Microbiome Profiling with the Microbial Genomics Pro Suite

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

1 QIAGEN Microbial Gx Pro Suite

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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Webinar: Microbiome analysis from Sample to Insight

o The Microbial Genomics Pro Suite o Taxonomic microbiome profiling o Functional metagenomics o Q&A


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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



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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)


Microbiome profiling in the Microbial Genomics Pro Suite

Sample to Insight

Microbiome profiling in the Microbial Genomics Pro Suite

QIAGEN Microbial Gx Pro Suite 7

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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OTU-Clustering and taxonomic microbiome analysis made easy

9 QIAGEN Microbial Gx Pro Suite

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16s rRNA gene


The 16s rRNA gene – a phylogenetic marker for bacterial ID

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Microbial community analysis via 16S sequencing


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.


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


Microbial Soil Community Analyses: A Forensics Case

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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.


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


Microbial Soil Community Analyses: A Forensics Case






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Functional analysis of microbial communities


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.

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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.


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

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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


High assembly quality is needed for good annotation

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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.


Running time and compute resource requirements

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• 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.


How does antibiotic treatment affect the gut microbiome?

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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.”


Willmann et al.: Some key findings

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Willmann et al.: Some key findings



• “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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• 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


Conclusions

https://www.qiagenbioinformatics.com

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www.qiagenbioinformatics.com

Microbial genomics

Email Support: [email protected]


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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


Date post:	17-Jan-2017
Category:	Health & Medicine
Upload:	qiagen
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Microbiome Profiling with the Microbial Genomics Pro Suite

Health & Medicine