MiSeq SOP
NOTE: Although this is an SOP, it is something of a work in progress and continues to be modified as we learn more. If you are using this protocol in a paper, you must cite the Schloss et al. 2013 AEM paper and cite the date you accessed this page:
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. (2013): Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Applied and Environmental Microbiology. 79(17):5112-20.
The goal of this tutorial is to demonstrate the standard operating procedure (SOP) that the Schloss lab uses to process their 16S rRNA gene sequences that are generated using Illumina’s MiSeq platform using paired end reads. This SOP has been updated to reflect the features found in mothur v. 1.48.0. The approach we take is to use index reads to multiplex a large number of samples (i.e. 384) on a single run. You can also see our latest wet-lab SOP for generating these libraries. Others have generated similar data but without the index reads and so the index (aka barcode) sequences are found at the beginning of each read. This SOP will highlight the differences in processing between these two approaches. This SOP is largely the product of a series of manuscripts that we have published and users are advised to consult these for more details and background data.
Logistics
Starting out we need to first determine, what is our question? The Schloss lab is interested in understanding the effect of normal variation in the gut microbiome on host health. To that end we collected fresh feces from mice on a daily basis for 365 days post weaning (we’re accepting applications). During the first 150 days post weaning (dpw), nothing was done to our mice except allow them to eat, get fat, and be merry. We were curious whether the rapid change in weight observed during the first 10 dpw affected the stability microbiome compared to the microbiome observed between days 140 and 150. We will address this question in this tutorial using a combination of operational taxonomic units (OTUs), amplicon/exact sequence variants (ASV/ESV), phylotype, and phylogenetic methods. To make this tutorial easier to execute, we are providing only part of the data - you are given the flow files for one animal at 10 time points (5 early and 5 late). In addition, to sequencing samples from mice fecal material, we resequenced a mock community composed of genomic DNA from 21 bacterial strains. We will use the 10 fecal samples to look at how to analyze microbial communities and the mock community to measure the error rate and its effect on other analyses.
In a manuscript submitted to Applied & Environmental Microbiology, we
describe a set of primers that will allow you to sequence 1536 samples
in parallel using only 80 primers (32+48) and obtain sequence reads that
are at least as good as those generated by 454 sequencing using our 454
SOP. Please consult the supplementary methods of
that manuscript for more information and our wet-lab SOP. All of the
data from that study are available through our server. Sequences come
off the MiSeq as pairs of fastq files with each pair representing the
two sets of reads per sample. fastq files contain both the sequence data
and the quality score data. If you aren’t getting these files off the
sequencer, then you likely have the software parameters set incorrectly.
For this tutorial you will need mothur and several sets of files:
- Latest version of mothur. You will most likely not need the version with “tools” in the name.
- Example data from schloss lab that will be used with this tutorial. It was extracted from the full dataset
- SILVA-based bacterial reference alignment
- mothur-formatted version of the RDP training set (v.9)
You can easily substitute these choices (and should) for the reference
and taxonomy alignments using the updated Silva reference
files, RDP reference
files, Greengenes-formatted
databases, and Greengenes2-formatted
databases. We use the above
files because they’re compact and do a pretty good job. The various
classification references perform differently with different sample
types so your mileage may vary. It is generally easiest to decompress
these files and to then move the contents of the Trainset9_032012.pds
and the silva.bacteria folders into the MiSeq_SOP folder. You will also
want to move the contents of the mothur executable folder there as well.
If you are a sysadmin wiz (or novice) you can probably figure out how to
put mothur in your path, but this will get you what you need for now.
If you are working on a Mac OS X computer, the first time you run mothur you will get a scary looking message that says, ““mothur” cannot be opened because it is from an unidentified developer. macOS cannot verify that this app is free from malware.” Click the OK button. To permanently get rid of this message right click on the mothur icon, highlight “Open with…”, and while holding the command key select “Terminal (open)”. Now you’ll get a message that says, “macOS cannot verify the developer of “mothur”. Are you sure you want to open it? macOS cannot verify the developer of “mothur”. Are you sure you want to open it?” Click “Open”. Repeat this with the vsearch app.
