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

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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

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Goals

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

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Presentation Outline

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

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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

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Genomics vs Metagenomics

• Genomics Old School
• Study of a single organism's genome
• Genome sequence determined using shotgun
  sequencing and assembly
• gt1300 microbes sequenced, first in 1995
• DNA usually obtained from pure cultures (lt1)
• 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

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Complexity of Microbial Communities

• Simple (e.g., AMD, gutless worm)
• Few species present (lt10)
• Diverse
• ? Variations on standard genomics techniques
• Complex (e.g., Soil or Marine)
• Many species present (gt10, often gt1000)
• Many closely related
• ? New techniques

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Global Ocean Sampling Expedition
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Global Ocean Sampling (GOS)

• 178 Total Sampling Locations
• Phase 1 7.7M reads, gt6M 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
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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

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Comparing of Dominant Ribotypes
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Comparison of Total Genomic Content
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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

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

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Viral acquisition of host genestalC Gene

• GOS Viral
• Public Viral
• GOS Bacterial
• Public Bacterial
• Public Euk

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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/

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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

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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

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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_at_calit2.net

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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

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CAMRA Compute and Storage Complexat UCSD/Calit2
512 Processors 5 Teraflops 200 Terabytes
Storage

• Source Larry Smarr, Calit2

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CAMERA Metagenomic Data Volume by Project
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CAMERA Metagenomic Samples
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CAMERA Usersgt2000 Registered Since March 2007
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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

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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

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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

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CAMERA Core Metadata by Project

• Defacto Core
• Lattitude and Longitude
• Collection date
• Habitat and Geographic Location
• Missing metadata

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CAMERA Contextual Metadata
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CAMERA 1.3

• http//camera.calit2.net

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Scalable BLAST with Metadata

• Large searches permitted and encouraged
• 454 FLX run vs All Metagenomic
• Some larger tblastx jobs have run gt20 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

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Scalable BLAST with Metadata
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Integration of Metadata and Data
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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

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Fragment Recruitment Viewer
Sequence Similarity
Genomic Position
Doug Rusch, JCVI
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Geographic Legend
Sequence Similarity
Genomic Position
Annotation
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Prochlorococcus marinus str. MIT 9312

• Coloring by geography
• 80-95 identity cloud
• GOS Indian Ocean
• Regions with no coverage
• Where?
• Real?

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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

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Mate Pairs Highlight Variation
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What Genes are Involved
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View by Sample
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View by Sample Filter by mate status
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Annotation ofEnvironmental Shotgun Data

• Gene Finding
• Using Yoosephs 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

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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

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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

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Search for Dehalogenase
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Browse Clusters
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Near Future

• More extensive data collection
• Summary views of data sets by
• Annotation
• Samples
• Mate Status
• Taxonomy
• Habitat and other contextual metadata
• 16S datasets?

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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