Slideshow Transcript

1. Slide 1: CAMERA A Metagenomics Resource for Microbial Ecology Saul A. Kravitz J. Craig Venter Institute Rockville, Maryland USA KNAW Colloquium May 29, 2008
2. Slide 2: Goals • Introduce you to CAMERA • Encourage you to use CAMERA • What can CAMERA do for you?
3. Slide 3: Presentation Outline • Introduction to Metagenomics • Global Ocean Sampling (GOS) Expedition • CAMERA Capabilities and Features - Compute Resources - Data Resources - Tools Resources • Looking Forward
4. Slide 4: Metagenomic Questions • Within an environment - What biological functions are present (absent)? - What organisms are present (absent) • Compare data from (dis)similar environments - What are the fundamental rules of microbial ecology • Adapting to environmental conditions? - How? - Evidence and mechanisms for lateral transfer • Search for novel proteins and protein families - And diversity within known families
5. Slide 5: Genomics vs Metagenomics • Genomics – ‘Old School’ - Study of a single organism's genome - Genome sequence determined using shotgun sequencing and assembly - >1300 microbes sequenced, first in 1995 - DNA usually obtained from pure cultures (<1%) • Metagenomics - Application of genome sequencing methods to environmental samples (no culturing) - Environmental shotgun sequencing is the most widely used approach - Environmental Metadata provides key context
6. Slide 6: Complexity of Microbial Communities • Simple (e.g., AMD, gutless worm) - Few species present (<10) - Diverse  Variations on standard genomics techniques • Complex (e.g., Soil or Marine) - Many species present (>10, often >1000) - Many closely related  New techniques
7. Slide 7: Global Ocean Sampling Expedition
8. Slide 8: Global Ocean Sampling (GOS) • 178 Total Sampling Locations - Phase 1: 7.7M reads, >6M proteins 3/07 - Phase 2-IO: 2.2M reads 3/08 - Phase 2: ~10M reads future • Diverse Environments - Open ocean, estuary, embayment, upwelling, fringing reef, atoll… 4/04 3/07 3/08
9. Slide 9: GOS: Sequence Diversity in the Ocean Rusch et al (PLoS 2007) • Most sequence reads are unique - Very limited assembly - Most sequences not taxonomically anchored - Relating shotgun data to reference genomes - Annotation challenging • New Techniques Needed - Fragment Recruitment - Extreme Assembly to find pan genomes - Sample to Sample Comparisons
10. Slide 10: Comparing of Dominant Ribotypes
11. Slide 11: Comparison of Total Genomic Content
12. Slide 12: GOS Protein Analysis Yooseph et al (PLoS 2007) • Novel clustering process • Sequence similarity based • Predict proteins and group into related clusters • Include GOS and all known proteins • Findings • GOS proteins • cover ~all existing prokaryotic families • expands diversity of known protein families • ~10% of large clusters are novel • Many are of viral origin • No saturation in the rate of novel protein family discovery
13. Slide 13: Added Protein Family Diversity Yooseph et al (PLoS 2007) Rubisco homologs Known eukaryotes Known prokaryotes GOS prokaryotes New Groups
14. Slide 14: GOS Viral Analysis (Williamson et al PLoSOne 2008) • Study of dsDNA viruses from shotgun data - 155k viral proteins identified from 37 GOS I sites (~2.5%) - 59% of viral sequences were bacteriophage • Viral acquisition and retention of host metabolic genes is common and widespread - Viruses have made these genes “their own” - Clade tightly with viral genes • Codistribution of P-SSM4-like cyanophage and the dominant ecotype of Prochlorococcus in GOS samples.
15. Slide 15: Viral acquisition of host genes talC Gene GOS Viral Public Viral GOS Bacterial Public Bacterial Public Euk
16. Slide 16: Reference Genomes • Overview - 150+ reference marine microbes (101 released) - Scaffold for GOS - Sequenced, assembled, autoannotated • Isolation Metadata - Incomplete • Bottlenecks - Availability of DNA - Purity of DNA • Status and Data - https://research.venterinstitute.org/moore/
17. Slide 17: Motivations for CAMERA • Significant investment in sequencing - Only accessible to bioinformatics elite - Diversity of user sophistication and needs • Bioinformatics and Computation Challenges - Assembly, annotation, comparative analysis, visualization - Dedicated compute resources • Importance of Metadata - Metadata required for environmental analysis - Need to drive standards • Compliance with Convention on Biodiversity
18. Slide 18: Convention on Biological Diversity • Sample in territorial waters? - Country granted certain rights by CBD - Sampling agreements may contain restrictions • CAMERA users must acknowledge potential restrictions on commercial data use • CAMERA maintains mapping of country- of-origin for all data objects
19. Slide 19: CAMERA – http://camera.calit2.net • “Convenient acronym for cumbersome name…” - Henry Nichols, PLoS Biology • Mission - Enable Research in Marine Microbiology • Debuted March 2007 camera-info@calit2.net