You will probably want to get your hands on the following...
- Visual Studio Code or some other text editor
- R, Excel, or another program to graph data
Getting started
Because of the large size of the original dataset (3.9 GB) we are giving you 20 of the 362 pairs of fastq files. For example, you will see two files: F3D0_S188_L001_R1_001.fastq and F3D0_S188_L001_R2_001.fastq. These two files correspond to Female 3 on Day 0 (i.e. the day of weaning). The first and all those with R1 correspond to read 1 while the second and all those with R2 correspond to the second or reverse read. These sequences are 250 bp and overlap in the V4 region of the 16S rRNA gene; this region is about 253 bp long. So looking at the files in the MiSeq_SOP folder that you’ve downloaded you will see 40 fastq files representing 10 time points from Female 3 and 1 mock community. You will also see HMP_MOCK.v35.fasta which contains the sequences used in the mock community that we sequenced in fasta format. There are other files in this directory that we’ll get to later in this tutorial
To start mothur, the most reliable approach is to navigate to the directory that has your data, reference files, and the mothur executable using the command line (e.g. using the cd [mac or linux] or dir [windows] commands). To start mothur from the command line when mothur is in the same directory as the data, you can enter ./mothur [mac or linux] or mothur.exe [windows]. If it is elsewhere, then you’ll need to include the path to the executable. If it is in your PATH then you can simply type mothur or mothur.exe depending on your operating system. Alternatively, you can double click on the mothur icon. If you take the double clicking approach, it is easiest to do this when mothur is in the same directory as your data.
The first thing we need to do is tell mothur which fastq files go together. We can do this with the make.file command. This command will use the text before the first _ of the fastq file names as the name of the sample. For this reason, it is best not to include - characters in your sample names (e.g. don’t do “F3-D0”, but do “F3D0”).
If you started mothur directly from the command line, you can create the files file like this:
mothur > make.file(inputdir=., type=fastq, prefix=stability)
Alternatively, if you double clicked on the mothur icon from the same directory as your data, you can use the keyword ‘mothurhome’ to indicate the location of mothur’s executable.
mothur > make.file(inputdir=mothurhome, type=fastq, prefix=stability)
This will create a file called stability.files. The first lines of this file look like:
F3D0 F3D0_S188_L001_R1_001.fastq F3D0_S188_L001_R2_001.fastq
F3D141 F3D141_S207_L001_R1_001.fastq F3D141_S207_L001_R2_001.fastq
F3D142 F3D142_S208_L001_R1_001.fastq F3D142_S208_L001_R2_001.fastq
F3D143 F3D143_S209_L001_R1_001.fastq F3D143_S209_L001_R2_001.fastq
F3D144 F3D144_S210_L001_R1_001.fastq F3D144_S210_L001_R2_001.fastq
...
The first column is the name of the sample. The second column is the
name of the forward read for that sample and the third columns in the
name of the reverse read for that sample. If your fastq files are compressed as gz files then the value you should give to the type argument will be gz.
Reducing sequencing and PCR errors
The first thing we want to do is combine our two sets of reads for each
sample and then to combine the data from all of the samples. This is
done using the make.contigs command, which
requires stability.files as input. This command will extract the
sequence and quality score data from your fastq files, create the
reverse complement of the reverse read and then join the reads into
contigs. We have a very simple algorithm to do this. First, we align the
pairs of sequences. Next, we look across the alignment and identify any
positions where the two reads disagree. If one sequence has a base and
the other has a gap, the quality score of the base must be over 25 to be
considered real. If both sequences have a base at that position, then we
require one of the bases to have a quality score 6 or more points better
than the other. If it is less than 6 points better, then we set the
consensus base to an N. Let’s give it a shot...
