[2013.10.29] albertsen genomics metagenomics
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Genomicsand
MetagenomicsMads Albertsen
Introduction to community systems microbiology2013
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Agenda
Genomics• Introduction• Assembly• Validation• Metabolic reconstruction (SM @ Thursday)
Metagenomics• History• Pitfalls• Potentials
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Introduction
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Genome = Parts list of a single genome
Introduction
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How to get from sequenced DNA to metabolic model?
Introduction
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Extract DNA Shear DNA Sequence
Wet lab work
Bioinformatics
Reads50-500 bp
Assembly ScaffoldingContigs
1kb – 100 kbp
N N
ScaffoldsHopefully Mbp
Definitions
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Read
Paired-end read
Mate-pair read
Insert size
Contig
Scaffold
Coverage
N
> 1 kbp insert
A sequenced piece of DNA
Sequencing both ends of a short DNA fragment
Sequencing both ends of a long DNA fragment
The length of the DNA fragment
A set of overlapping DNA segments that represents a consensus region of DNA
Contigs separated by gaps of known length
The number of times a specific position in the genome is covered by reads
50-500 bp
300-600 bp insert
length
4x
Assembly
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Genome
Fragment
Sequence
AssembleScaffold 1
Inspiration: http://goo.gl/VOZVVg
Scaffold 2
Paired-end reads
Contig 1 Contig 19Contig 10
Assembly
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYInspiration: http://goo.gl/VOZVVg
Genome(3.000.000 letters)
Reads(50-500 letters each)
Sequencing Assembly
Genome(3.000.000 letters)
Assembly
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“It was the best of times, it was the worst of times, it was the age of
wisdom, it was the age of foolishness, it was the epoch of belief, it was
the epoch of incredulity,.... “
Dickens, Charles. A Tale of Two Cities. 1859. London: Chapman Hall
Example: http://goo.gl/nMWDAkVelvet example courtesy of J. Leipzig 2010
Assembly
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Way too much data to make all vs. all comparison
Assembly
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYExample: http://goo.gl/nMWDAkVelvet example courtesy of J. Leipzig 2010
theageofwi
the hea eag age geo eof ofw fwi
sthebestof
sth the heb ebe bes est sto tof
astheageof
ast sth the hea eag age geo eof
worstoftim
wor ors rst sto tof oft fti tim
Imesitwast
ime mes esi sit itw twa was ast
Reads
Kmers (k = 3)
Step 1: Convert reads into kmers
Assembly
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ast sth the hea eag age geo eof
Step 2: Join kmers with n-1 overlap
Assembly
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ast sth the hea eag age geo eofthe hea eag age geo eof ofw fwi
sth theheb ebe bes est sto tof
wor ors rststo tof
tim
oft
fti
ast
was
twa
itw sit esi mes ime
Step 2: Join kmers with n-1 overlap
Do the same for all reads…
Assembly
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Step 3: Simplify the graph
Assembly
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYExample: http://goo.gl/nMWDAkVelvet example courtesy of J. Leipzig 2010
“It was the best of times, it was the worst of times, it was the age of
wisdom, it was the age of foolishness, it was the epoch of belief, it was
the epoch of incredulity,.... “ Dickens, Charles. A Tale of Two Cities. 1859. London: Chapman Hall
≠
It was the
be
wor
age
epochst
oftimes
wisdom
foolishness belief
incredulity
=Contigs
Assembly
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYExample: http://goo.gl/nMWDAkVelvet example courtesy of J. Leipzig 2010
What is the minimum kmer size that results in a single contig?
Kmer = 3
Assembly
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYExample: http://goo.gl/nMWDAkVelvet example courtesy of J. Leipzig 2010
What is the minimum kmer size that results in a single contig?
Kmer = 3
ItwasthebestoftimesitwastheworstoftimesitwastheageofwisdomitwastheageoffoolishnessitwastheepochofbeliefitwastheepochofincredulityKmer = 10
Assembly
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYExample: http://goo.gl/nMWDAkVelvet example courtesy of J. Leipzig 2010
Repeat = repeated DNA sequence that can’t be spanned by reads
Assembly
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Why not just increase the kmer size?
