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

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

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

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

Genomics – ‘Old School’ - Study of a single organism's genome - Genome sequence determined using shotgun sequencing and assembly - >1300 microbes sequenced, first in 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

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

Global Ocean Sampling Expedition

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… 3/08 3/07 4/04

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 GOS: Sequence Diversity in the Ocean Rusch et al (PLoS 2007)

Comparing of Dominant Ribotypes

Comparison of Total Genomic Content

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 GOS Protein Analysis Yooseph et al (PLoS 2007)

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

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. GOS Viral Analysis (Williamson et al PLoSOne 2008)

Viral acquisition of host genes talC Gene GOS Viral Public Viral GOS Bacterial Public Bacterial Public Euk

Reference Genomes Overview reference marine microbes (101 released) - Scaffold for GOS - Sequenced, assembled, autoannotated Isolation Metadata - Incomplete Bottlenecks - Availability of DNA - Purity of DNA Status and Data -

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

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

CAMERA – “Convenient acronym for cumbersome name…” - Henry Nichols, PLoS Biology Mission - Enable Research in Marine Microbiology Debuted March 2007

CAMERA Capabilities Compute Resources node compute grid Tb storage Data and Metadata Resources - Annotated Metagenomic and genomic data Tools Resources - Scalable BLAST - Fragment Recruitment - Metagenomic Annotation - Text Search

512 Processors ~5 Teraflops ~ 200 Terabytes Storage CAMRA Compute and Storage Complex at UCSD/Calit2 Source: Larry Smarr, Calit2

CAMERA Metagenomic Data Volume by Project

CAMERA Metagenomic Samples

CAMERA Users >2000 Registered Since March 2007

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 CAMERA Data Collections

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 Standardizing Contextual Metadata

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

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

CAMERA Contextual Metadata

CAMERA 1.3

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

Scalable BLAST with Metadata

Integration of Metadata and Data

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

Fragment Recruitment Viewer Sequence Similarity Genomic Position Doug Rusch, JCVI

Sequence Similarity Genomic Position Annotation Geographic Legend

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

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

Mate Pairs Highlight Variation

What Genes are Involved

View by Sample

View by Sample Filter by mate status

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

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

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

Search for Dehalogenase

Browse Clusters

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

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