20. Slide 20: CAMERA Capabilities • Compute Resources - 512 node compute grid + 200 Tb storage • Data and Metadata Resources - Annotated Metagenomic and genomic data • Tools Resources - Scalable BLAST - Fragment Recruitment - Metagenomic Annotation - Text Search
21. Slide 21: CAMRA Compute and Storage Complex at UCSD/Calit2 512 Processors ~5 Teraflops ~ 200 Terabytes Storage Source: Larry Smarr, Calit2
22. Slide 22: CAMERA Metagenomic Data Volume by Project
23. Slide 23: CAMERA Metagenomic Samples
24. Slide 24: CAMERA Users >2000 Registered Since March 2007
25. Slide 25: CAMERA Data Collections • Metagenomic Sequence Collection - Reads and assemblies w/associated metadata - CAMERA-computed annotation • Protein Clusters - Maintaining clusters from Yooseph et al (Yooseph and Li, ’08) • Genomic Data - Viral, Fungal, pico-Eukaryotes, Microbial - Moore Marine Genomes with Metadata • Non-redundant sequence Collection - Genbank, Refseq, Uniprot/Swissprot, PDB etc
26. Slide 26: Standardizing Contextual Metadata • Genome Standards Consortium - Led by Dawn Field, NIEeS - Members from EU, UK, US • Goals are to promote - Standardization of genomic descriptions - Exchange & Integration of genomic data • Metadata standardization key enabler - MIMS: Min Info for Metagenomic Sample - GCDML: Standard format
27. Slide 27: Contextual Metadata Challenges • Researchers Need to Collect and Submit • Relevant metadata depends on study – MIMS - Specification of minimum metadata • Standardize Exchange Format - GCDML - Comprehensive and extensible - Leverages Existing Ontologies, Validatable And… - Easy for a scientist to use... • Need ongoing software support for tools
28. Slide 28: CAMERA Core Metadata by Project • Defacto Core •Lattitude and Longitude •Collection date •Habitat and Geographic Location • Missing metadata =
29. Slide 29: CAMERA Contextual Metadata
30. Slide 30: CAMERA 1.3 http://camera.calit2.net
31. Slide 31: Scalable BLAST with Metadata • Large searches permitted and encouraged • 454 FLX run vs “All Metagenomic” • Some larger tblastx jobs have run >20 hrs • 10kbp BLASTN vs All Metagenomic – 1 min • BLAST XML or Tabular Export • Searches against NRAA • BLAST XML output feeds MEGAN • Searches against ‘All Metagenomic’ • GUI with metdata • Tabular with metadata
32. Slide 32: Scalable BLAST with Metadata
33. Slide 33: Integration of Metadata and Data
34. Slide 34: Browsing Large Data Collections: Fragment Recruitment Viewer • Microbial Communities vs Reference Genomes - Millions of sequence reads vs Thousands of genomes • Definition: A read is recruited to a sequence if: - End-to-end blastN alignment exists • Rapid Hypothesis Generation and Exploration - How do cultured and wildtype genomes differ? - Insertions, deletion, translocations - Correlation with environmental factors • Export sequence and annotation • Credits: Doug Rusch and Michael Press
35. Slide 35: Fragment Recruitment Viewer Sequence Similarity Genomic Position Doug Rusch, JCVI
36. Slide 36: Sequence Similarity Geographic Legend Genomic Position Annotation
37. Slide 40: Prochlorococcus marinus str. MIT 9312 • Coloring by geography • 80-95% identity cloud • = GOS Indian Ocean • Regions with no coverage • Where? • Real?
38. Slide 41: Mate Status Highlights Differences • Paired end (mate) sequencing • Coloring by mate status • Highlights cultured vs metagenomic differences • Selective display of - Mates by status - Reads by sample
39. Slide 42: Mate Pairs Highlight Variation
40. Slide 43: What Genes are Involved
41. Slide 44: View by Sample
42. Slide 45: View by Sample Filter by mate status
43. Slide 46: Annotation of Environmental Shotgun Data • Gene Finding - Using Yooseph’s Protein Clusters, and/or - Metagene • Functional Assignment - Variation of JCVI prok annotation pipeline* - Leverages protein cluster annotation -- soon • Quality Nearly Comparable to Prokaryotic Genomic Annotation
44. Slide 47: Protein Clusters as Gene Finder • Identification and soft mask of ncRNAs • Naïve identification of ORFs (60aa min) • Add peptides to clusters incrementally - Yooseph and Li, 2008 • Predicted Genes based on ORFS in - Clusters of sufficient size - Clusters that satisfy additional filters
45. Slide 48: Protein Clusters Advantages and Disadvantages • Weaknesses - Homology-based - Stateful (also a strength) - Less sensitive (for now) • Strengths - More specific - Transitive Annotation - Learns over time - Easy to maintain
46. Slide 49: Search for Dehalogenase
47. Slide 50: Browse Clusters
48. Slide 51: Near Future • More extensive data collection • Summary views of data sets by - Annotation - Samples - Mate Status - Taxonomy - Habitat and other contextual metadata • 16S datasets?
49. Slide 52: Credits • JCVI CAMERA Team - Leonid Kagan, Michael Press, Todd Safford, Cristian Goina, Qi Yang, Sean Murphy, Jeff Hoover, Tanja Davidsen, Ramana Madupu, Sree Nampally, Nikhat Zhafar, Prateek Kumar - Doug Rusch, Shibu Yooseph, Aaron Halpern*, Granger Sutton, Shannon Williamson - Marv Frazier and Bob Friedman • Calit2 CAMERA Team - Adam Brust, Michael Chiu, Brian Fox, Adam Dunne, Kayo Arima - Larry Smarr and Paul Gilna http://camera.calit2.net