Assembly
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYExample: http://goo.gl/nMWDAkVelvet example courtesy of J. Leipzig 2010
the hea eag age geo eof ofw fwi
theageofw heageofwi
theageofwi
Errors!
Kmer = 3 Kmer = 9
Kmers with errors = 2/2
Kmers with errors = 3/8
Validation
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I’ve assembled my 4.3 Mbp genome into 25 scaffolds
with a N50 of 553 kbp.
Is it a good assembly?
Validation
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N50
Validation
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Estimating repeat content
Validation
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4 repeats in 2 copies each
Validation
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4 repeats in 2 copies each
How could I close this genome?
Validation
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How complete is the genome?
Validation
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100-106 Essential single copy genes
(can also be used to identify contamination)
Gen
es
Phyla
Survey of essential single copy genes across sequenced phyla
Validation
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYInspect the assembly
Validation
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• N50 does not make much sense
• Repeat content versus the number of scaffolds• Calculate the percentage of essential genes• Inspect the assembly
Metabolic reconstruction
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4.3 Mbp genome… and so what?
(@Thursday)
Metagenomics
Mads AlbertsenIntroduction to community systems microbiology
2013
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Introduction
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Genome = Parts list of a single genome
Introduction
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Metagenome = Parts list of the community
Photo: D. Kunkel; color, E. Latypova
Introduction
”...functional analysis of the collective genomes of soil microflora, which we term the metagenome of the soil.”
- J. Handelsman et al., 1998
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Introduction
PubMed: metagenom*[Title/Abstract]
”...functional analysis of the collective genomes of soil microflora, which we term the metagenome of the soil.”
- J. Handelsman et al., 1998
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Introduction
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”...functional analysis of the collective genomes of soil microflora, which we term the metagenome of the soil.”
- J. Handelsman et al., 1998
PubMed: metagenom*[Title/Abstract]
Sequencing costs
http://www.genome.gov/sequencingcosts/
Introduction
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Metagenomics ≠ Amplicon sequencing
Sequencing and assembly
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≈3.000.000 bppr. genome
≈1000 bp+contigs
150 bp reads
Assigning information
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Contigs
Function
Taxonomy
Databases
Binning
What have metagenomics been used for?
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Rusch et al., 2007 Plos Biology
Exploration
Qin et al., 2010 Nature
• 6.3 Gbp of sequence (2x Human genomes, 2000 x Bacterial genomes)
• Most sequences were novel compared to the databases
• 127 Human gut metagenomes• 600 Gbp sequence (200 x Human genomes)• 3.3 million genes identified• Minimal gut metagenome definded
What have metagenomics been used for?
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• A characteristic microbial fingerprint for each of the nine different ecosystem types
Dinsdale et al., 2008 Nature
Comparative Specific functions
Hess et al., 2011 Science
• Identified 27.755 putative carbohydrate-active genes from a cow rumen metagenome
• Expressed 90 candidates of which 57% had enzymatic activity against cellulosic substrates
What have metagenomics been used for?
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• Genome extraction from low complexity metagenome
• Candidatus Accumulibacter phosphatis• The first genome of a polyphosphate
accumulating organism (PAO) with a major role en enhanced biological phosphorus removal
Extracting genomes
• Genome extraction of low abundant species (< 0.1%) from metagenomes
• First complete TM7 genome• Access to genomes of the ”uncultured
majority”
Garcia Martin et al., 2006 Nat. Biotechnol. Albertsen et al., 2013 Nat. Biotechnol.
Pitfalls
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Metagenomics made easy
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Great resources – but use with care
MG-RAST example
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Contigs
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Dataset overview
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FunctionTaxonomy
Taxonomy and Function overview
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Compare with other samples
Samples Functional categories
Pitfalls
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You always get billions of data!
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Pitfalls
Is your DNA extraction OK?... and the samples you want to compare with?
Did you sequence enough?Did you know the GC bias of your protocol?Did you normalize for sequencing depth?Did you use the same sequencing platform?
Assembly = data not quantitative!Are you comparing assembled data with reads?
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Databases
Contigs
Databases
...you only see what is in the database
Annotated metagenome
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What is in the databases?
PhylaClassOrderSpecies
2946
1001268
90249405
99322
Genomes 16S
Finshed Genomes in IMGVs.
