1.
“Scientists often have a naïve
faith that if only they could
discover enough facts about a
problem, these facts would
somehow arrange themselves in
a compelling and true solution.”
Theodosius Dobzhansky

2.
Microbiome studies using 16S ribosomal DNA PCR: some cautionary tales.
Jenna Morgan Lang
postdoc
Jonathan Eisen’s Lab
UC Davis
email: jennomics@gmail.com
Twitter: @jennomics
websites: jennomics.com
seagrassmicrobiome.org
phylogenomics.wordpress.com

3.
16S ribosomal RNA
PCR surveys

4.
Metagenomics

5.
Typical laboratory workflow
• Extract DNA with MoBio PowerSoil Kit
• Amplify 16S rDNA with barcoded primers
• Pool samples and sequence on the MiSeq
– 15 million reads, 250bp PE
– 50-200(?) samples
– Sample drop out

6.
Typical bioinformatic workflow
• Demultiplex and QC sequence data
• Process using QIIME
• Stare at graphs and wait for a revelation

7.
inputs pre-processing under the hood analysis
Meta-data
Sequence
data
z
Sequence
pre-processing
Cluster
sequences
Build
OTU table
Build
phylogenetic
tree
Assign
taxonomy
Alpha
diversity
Beta
diversity
Hypothesis
testing
Data
visualization
Q
I
I
M
E

8.
You can do lots of things with a .biom table
produced by QIIME
• METAGENassist
• interactive web tool that will do lots of stats and make
pretty pictures
• PICRUSt (google: picrust metagenomes)
• infers functional potential based on your 16S data
• STAMP (google: stamp bioinformatics)
• flexible python tool (with a GUI) that will do statistical
analysis of taxonomic and functional profiles on the fly
• R (phyloseq package)
• If you are familiar with R, this will bridge the gap between
QIIME and Rstats
• Phinch
• Interactive web-based visualization tool

9.
METAGENassist
• Input is .biom table and “mapping file”
• can input matrix of taxonomy or
functional assignments
• many options for statistical analysis
• easily generate nice plots

10.
Some examples of METAGENassist output:

11.
PICRUSt
(Phylogenetic Investigation of Communities by Reconstruction of Unobserved States)
• .biom table input from QIIME
• normalize by copy number
• predict metagenome
• .biom table output (with functional
categories)
Zaneveld, J.R., Lozupone, C., Gordon, J.I. & Knight, R. Ribosomal RNA diversity predicts
genome diversity in gut bacteria and their relatives. Nucleic Acids Res. 38, 3869–3879
(2010)
Martiny, A.C., Treseder, K. & Pusch, G. Phylogenetic conservatism of functional traits in
microorganisms. ISME J. 7, 830–838 (2013)

12.
PICRUSt accuracy
across various
environmental
microbiomes

13.
PICRUSt can
produce results that
make sense!
Tributary
contaminated by
old sulfur mine
Sulfur Metabolism

14.
STAMP
• Input is .biom table and “mapping file”
• Can input matrix of taxonomy or
functional assignments
• powerful statistical options
• Can subsample data on the fly
• Generates OK plots

15.
Using STAMP to identify SEED subsystems which are differentially abundant between
Candidatus Accumulibacter phosphatis sequences obtained from a pair of enhanced
biological phosphorus removal (EBPR) sludge metagenomes(data originally described in
Parks and Beiko, 2010).

16.
phyloseq R package
• Create a phyloseq object
– .biom table
– “mapping file”
– phylogenetic tree
• google: phyloseq demo
• do stats and make plots that you can
prettify with ggplot2

17.
phinch.org
• Add metadata to biom table
• Upload to phinch

18.
Phinch allows you to manipulate and explore your data

19.
Lots of data
cannot compensate
for a poorly designed
experiment

20.
Bioinformatics
cannot save
a poorly designed
experiment

21.
Design your experiment.
replication
controls
biases

22.
Read number distribution for 60 samples on one MiSeq run
233 sequences

23.
Read number distribution for 95 samples on one MiSeq run
318 sequences

24.
Standardize collection, storage, and laboratory procedures
Figure 3. Predicted and observed frequencies of sequence reads from each organism.
Morgan JL, Darling AE, Eisen JA (2010) Metagenomic Sequencing of an In Vitro-Simulated Microbial Community. PLoS ONE 5(4):
e10209. doi:10.1371/journal.pone.0010209
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010209

25.
Beware the chimera

26.
The How:
The Why:
• too many cycles
• extension time too short
• close relatives in the mix
• less abundant taxa

27.
Include
kit / negative
controls

28.
16S rRNA gene sequencing of a pure Salmonella bongori culture

29.
16S rRNA gene sequencing of a pure Salmonella bongori culture

30.
Child nasopharyngeal samples from Thailand,
appears to show age-related clustering

31.
Child nasopharyngeal samples from Thailand,
extraction kit lot # explains the pattern better

32.
Child nasopharyngeal samples from Thailand,
loss of clustering after excluding contaminant OTUs

33.
Schloss
reducing
artifacts
Last Bit of Ugly Data
mock community consisting of 21 taxa
3 different regions amplified
4 different sequencing centers
Fecal sample

34.
“Perfection is the enemy of progress”

35.
WORDS OF WISDOM
Consult an expert.

36.
WORDS OF WISDOM
Include replicates and controls.
Design your experiment!

37.
WORDS OF WISDOM
Have a specific question.
Seek to answer THAT question.
(no pilots!)

38.
WORDS OF WISDOM
Do microbes differ between your
treatments?
Yes.

39.
WORDS OF WISDOM
Know the answer to the
question:
So now what?
(follow-up experiments)

40.
WORDS OF WISDOM
Avoid metagenomics.