Post on 17-Jan-2017
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Sample to Insight
Data analysis on day one Taxonomic and functional microbiome profiling with the Microbial Genomics Pro Suite Dr. Arne Materna, Dr. Andreas Sand, QIAGEN
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Sample to Insight
Microbial Genomics Pro Suite
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Extraction bias
NGS library preparation
Data interpretation
Inhibitor removal
Amplification bias
Converting data into insight
Sample processing
Complete Sample to Insight workflows with integrated microbiome research
Processing massive sample data is not enough
Best-in-class algorithms for microbiome profiling and comparative analytics
Comparative analytics: Key for microbiome research
Insight
Microbial Genomics Pro Suite turns microbiomes into insights
QIAGEN Microbial Gx Pro Suite
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Microbial Genomics Pro Suite
Webinar: Microbiome analysis from Sample to Insight
o The Microbial Genomics Pro Suite o Taxonomic microbiome profiling o Functional metagenomics o Q&A
QIAGEN Microbial Gx Pro Suite
Sample to Insight 4
Microbial Genomics Pro Suite
Webinar: Microbiome analysis from Sample to Insight
o The Microbial Genomics Pro Suite o Taxonomic microbiome profiling o Functional metagenomics o Q&A
QIAGEN Microbial Gx Pro Suite
Sample to Insight 5
MGM
CLC Microbial Genomics Module
GFM
CLC Genome Finishing Module
Meta GeneMark
MetaGeneMark plugin
CLC Genomics Workbench
CLC Genomics
Server
Applications areas
Microbiome profiling o Function o Taxonomy o Comparative Analytics
NGS-based isolate typing o Taxonomy o AM resistance o Epidemiology
Microbial Genomics , Microbiome Analysis
Food Safety Pathogen typing Outbreak analysis
Genome/Metagenome Assembly o De novo assembly
(including PacBio) o Metagenome-
assembly o Gene finding, annotation
QIAGEN Microbial Gx Pro Suite
Microbial Genomics Pro Suite
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Explore the taxonomic and functional composition of microbial communities. The microbiome as a biomarker Identify microbial community composition profiles that are indicative of: o patient health, o changing yields of agricultural crops or livestock, o emergence of public health threats.
Taxonomic profiling o 16S, 18S, ITS, amplicons
Functional profiling o Whole metagenome assembly o Gene finding & annotation
Comparative analysis between samples (differential abundance analysis)
QIAGEN Microbial Gx Pro Suite
Microbiome profiling in the Microbial Genomics Pro Suite
Sample to Insight
Microbiome profiling in the Microbial Genomics Pro Suite
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Explore the taxonomic and functional composition of microbial communities. The microbiome as a biomarker Identify microbial community composition profiles that are indicative of: o patient health, o changing yields of agricultural crops or livestock, o emergence of public health threats.
Taxonomic profiling o 16S, 18S, ITS, amplicons o whole metagenome
Functional profiling
o Whole metagenome assembly o Gene finding & annotation o Antimicrobial resistance, virulence
Comparative analysis between samples (differential abundance analysis)
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Microbial Genomics Pro Suite
Webinar: Microbiome analysis from Sample to Insight
o The Microbial Genomics Pro Suite o Taxonomic microbiome profiling o Functional metagenomics o Q&A
QIAGEN Microbial Gx Pro Suite
Sample to Insight
OTU-Clustering and taxonomic microbiome analysis made easy
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16s rRNA gene
QIAGEN Microbial Gx Pro Suite
The 16s rRNA gene – a phylogenetic marker for bacterial ID
Sample to Insight
Microbial community analysis via 16S sequencing
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Main question: which microbes are around, and how abundant are they? 1. PCR amplify hyper-variable regions in 16S rRNA gene 2. NGS sequence the amplicon (V3, V4 paired end) 3. Assign taxonomy to reads and tally
Due to lack of taxonomical knowledge and sequencing errors: cluster reads at some level of similarity.
⇒ Operational Taxonomical Units (OTUs), reported in abundance tables
Secondary analyses: ⇒ Alpha diversity: how many species are there? Is sampling depth OK? ⇒ Beta diversity: how different are my samples? ⇒ Comparative analysis: statistical tests for differential abundance reveal
organisms that differ most in terms of their abundance across samples.
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The Crime: Sacking of iron age burial site
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DNA from soil on suspect 1 boots
DNA from soil alibi site 1
DNA from soil on suspect 2 boots
DNA from soil alibi site 2
DNA from soil at excavation site
QIAGEN Microbial Gx Pro Suite
Microbial Soil Community Analyses: A Forensics Case
Sample to Insight
Between Sample Diversity
Beta diversity measure Information Bray-Curtis More robust with heterogeneous data. Less
sensitive to outliers.
Jaccard More robust with heterogeneous data. Least sensitive to outliers. (count based, 1- intersection/union)
Euclidian Less robust with heterogeneous data. More sensitive to outliers (grows with the square of the distance).
Phylogenetic diversity measure Information Unweighted UniFrac Unweighted UniFrac distance gives
comparatively more importance to rare lineages.
Weighted UniFrac Weighted UniFrac distance gives more importance to abundant lineages.
Weighted UniFrac (not normalized)
Generalized UniFrac distance d(0)
Generalized UniFrac distance d(0.5) The generalized UniFrac distance offers a robust tradeoff between Unweighted and Weighted UniFrac.
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Beta Diversity Estimation
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Soil microbiome on suspect 1 boots Soil microbiome on suspect 2 boots
Soil microbiome at alibi site 1 Soil microbiome at excavation site
Soil microbiome at alibi site 2
Match between microbiomes obtained
from suspect 1 and excavation site.
