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Shotmap meta center_2014
- 1. High-throughput Annotation of Metagenomes Reveals Community Physiological Variation in the Mammalian Microbiome Thomas J. Sharpton Oregon State University @tjsharpton
- 2. How do Microbial Communities Interact with their Hosts? • Microbiome structural changes often associate with changes in host physiology • Knowing the mechanisms of interaction between microbiome and host physiology may yield improved diagnostics or therapeutics • Metagenomic functional annotation can reveal how microbiome physiology scales with host physiology
- 3. Metagenome Annotation through Protein Family Classification
- 4. The Shotgun Metagenome Annotation Pipeline (shotmap)
- 5. Statistical Simulations Assess Accuracy and Properties of Annotation • Simulated mock communities and metagenomes • Predicted community physiology using Shotmap • Compared predictions to mock- community truth using Bray- Curtis distances (L1) Stephen Nayfach Pollard Lab Poster #02
- 6. Annotation Accuracy Varies as a Function of Classification Thresholds
- 7. Optimal Alignment Thresholds Vary as a Function of Sequence Length
- 8. Alpha-Diversity Estimation Accuracy Plateaus as Sequence Volume Increases
- 9. Community Structure Varies Across Inflammatory Bowel Diseased Patients Adapted from Qin et al Nature (2009)
- 10. Crohn’s Diseased Patient Microbiomes Exhibit Physiological Differentiation Adapted from Qin et al Nature (2009)
- 11. Physiological Alpha-Diversity is Lower in Crohn’s Diseased Patient Microbiomes R Richness Shannon Entropy * * Crohn’s Disease Healthy Ulcerative Colitis Crohn’s Disease Healthy Ulcerative Colitis * p < 0.05
- 12. Specific KOs Differentiate Crohn’s Diseased Microbiomes * * Crohn’s Disease Healthy Ulcerative Colitis Crohn’s Disease Healthy Ulcerative Colitis Abundance K05595 - multiple antibiotic resistance protein K09684 - purine catabolism regulatory protein * q < 0.2See Morgan et al. Genome Biology (2012)
- 13. IBD-Associated Longitudinal Variation in Microbiome Physiology • TGF-B-DNR mice Develop an IBD akin to Crohn’s disease • Generated Metagenomes Before and After Disease Onset
- 14. * Specific KOs Uniquely Stratify Post- Disease Onset Microbiomes Pre-Disease Post-DiseasePre-Disease Post-Disease WT WTDNR DNR K00559 - sterol 24-C-methyltransferaseK02399 - flagella synthesis protein FlgN * * q < 0.2 WT WTDNR DNR * RelativeAbundance
- 15. Conclusions • Shotmap is a new tool that can quantify physiological variation between metagenomes • Metagenome annotation accuracy depends on the parameters selected and data properties • Microbiome physiology varies in association with inflammatory bowel disease state
- 16. Acknowledgements Collaborators Stephen Nayfach (Gladstone), Poster 02 Katherine Pollard (Gladstone) Patrick Bradley (Gladstone) Tim Laurent (Solozyme) Stacia Wyman (Gladstone) Shomyseh Sanjabi (Gladstone) Jonathan Eisen (UC Davis) Shotmap is Open Source. Download here: www.github.com/sharpton/ Funding Gordon & Betty Moore Foundation NSF/NIGMS Joint Program in Mathematical Biology #DMS1069303 NIH R21 AI08953 Developers of open source software and providers of public data
- 19. Alpha-Diversity Estimation Accuracy Plateaus as Sequence Volume Increases
- 20. Beta-Diversity Diversity also Plateaus as Sequence Volume Increases
- 21. Metagenome Annotation • Classify metagenomic sequences into functionally annotated protein families • Several great tools currently exist • But, few provide extensive analytical control and force users to adopt settings that may not be appropriate for their analysis – Use specific databases or annotation paradigms • Additionally, many require the user to upload data to the cloud, which may yield infrastructural bottlenecks as data volume increases • We sought to develop a high-throughput workflow that provides users analytical flexibility, can be run on a multi-core machine in a laboratory, and handle the entire annotation process, from raw metagenomic data to statistical comparisons across samples
Editor's Notes
- It is less well-understood how microbiome physiology associates with such changes in the host. There are many methods by which these mechanisms can be elucidated. One that has been very powerful is: By evaluating how microbiome physiology scales across host physiology, we can develop testable hypotheses about potential mechanisms of interaction between microbiomes and their hosts
- Shotmap is: Analytically flexible and allows users to tune workflow parameters and conduct analyses appropriate to the data being investigated. For example, it is agnostic to the search database Extensible. The publically available software accommodates a variety of search algorithms, including blast and fastblast tools as well as profile-based alignment tools like HMMER Infrastructurally flexible. It can either be run on a multicore lab server or it can interface with a distributed computing environment. It can also optionally communicate with a relational database server to handle the management of results.
- I’m not showing the data here, but we see the same pattern with beta-diversity
- A virulence factor in several other disease systems, may trigger inflammation Purine catabolism part of a pathway found to be significantly elevated in healthy patients in morgan et al
- Mounting evidence indicates that flagellen may trigger immune activation via innate and adaptive immune systems. Elson lab shown that flagellated bacteria are implicated in Crohn’s disease Involved in Vitamin D biosynthesis; Vit D
- Note that there are other excellent tools available to researchers in the community Note Morgan et al study, which explored distinct patient population Note that many of the KOs found in mice are also found in human clinical study, indicating that there may be consistent physiological differences across species