C A M E R A A Metagenomics Resource for Microbial Ecology

1. C A M E R AA Metagenomics Resource for Microbial Ecology Saul A. Kravitz J. Craig Venter Institute Rockville, Maryland USA KNAW Colloquium May 29, 2008

2. Goals • Introduce you to CAMERA • Encourage you to use CAMERA • What can CAMERA do for you?

3. Presentation Outline Introduction to Metagenomics Global Ocean Sampling (GOS) Expedition CAMERA Capabilities and Features Compute Resources Data Resources Tools Resources Looking Forward

4. 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 Metagenomic Questions

5. 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 Genomics vs Metagenomics

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. Global Ocean Sampling Expedition

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. GOS: Sequence Diversity in the OceanRusch 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. Comparing of Dominant Ribotypes

11. Comparison of Total Genomic Content

12. GOS Protein AnalysisYooseph 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. Added Protein Family Diversity Yooseph et al (PLoS 2007) Rubisco homologs Known eukaryotes Known prokaryotes GOS prokaryotes New Groups

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. Viral acquisition of host genestalC Gene • GOS Viral • Public Viral • GOS Bacterial • Public Bacterial • Public Euk

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. 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 Motivations for CAMERA

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. 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. 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. CAMRA Compute and Storage Complexat UCSD/Calit2 512 Processors ~5 Teraflops ~ 200 Terabytes Storage • Source: Larry Smarr, Calit2

22. CAMERA Metagenomic Data Volume by Project

23. CAMERA Metagenomic Samples

24. CAMERA Users>2000 Registered Since March 2007

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. 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. 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. CAMERA Core Metadata by Project • Defacto Core • Lattitude and Longitude • Collection date • Habitat and Geographic Location • Missing metadata =

29. CAMERA Contextual Metadata

30. CAMERA 1.3 • http://camera.calit2.net

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. Scalable BLAST with Metadata

33. Integration of Metadata and Data

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. Fragment Recruitment Viewer Sequence Similarity Genomic Position Doug Rusch, JCVI

36. Geographic Legend Sequence Similarity Genomic Position Annotation

40. Prochlorococcus marinus str. MIT 9312 • Coloring by geography • 80-95% identity cloud • = GOS Indian Ocean • Regions with no coverage • Where? • Real?

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

42. Mate Pairs Highlight Variation

43. What Genes are Involved

44. View by Sample

45. View by Sample Filter by mate status

46. Annotation ofEnvironmental 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

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

48. Protein ClustersAdvantages and Disadvantages • Weaknesses • Homology-based • Stateful (also a strength) • Less sensitive (for now) • Strengths • More specific • Transitive Annotation • Learns over time • Easy to maintain

49. Search for Dehalogenase

50. Browse Clusters