Greengenes 16S rRNA database
Note: only including 1 strain pr. species
*97% clustering
*
MG-RAST example
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Contigs
650.000 EBPR proteins with taxonomy assigned
How similar are they to the genomes in the database?
Sludge microbes vs. Database genomes
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650.000 EBPR proteins
Note: not abundance weighted
Sludge microbes vs. Database genomes
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650.000 EBPR proteins1.260.000 Human gut
Qin et al., 2010 NatureRAST ID: 4448044.3
Note: not abundance weighted
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Sludge microbes vs. Database genomes
The 7 genera with most EBPR proteins assigned
Effect of missing genomes
What is the effect of not having closely related genomes in the database?
1. Remove a genome from the database
2. Search the removed genome against the database
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Effect of missing genomes
Best hit
Bacteria 1268Proteobacteria 564Betaproteobacteria 84Rhodocyclales 5Rhodocyclaceae 5
Accumulibacter phosphatis
blastp
Related genomes
4326 proteins
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Effect of missing genomes
Best hit
Accumulibacter phosphatis
blastp
Related genomes
4326 proteinsAzoarcus
Bacteria 1268Proteobacteria 564Betaproteobacteria 84Rhodocyclales 5Rhodocyclaceae 5
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Effect of missing genomes
MEGAN LCA
Accumulibacter phosphatis
blastp
Lowest common ancester (LCA) approach:Hit 1: Beta-proteobacteria 80% IDHit 2: Gamma-proteobacteria 79% IDHit 3: Actinobacteria 59% ID
Assigned to Proteobacteria
Related genomes
4326 proteins
Bacteria 1268Proteobacteria 564Betaproteobacteria 84Rhodocyclales 5Rhodocyclaceae 5
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Effect of missing genomes
MEGAN LCA
Accumulibacter phosphatis
blastp
Genus
No hits 261
Bacteria 325
Proteobacteria 860
Beta- 853
Rhodocyclaceae 1149
4326 proteins:• 27% correctly
classified on genus level
• 54% not assigned the correct class
• 101 genera identified
Related genomes
Lowest common ancester (LCA) approach:Hit 1: Beta-proteobacteria 80% IDHit 2: Gamma-proteobacteria 79% IDHit 3: Actinobacteria 59% ID
Assigned to Proteobacteria
4326 proteins
Bacteria 1268Proteobacteria 564Betaproteobacteria 84Rhodocyclales 5Rhodocyclaceae 5
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Effect of missing genomes
MEGAN LCA
Nitrospira defluvii
Bacteria 1268Nitrospirae 3
blastp
Related genomes
4268 proteins:• 1% correctly
classified on phylum level
Phylum
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Effect of missing genomes
MEGAN LCA+
KEGG
Nitrospira defluvii
blastp
Related genomesBacteria 1268Nitrospirae 3
What about function?
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Effect of missing genomes
MEGAN LCA+
KEGG
Nitrospira defluvii
blastp
Related genomesBacteria 1268Nitrospirae 3
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Effect of missing genomes
Nitrospira defluvii
blastp
Related genomes
MEGAN LCA+
KEGG
Bacteria 1268Nitrospirae 3
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Implication of missing genomes
Function A
Function B
Function C
Function D
Pitfalls
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You always get billions of data!
Metagenomics
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”If you want to understand the ecosystem
you need to understand the individual species
in the ecosystem”
Metagenomics
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Lion + Eagle ≠ Flying Lion
Potentials
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Who - when, where and why?
Culturing
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How do we get the genomes?
Few microorganisms can be easily cultured (<<5%)Microorganisms needs to be studied in their environment
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How do we get the genomes?
What you think you study What you actually study
Single cell genomics
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How do we get the genomes?
CulturingFew microorganisms can be easily cultured (<<5%)Microorganisms needs to be studied in their environment
Only routinely performed in specialized labsVery incomplete genomes (mean 40%, range 10-90%)
https://www.bigelow.org/
Single cell genomics
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How do we get the genomes?
CulturingFew microorganisms can be easily cultured (<<5%)Microorganisms needs to be studied in their environment
Only routinely performed in specialized labsVery incomplete genomes (mean 40%, range 10-90%)
Metagenomics
https://www.bigelow.org/
DNA extraction
Sequencing
Assembly Contigs
1000+ bp
100-150 bp
Reads
Metagenomics
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Why not full genomes?