mothur > make.contigs(file=stability.files)
The first thing you’ll see is that mothur can detect the number of processors
you have on your computer. If you want to use a different number of processors
than what mothur selects, you can use the command set.current(processors=8) to
use 8 processors. As you’ll see, make.contigs processes the fastq files to
generate the individual fasta and qual files. Then it will go through
each set of files and make the contigs. This took about 19 seconds on my
computer. Clearly, it will take longer for a full dataset. In the end it
will tell you the number of sequences in each sample:
Group count:
F3D0 7793
F3D1 5869
F3D141 5958
F3D142 3183
F3D143 3178
F3D144 4827
F3D145 7377
F3D146 5021
F3D147 17070
F3D148 12405
F3D149 13083
F3D150 5509
F3D2 19620
F3D3 6758
F3D5 4448
F3D6 7989
F3D7 5129
F3D8 5294
F3D9 7070
Mock 4779
Total of all groups is 152360
This
command will also produce several files that you will need down the
road: stability.trim.contigs.fasta and stability.contigs.count_table. These
contain the sequence data and group identity for each sequence. The
stability.contigs.report file will tell you something about the contig
assembly for each read. Let’s see what these sequences look like using
the summary.seqs command:
mothur > summary.seqs(fasta=stability.trim.contigs.fasta, count=stability.contigs.count_table)
Start End NBases Ambigs Polymer NumSeqs
Minimum: 1 248 248 0 3 1
2.5%-tile: 1 252 252 0 3 3810
25%-tile: 1 252 252 0 4 38091
Median: 1 252 252 0 4 76181
75%-tile: 1 253 253 0 5 114271
97.5%-tile: 1 253 253 6 6 148552
Maximum: 1 502 502 249 243 152360
Mean: 1 252 252 0 4
# of unique seqs: 152360
total # of seqs: 152360
This tells us that we have 152360 sequences that for the most part vary
between 248 and 253 bases. Interestingly, the longest read in the
dataset is 502 bp. Be suspicious of this. Recall that the reads are
supposed to be 251 bp each. This read clearly didn’t assemble well (or
at all). Also, note that at least 2.5% of our sequences had some
ambiguous base calls. Finally, when we’ve previously looked at V4 sequence data we rarely/never see good sequences with a stretch where the same nucleotide is repeated more than 8 times. We could have used the maxambig, maxlength, and maxhomop options
in make.contigs to resolve these issue while assembling the reads. But since we
already ran make.contigs, let’s include those values in screen.seqs command instead.
mothur > screen.seqs(fasta=stability.trim.contigs.fasta, count=stability.contigs.count_table, maxambig=0, maxlength=275, maxhomop=8)
This implementation of the command will remove any sequences with
ambiguous bases and anything longer than 275 bp. Also, mothur is smart enough to remember the correct number of processors, so it will use that throughout your
current session unless we change it. To see what else mothur knows about you, run the
following:
mothur > get.current()
Current RAM usage: 0.410511 Gigabytes. Total Ram: 32 Gigabytes.
Current files saved by mothur:
fasta=stability.trim.contigs.fasta
contigsreport=stability.contigs_report
count=stability.contigs.count_table
processors=16
summary=stability.trim.contigs.summary
What this means is that mothur remembers your latest fasta file and
count file as well as the number of processors you have. So you could
run:
mothur > summary.seqs(fasta=stability.trim.contigs.good.fasta, count=stability.contigs.good.count_table)
mothur > summary.seqs(fasta=current, count=current)
mothur > summary.seqs(count=current)
and you would get the same output for each.
