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The Sorcerer II Global The Sorcerer II Global ocean sampling expeditionocean sampling expedition
Katrine LekangKatrine Lekang
• Global Ocean Sampling Global Ocean Sampling project (GOS)project (GOS)• CAMERACAMERA• METAREPMETAREP
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Background
• Microorganisms in the world oceans: what do we know?– Play an important role in the marine
ecosystem and global biogeochemical cycles.– 10 million species?
• How can second generation sequencing techniques contribute?
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Craig Venter
• Human genome
• Ocean sampling (GOS)
• Synthetic biology– Modified microorganisms
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Global Ocean Sampling
• The expedition’s goal is to evaluate the microbial diversity in the world’s oceans using the tools and teqhniques developed to sequence the human and other organisms’ genomes.
• They want to increase the knowledge about microbial diversity and expect that this will help them understand how ecosystems function and to discover new genes of ecological and evolutionary importance
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Sorcerer II - expedition
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Sampling• 200-400 liters of
water every 200 miles• Filtering
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Methods
• Total DNA was extracted (0.1-0.8 µm)
• Random insert clone libraries
• End-sequencing of 44 000-420 000 clones per sample (Sanger sequencing)
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Development of new tools
• Fragment recruitment analyses for performing and visualizing comparative genomic analysis when a reference sequence is available.
• New assembly techniques that use metadata to produce assemblies for uncultivated microbial taxa.
• A whole metagenome comparison tool to compare entire samples at arbitrary degree og genetic divergence.
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Assembly
• Primary assembly: Celera assembler– Pairs of mated reads were testet- overlap-
single pseudo-read– Overlap cut-off 98 % to construct unitigs– Fragmented
• Second assembly: 94 % cut-off
• Series of assemblies at various stringencies for subsets of GOS-data
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Fragment recruitment
• GOS dataset compared with genomes of sequenced microbes (NCBI)- 584 reference genomes
• BLAST- 55 % identity• 70 % of the reads aligned to one or more
genomes. – Many with large gaps and low identity
• Recruited reads: stringent criteria- 30 % of the reads
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Fragment recruitment analysis
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Identification of structural variation with metagenomic data
• Variations in genome structure (rearrangements, duplications, inserions, deletions) can be explored by fragment recruitment
• Mated sequencing reads- assessing structural differences between the reference and environmetntal sequence.
• Determines the orientation and distance between two mated sequencing reads.
• Relative location and orientation of mated reads →metadata that can be used to color-code a fragment recruitment plot.
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Fragment recruitment• All genome structure variations that are large enough to prevent recruitment
can be detected → will be associated with missing mates. • Depending on the type of rearrangement present, other recruitment
metadata categories will be present near the rearrangements’ endpoint → possible to distinguish among deletions, translocations, inversions and inverted translocations from the recruitment plots.
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Extreme assembly of uncultivated populations
• Assemblies for abundant, uncultivated microbial genera
• Assembly apporach that resolves conflict – ”Extreme assembly”
• Do not use matepairing data – contigs– Assembly artefacts
• Alternative way to an unguided assembly: start from seed fragments that can be identified as belonging to a particular taxonomic group.
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Fragment recruitment plots
• Investigate variation within a group of related organisms
• Repeatedly seeding extreme assembly with fragments mated to a SAR11 like 16S sequence.
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Sample comparisons
• A method that assess the genetic similarity between two samples that potentially make use of all portions of the genome, not just the 16S rRNA region.
• Assembly independent• Estimate of the fraction of sequence from one
sample that could be considered to be present in the other sample.
• Whole metagenomic similarities were computed for all pairs of samples.
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Variations in gene abundance
• Differences in gene content between samples– Can identify functions that reflect the lifestyles of the
community in the context of its local environment.
• Binning of genes into functional categories – TIGERFRAM hidden Markov models.
• Genes predominately found in a single sample. • Differences between temperate/tropical samples• Differences between samples with almost similar
taxonomy
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CAMERA
• Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis
• http://camera.calit2.net/• A need for a systematic way to explore the
structure and function of ocean ecosystems, and their impact on global carbon processing and climate. – Bridge the gap between the rates of collecting data and interpreting it.
• Monitoring microbial communities in the ocean and their response to environmental changes.
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CAMERA
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Metadata
• CAMERA will integrate sequence data with all available metadata
• Allow researchers to derive correlations between ecology and environmental conditions that may favour one community structure or another.
• Future…. Metadata from satelites and weather stations can be used to help interpret and inform us on how these factors affect microbial processes as well as community composition.
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New generation Bioinformatics tools
• Combine bioinformatical tools with large-scale compute resources
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METAREP
• JCVI Metagenomics Report • http://www.jcvi.org/metarep/#• Analyze and compare annotated metagenomics
datasets• Solr/Lucene search engine• SQL-like query syntax- filter and refine datasets• Functional classification, GO, NCBI taxonomy• Statistical tests• Analyze function in the context of phylogeny
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Web analysis features