Open-Source Analysis of My Raw American Gut Data

Just like I used open-source tools to re-analyze my 23andMe data, I wanted to see what I could learn from analyzing the raw 16S rRNA reads from the American Gut Project.  The American Gut team is very well organized, and you can currently access your raw data in public databases.

I've provided a tutorial based entirely on web-based analysis, so you don't need to know any programming to follow these steps in your own data.  You can also skip the tutorial links to just see my own results.

Step #1: Get Your Data
Step #2a: Analyze Your Data in MG-RAST (preferred, but time-consuming)
Step #2b: Analyze Your Data using RDP-Classifier (quick, but less functionality)

Here are some charts that I could quickly create in Excel using data from RDP-Classifier:

If you compare these distributions to the average gut distribution from the American Gut preliminary report, then you can tell it is very different than the average participant.  Clearly, I have more Proteobacteria than the average participant (and less Firmicutes and Bacteroidetes).  However, it is also important to note that there was also a large amount of variation between participants.

I don't know how my class distributions compare to other samples, but it seems like I can at least infer that there is more variety in my oral sample than my fecal sample:

I also found it useful to see what specific genera were most highly abundant.  According to RDP-Classifer, these are the genera with more than 1000 reads:

	Fecal	Oral
Streptococcaceae Streptococcus	0	10614
Neisseriaceae Neisseria	0	8824
Actinomycetaceae Actinomyces	0	4129
Lachnospiraceae Oribacterium	0	2319
Veillonellaceae Veillonella	0	2124
Bacteroidaceae Bacteroides	1800	5
Enterobacteriaceae Escherichia/Shigella	8039	5

I was able to find reports that Actinomyces was associated with transformation of lymphocytes in patients with periodontal disease (Baker et al. 1976) and Streptococcus mutans plays a role in human dental decay (Loesche 1986), although I don't know if I had this specific strain (based upon the MG-RAST report, it looks like I don't).  Likewise, I showed this list to my dentist, and she recognized these two genera as being associated with dental problems.  Although I don't know how common these are overall, it seems to make sense that they could be found in an oral sample.

Obviously, I recognized Escherichia/Shigella.  The American Gut report points out that the phylum containing this genus is not highly abundant in an average participant.  At one point, I had to be hospitalized with ulcerative colitis (with a strain of E. coli producing shiga toxin), so perhaps this is related to the high abundance of this genus (although that was several years ago).

If you are patient enough to wait for your MG-RAST results, then you can make similar (but slightly cooler looking) pie charts and tables automatically.  For example, you can take a look at the corresponding plots from MG-RAST for my fecal and oral samples.  You can also create plots to compare species in multiple samples (red bars are for my oral samples, green bars are for my fecal sample):

Perhaps most importantly, MG-RAST will provide annotations down to the species level (and strain level, when possible).  The species counts aren't perfectly correlated with the genera counts (predicted from the classifier), but the the most interesting genera appeared in both lists.

metagenome	strain	abundance
oral	uncultured bacterium	19715
fecal	Escherichia coli ED1a	11316
oral	Abiotrophia para-adiacens	6185
oral	Actinomyces odontolyticus	4154
oral	Veillonella dispar	1913
oral	Butyrivibrio fibrisolvens	1431
oral	Blautia sp. Ser8	1075
fecal	Bacteroides stercoris	736
oral	Syntrophococcus sucromutans	661
fecal	Pseudomonas fluorescens	660
fecal	Bacteroides caccae	559
fecal	Bacteroides stercoris ATCC 43183	558
fecal	Bacteroides vulgatus	510
oral	Streptococcus	499
oral	Rothia mucilaginosa	450
fecal	uncultured bacterium	370
oral	Haemophilus haemolyticus	295
fecal	Prevotella buccalis	287
oral	Ruminococcus gauvreauii	282
oral	Streptococcus sanguinis	275
oral	Ruminococcus torques L2-14	272
fecal	Escherichia coli	232
oral	Abiotrophia defectiva	213
oral	Gemella morbillorum	204
fecal	Dialister propionicifaciens	184
oral	Veillonella parvula	175
oral	Butyrivibrio hungatei	142
oral	Atopobium minutum	137
oral	Parvimonas micra	126
oral	Leptotrichia shahii	121

This information can allowed to conduct more effective literature searches.  For example, my understanding is that the ED1a strain has not been shown to be associated with ulcerative colitis.  On the other hand, the species information allowed me to find a paper for the discovery of my specific strain of Actinomyces, which was harvested from 450 tooth cavities (Batty 2005).  Likewise, I could confirm that Streptococcus sanguinis was also pathogenic (Xu et al. 2007).

FYI, Galaxy also has some metagenomic tools.  However, running BLAST on Galaxy will take a long time.  If you are comfortable with running BLAST locally, it should be easier (but this requires some comfort using the computer).  You can also analyze your data locally using QIIME upload the results to PICRUSt for functional enrichment, if you don't need the convenience of the web-based tools listed above (MG-RAST can produce QIIME reports, but I think it is better to use a tab-delimited text file to avoid formatting problems).

I am still interested in seeing what my official individual report will look like: although I have general experience with bioinformatics analysis, the folks at American Gut have looked at a lot more metagenomic data than I have.  Likewise, I am interesting in seeing how my profiles change at different time points: once I eventually get my uBiome results, I will put together another post to compare the results.