06.17.2014.a.short.tutorial.on.processing.16 s.metagenomic.sequences.using.mothur.python.and.r

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This document provides a tutorial on processing 16S metagenomic sequences using mothur, Python and R. It outlines the workflow including data preparation, preprocessing such as denoising and merging files, OTU clustering, classification and downstream analysis. Methods covered include using mothur for alignment, preprocessing, OTU picking and classification, and Python and R scripts for additional processing such as text parsing, normalization and visualization. The document concludes by summarizing the overall workflow and requesting help with further improving and automating the process.

Science

A Short Tutorial on Processing 16S
Metagenomic Sequences Using
mothur, Python, and R
Mitch Fernandez
June 17th, 2014

PCR
http://calculatedimages.blogspot.com/2013/06/need-to-teach-pcr.html

16S rRNA Gene
http://www.clinchem.org/content/55/5/856.full
http://rna.ucsc.edu/rnacenter/ribosome_images.html

Pyrosequencing
http://www.nature.com/nmeth/journal/v8/n7/fig_tab/nmeth.1634_F1.html
http://aem.asm.org/content/78/20/7480/F1.expansion.html

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

mothur
http://www.cbs.dtu.dk/researchgroups/metagenomics/metagenomics.php

Oligos
Map barcode to sample
Identify primer and adaptor

Shell Scripts
Loop
through the
files
Modify
mothur
template
file
Call mothur
and pass
modified
template
Clean up
output

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

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

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

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

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

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

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

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

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

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

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1. A Short Tutorial on Processing 16S Metagenomic Sequences Using mothur, Python, and R Mitch Fernandez June 17th, 2014

2. Metagenomics https://www.sciencenews.org/article/year-review-your-body-mostly-microbes

3. PCR http://calculatedimages.blogspot.com/2013/06/need-to-teach-pcr.html

4. 16S rRNA Gene http://www.clinchem.org/content/55/5/856.full http://rna.ucsc.edu/rnacenter/ribosome_images.html

5. Pyrosequencing http://www.nature.com/nmeth/journal/v8/n7/fig_tab/nmeth.1634_F1.html http://aem.asm.org/content/78/20/7480/F1.expansion.html

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

7. mothur http://www.cbs.dtu.dk/researchgroups/metagenomics/metagenomics.php

8. Oligos Map barcode to sample Identify primer and adaptor

9. Shell Scripts Loop through the files Modify mothur template file Call mothur and pass modified template Clean up output

10. Denoising

11. Merging Files

12. Preprocessing pt. 1

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

14. Preprocessing pt. 2

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

16. Preprocessing pt. 3

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

18. Preprocessing pt. 4

19. 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

20. Operational Taxonomic Units

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

22. Classifying OTUs

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

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

25. Normalization

26. Visualization

27. 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

28. 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

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

30. Thanks

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