100++ Abundant species (≈3 Mbp each)
DNA extraction
Sequencing
Assembly Contigs
1000+ bp
100-150 bp
Reads
Metagenomics
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Why not full genomes?
1. Micro-diversity
2. Separation of genomes (Binning)
100++ Abundant species (≈3 Mbp each)
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Not 1 strain
Many closely related strains
AAAAAAAAAAAAAA
AAAAAAAAATAAAA
AAAAAAAAACAAAA
AAAAAAAAA
TAAAA
CAAAA
What you get
AAAAA
Assembly
Extracting genomes
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Low micro-diversityHigh micro-diversity
Short term enrichment
Extracting genomes
DNA extraction
Sequencing
Assembly Contigs
1000+ bp
100-150 bp
Reads
Metagenomics
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Why not full genomes?
1. Micro-diversity
2. Separation of genomes (Binning)
100++ Abundant species (≈3 Mbp each)
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Genomic signatures:- GC / Codon usage- Tetranucleotide frequency + statistical method
Complex sample
PhD student
”Binning”
Binning
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Genomic signatures:- GC / Codon usage- Tetranucleotide frequency + statistical method
Complex sample
PhD student
”Binning”
Problems:- Short pieces of sequence (1-10kbp)- Local sequence divergence
Binning
Sequence composition-independent binning
Sample 1
Abun
danc
e
Sample 2
Abun
danc
e
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Binning
Sequence composition-independent binning
Sample 1 Sample 2
Abundance Sample 1
Abun
danc
e Sa
mpl
e 2
Abun
danc
e
Abun
danc
e
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Binning
1. Reduce micro-diversity
2. Use multiple related samples
Abundance Sample 1
Abun
danc
e Sa
mpl
e 2
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Binning
1. Reduce micro-diversity
2. Use multiple related samples
Abundance Sample 1
Abun
danc
e Sa
mpl
e 2
Abundance Sample 1
Abun
danc
e Sa
mpl
e 2
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Binning
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYH. Daims & C. Dorninger, DOME, University of Vienna
• Nitrospira enrichment running for years
• 3 dominant species
• No micro-diversity
Binning
Short term enrichment
Full-scale EBPR plantSBR reactor
Days 1. Reduction of (micro)-diversityCENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
Short term enrichment
Full-scale EBPR plantSBR reactor
2. Two different
DNA extraction methods
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
Colored using a set of 100 phylogenetic marker genes
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
Colored using a set of 100 phylogenetic marker genes
TM7-1 (1.6%)
TM7-2 (0.7%)
TM7-3 (0.2%)
TM7-4 (0.06%)
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
Zoom on target
TM7-2 (0.7%)
Colored using a set of 100 phylogenetic marker genes
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
Zoom on target
PC2
PC1
TM7-2
PCA on genomic signatures
TM7-2 (0.7%)
Colored using a set of 100 phylogenetic marker genes
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
Colored using a set of 100 phylogenetic marker genes
TM7-1 (1.6%)
Candidate phylum TM7
Saccharibacteria
Candidatus Saccharimonas aalborgensis
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
Phyla
Genes (HMM models)
Essential single copy genesAssembly inspection
Genome validation
CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.
http://madsalbertsen.github.io/multi-metagenome/Short: goo.gl/0ctA3
• Guides• Workflow scripts• Example data• All the code• Reccomendations
Multi-metagenome
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...add more samples!
Complex samples
S. M. Karst, AAU
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It’s just a potential!
..and a poorly translated description of it.
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Metabolites
Proteins
mRNA
DNA
Meta-bolomics
Meta-proteomics
Meta-transcriptomics
Meta-genomics
Data integration
In Situ methods
Community structure Microbial functions
Extraction
P-Removal:
N-Removal:
-Removal:
Foaming:
Ethanol production:
Microbial needsEcology
Understanding ecosystems
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G.W. Tyson
Per H. NielsenSimon J. McIllroySøren M. KarstEB group
C. Dorringer H. Daims M. WagnerP. Hugenholtz
University of Vienna
University of Queensland
Questions? @MadsAlbertsen85
MadsAlbertsenma@bio.aau.dk
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