discovery Environment: Automated Workflows POV + Novel PCs PCpipe: creaIng protein clusters for viral ecology
- 52. 1. Select the Apps 2. Order the Apps 3. Map Outputs to Inputs 4. Run the analysis Crea0ng Workflows Easy as 1-‐2-‐3-‐4
- 53. Create a New Workflow
- 55. Select the Apps
- 56. Add the Apps
- 57. Remove an App
- 58. Order the Apps
- 59. New.a.faa POV PCs Map Outputs to Inputs
- 60. A New Workflow
- 61. User’s ORFs POV PCs Run the Workflow
- 62. Automated workflows cannot use Apps that run on the HPC
- 63. QC sequences • FASTQ_ shrinker Assembly part 1 • Velveth pcpipe part 1 • Cd-‐hit-‐2d AnnotaIon • Protein annotaMon • Secondary annotaMon New.fastq Find Genes • Meta-‐ Gene-‐Mark POV PCs pcpipe part 2 • Cd-‐hit pcpipe workflow Assembly part 2 • Velvetg New.a.faa Gotchas in the PCpipe Workflow FoundaIon API Runs on XSEDE (HPC) cannot be used in a workflow POV + Novel PCs FoundaIon API Runs on XSEDE
- 64. iPlant App iMicrobe adapter iMicrobe condor node BLAST vs SIMAP cd-hit-2d cd-hit extract proteins in novel PCs SIMAP Annotation Pipeline Management Foundation Code HPC Job distribution on condor on condor on condor on TACC on condor Step 1 Step 2 Step 3 Step 4 Step 5 User ORFs Existing Protein Clusters Input 1 Input 2 ORFs in existing clusters ORFs in new clusters Annotation for new clusters Output 1 Output 2 Output 3 An Integrated PCPipe
- 65. Exis0ng PCs (POV) Directory of User defined ORFS PCPipe App
- 66. Collaborating with iPlant • Solve computa0onal boulenecks • Make tools easier to use • Share Data • Provide community input Collaboration
- 67. QuesIons or Comments? Bonnie Hurwitz, PhD
- 68. QC sequences • FASTQ_ shrinker Assembly • Velvet pcpipe part 1 • Cd-‐hit-‐2d Gene AnnotaIon • SIMAP • GO • PFAM… New.fastq PCs pcpipe part 2 • Cd-‐hit Find Genes • Prodigal ORFs PCpipe: Protein Cluster Pipeline Steps in iPlant DE PCs + Novel PCs (HPC or Cloud)