Metagenomic data can be rich, but finding interesting patterns can be a challenge. What should we be looking for that's new and unexpected, and how do we find it?

1. Brainstorming the Downstream
Analysis of Metagenomic Data Using R,
Bayesian Statistics, or Other Methods
Mitch Fernandez
July 1st, 2014

2. Workflow
• Data Preparation
• Data Preprocessing
• Data Clustering
• Downstream Analysis

3. Alignment
http://genome.cshlp.org/content/17/2/127/F2.expansion.html

4. Preprocessing pt. 2

5. Pre.clustering
http://www.nature.com/nmeth/journal/v9/n5/full/nmeth.1990.html

6. Preprocessing pt. 3

7. Chimeras
http://genome.cshlp.org/content/21/3/494/F1.expansion.html

8. Preprocessing pt. 4

9. Contaminants
http://www.reids-workouts.com/the-reason-you-are-not-building-muscle-part-2; http://acceleratingscience.com/proteomics/proteomic-analysis-of-mitochondria-unravels-
the-pathophysiology-of-pre-eclampsia/; http://vickgaza.deviantart.com/art/Mountain-Yeti-360756260; http://www.funchap.com/pictures-of-dogs/;
http://en.wikipedia.org/wiki/Chloroplast

10. Operational Taxonomic Units

11. OTU Clustering
http://rosalind.info/glossary/distance-matrix/
https://peerj.com/articles/237/

12. Classifying OTUs

13. • Low Diversity
Richness and Diversity
• High Diversity
Equal Richness

14. Text Parsing
• Automate Oligos file creation
• Produce read count tables
• Parse richness and diversity results
• Parse error logs
• Split data into groups

15. Normalization

16. Visualization

17. Metastats
Former (n=24) Active (n=22)
Difference in Relative AbundanceOTU Reads Relative Abundance Variance Std. Error Reads Relative Abundance Variance Std. Error p-value q-value
Selenomonas_003 79 8.71E-04 1.00E-06 1.85E-04 216 2.03E-03 4.00E-06 4.01E-04 -1.16E-03 1.10E-02 2.33E-02
Porphyromonas_001 607 8.30E-03 8.50E-05 1.89E-03 331 3.96E-03 7.00E-06 5.68E-04 4.34E-03 3.10E-02 6.38E-02
Family_Burkholderiaceae_001 96 1.51E-03 5.00E-06 4.40E-04 67 5.04E-04 0.00E+00 1.48E-04 1.00E-03 3.50E-02 7.05E-02
Neisseria_001 4,851 4.58E-02 4.68E-03 1.40E-02 1,353 2.00E-02 2.55E-04 3.41E-03 2.57E-02 3.80E-02 7.38E-02
Campylobacter_001 121 1.10E-03 2.00E-06 2.76E-04 115 2.10E-03 4.00E-06 4.26E-04 -1.00E-03 5.29E-02 8.54E-02
Rhizobium_001 20 3.42E-04 0.00E+00 1.37E-04 76 7.55E-04 1.00E-06 1.74E-04 -4.13E-04 5.99E-02 9.21E-02
Class_Gammaproteobacteria_002 158 2.03E-03 6.00E-06 5.06E-04 454 4.29E-03 2.90E-05 1.14E-03 -2.26E-03 7.09E-02 9.93E-02
Catonella_001 274 2.13E-03 1.10E-05 6.81E-04 65 9.22E-04 1.00E-06 2.00E-04 1.21E-03 7.19E-02 1.00E-01
Family_Carnobacteriaceae_001 621 6.83E-03 4.30E-05 1.34E-03 283 4.31E-03 1.20E-05 7.30E-04 2.52E-03 1.01E-01 1.25E-01
Prevotella_001 3,064 3.58E-02 5.73E-04 4.89E-03 6,061 5.58E-02 2.50E-03 1.07E-02 -2.00E-02 1.06E-01 1.30E-01
Paracoccus_001 24 2.76E-04 0.00E+00 1.28E-04 138 1.44E-03 1.40E-05 8.07E-04 -1.17E-03 1.06E-01 1.30E-01
Actinomyces_001 903 7.32E-03 4.50E-05 1.37E-03 1,055 1.23E-02 1.76E-04 2.83E-03 -5.01E-03 1.07E-01 1.31E-01
Prevotella_005 46 6.95E-04 2.00E-06 2.86E-04 328 3.09E-03 6.80E-05 1.76E-03 -2.40E-03 1.10E-01 1.34E-01
Family_Rhodocyclaceae_001 14 1.92E-04 0.00E+00 1.36E-04 87 1.14E-03 8.00E-06 5.94E-04 -9.47E-04 1.25E-01 1.47E-01

18. Running the Workflow
1. Gather your data
2. Prepare an Oligos file
3. Zip everything up and copy to the “work” folder
4. Run mothur.sh
5. Come back in a few hours/days
6. Run ReadCountTable.py on the taxonomy output
7. Do additional downstream processing
8. Publish results

19. What we need help with
Data management
Post-hoc OTU naming
Improved scripting
Identifying new tools
Other stuff I haven’t thought of

20. Thanks