The Crime: Sacking of iron age burial site
QIAGEN Microbial Gx Pro Suite
Microbial Soil Community Analyses: A Forensics Case
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Microbial Genomics Pro Suite
Webinar: Microbiome analysis from Sample to Insight
o The Microbial Genomics Pro Suite o Taxonomic microbiome profiling o Functional metagenomics o Q&A
QIAGEN Microbial Gx Pro Suite
Sample to Insight
Functional analysis of microbial communities
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Main questions: which functions are coded for in my sample (genes or high-level functional groups) and how abundant are they?
1. De novo assemble your input reads 2. Call coding sequences and assign function 3. Build functional abundance profile
⇒ Functional entities reported in abundance tables
Secondary analyses: ⇒ Alpha diversity: How many functional entities are there? Is sampling depth
OK? ⇒ Beta diversity: How different are my samples? How do they cluster? ⇒ Comparative analysis: Differential abundance analysis reveal functions that
differ significantly across samples or groups of samples.
Sample to Insight
Functional annotation of microbiomes
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De novo assemble microbiome
CDS annotate contigs using MetaGeneMark
Annotate CDSs with “best BLAST hits”
Build abundance profile
Annotate CDSs with Pfam domains and GO terms
Map reads back to contigs
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• Benchmarked 14 microbiome toolkits on synthetic data
• Built six datasets from two communities
• Dataset A was enriched for: • Cyanobacteria - Photosynthesis • Bradyrhizobium - Nitrogen fixation • Rhizobium - Nitrogen fixation
• Dataset B was enriched for a set of known pathogens
• Only five toolkits allowed analysis of the functional
content of microbiomes
• Only two reliably detected the known differences between dataset A and B
Lindgreen, Stinus, Karen L. Adair, and Paul P. Gardner. "An evaluation of the accuracy and speed of metagenome analysis tools." Scientific reports 6 (2016): 19233.
QIAGEN Microbial Gx Pro Suite
Benchmarks based on Lindgreen et al.
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Successful recall of functional content
Sample to Insight
De novo assemble microbiome
CDS annotate contigs using MetaGeneMark
Annotate CDSs with “best BLAST hits”
Build abundance profile
Annotate CDSs with Pfam domains and GO terms
Map reads back to contigs
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Functional annotation of microbiomes
Sample to Insight
Mock community dataset from: Shakya, Migun, et al. "Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities." Environmental microbiology 15.6 (2013): 1882-1899.
High accuracy in functional profiling is owed to superior metagenome de novo assembly
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High assembly quality is needed for good annotation
Sample to Insight
Mock community dataset from: Shakya, Migun, et al. "Comparative metagenomic and rRNA microbial diversity characterization using archaeal and bacterial synthetic communities." Environmental microbiology 15.6 (2013): 1882-1899.
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Running time and compute resource requirements
Sample to Insight
• Two healthy male volunteers (P1 and P2)
• Both were treated with ciprofloxacin for 6 days (500 mg orally twice daily).
• Stool samples from both participants were collected on:
• day 0 (before treatment),
• day 1, 3 and 6 of treatment,
• day 8 and 34 (2 and 28 days after treatment, respectively)
• In total 12 samples were sequenced to generate 820,599,346 reads (~82.9 Gbp)
Willmann, Matthias, et al. "Antibiotic selection pressure determination through sequence-based metagenomics." Antimicrobial agents and chemotherapy 59.12 (2015): 7335-7345.
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How does antibiotic treatment affect the gut microbiome?
Sample to Insight
Willmann, Matthias, et al. "Antibiotic selection pressure determination through sequence-based metagenomics." Antimicrobial agents and chemotherapy 59.12 (2015): 7335-7345.
• “Gut species diversity was profoundly disturbed over the course of Cp exposure in subject 1.”
• “The lowest species diversity occurred at the last treatment day and in the following two days…”
• “… but recovered almost fully 28 days after treatment.“
• “Subject 2 generally was much less affected…”
• “… and diversity remained almost the same in all samples... ”
• “At values comparable to those for subject 1 under treatment.”
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Willmann et al.: Some key findings
Sample to Insight
Willmann et al.: Some key findings
Willmann, Matthias, et al. "Antibiotic selection pressure determination through sequence-based metagenomics." Antimicrobial agents and chemotherapy 59.12 (2015): 7335-7345.
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• “Gut species diversity was profoundly disturbed over the course of Cp exposure in subject 1.”
• “The lowest species diversity occurred at the last treatment day and in the following two days…”
• “… but recovered almost fully 28 days after treatment.“
• “Subject 2 generally was much less affected…”
• “… and diversity remained almost the same in all samples... ”
• “At values comparable to those for subject 1 under treatment.”
Sample to Insight
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• Accurately detect and quantify functional elements in microbiome samples
• Confidently measure statistically significant changes in function between samples
• Group and analyze data in the context of your sample-metadata
• Shorter run time and better compute resource efficiency
• Publication-ready and interactive visualisations
Tutorials, trials, app. notes: https://www.qiagenbioinformatics.com
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Conclusions
Sample to Insight
Microbial Genomics Pro Suite
Webinar: Microbiome analysis from Sample to Insight
o The Microbial Genomics Pro Suite o Taxonomic microbiome profiling o Functional metagenomics o Q&A
QIAGEN Microbial Gx Pro Suite 27
Sample to Insight
www.qiagenbioinformatics.com
Microbial genomics
Email Support: Support-Clcbio@qiagen.com
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Sample to Insight
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Browsing the functional content of microbiomes
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Alpha diversity
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Beta diversity
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Differential abundance analysis and hierarchical clustering
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