Start End NBases Ambigs Polymer NumSeqs
Minimum: 1 250 250 0 3 1
2.5%-tile: 1 252 252 0 3 3222
25%-tile: 1 252 252 0 4 32217
Median: 1 252 252 0 4 64433
75%-tile: 1 253 253 0 5 96649
97.5%-tile: 1 253 253 0 6 125644
Maximum: 1 270 270 0 8 128865
Mean: 1 252 252 0 4
# of unique seqs: 128865
total # of seqs: 128865
It is generally easiest to use the “current” option for many of the
commands since the file names get very long. Because this tutorial is
meant to show people how to use mothur at a very nuts and bolts level,
we will only selectively use the current option to demonstrate how it
works. Generally, we will use the full file names for this tutorial. At this
point our sequencing error rate has probably dropped more than an order of
magnitude and we have 128865 sequences. Let’s press on...
Processing improved sequences
We anticipate that many of our sequences are duplicates of each other. Because it’s computationally wasteful to align the same thing a bazillion times, we’ll unique our sequences using the unique.seqs command:
mothur > unique.seqs(fasta=stability.trim.contigs.good.fasta, count=stability.contigs.good.count_table)
If two sequences have the same identical sequence, then they’re considered duplicates and will get merged. In the screen output there are two columns - the first is the number of sequences characterized and the second is the number of unique sequences remaining. So after running unique.seqs we have gone from 128872 to 16426 sequences. This will make our life much easier.
mothur > summary.seqs(count=stability.trim.contigs.good.count_table)
Using stability.trim.contigs.good.unique.fasta as input file for the fasta parameter.
Using 16 processors.
Start End NBases Ambigs Polymer NumSeqs
Minimum: 1 250 250 0 3 1
2.5%-tile: 1 252 252 0 3 3222
25%-tile: 1 252 252 0 4 32217
Median: 1 252 252 0 4 64433
75%-tile: 1 253 253 0 5 96649
97.5%-tile: 1 253 253 0 6 125644
Maximum: 1 270 270 0 8 128865
Mean: 1 252 252 0 4
# of unique seqs: 16421
total # of seqs: 128865
Cool, right? Now we need to align our sequences to the reference alignment. Again we can make our lives a bit easier by making a database customized to our region of interest using the pcr.seqs command. To run this command you need to have the reference database (silva.bacteria.fasta) and know where in that alignment your sequences start and end. To remove the leading and trailing dots we will set keepdots to false. You could also run this command using your primers of interest.
mothur > pcr.seqs(fasta=silva.bacteria.fasta, start=11895, end=25318, keepdots=F)
Those coordinates include the primers. The coordinates without the primers are 13862 and 23444. If you sequenced a region other than the V4 region, you should consult this blog post to see how to customize the coordinates for your region.
Let’s rename it to something more useful using the rename.file command:
mothur > rename.file(input=silva.bacteria.pcr.fasta, new=silva.v4.fasta)
Let’s take a look at what we’ve made:
mothur > summary.seqs(fasta=silva.v4.fasta)
Start End NBases Ambigs Polymer NumSeqs
Minimum: 1 13424 269 0 3 1
2.5%-tile: 1 13424 291 0 4 374
25%-tile: 1 13424 292 0 4 3740
Median: 1 13424 292 0 4 7479
75%-tile: 1 13424 292 0 5 11218
97.5%-tile: 1 13424 293 1 6 14583
Maximum: 3 13424 350 5 9 14956
Mean: 1 13424 291 0 4
# of Seqs: 14956
Now we have a customized reference alignment to align our sequences to. The nice thing about this reference is that instead of being 50,000 columns wide, it is now 13,425 columns wide which will save our hard drive some space and should improve the overall alignment quality. We’ll do the alignment with align.seqs:
mothur > align.seqs(fasta=stability.trim.contigs.good.unique.fasta, reference=silva.v4.fasta)
This should be done in a manner of seconds and we can run summary.seqs again:
mothur > summary.seqs(fasta=stability.trim.contigs.good.unique.align, count=stability.trim.contigs.good.count_table)
Start End NBases Ambigs Polymer NumSeqs
Minimum: 1250 10693 250 0 3 1
2.5%-tile: 1968 11550 252 0 3 3222
25%-tile: 1968 11550 252 0 4 32217
Median: 1968 11550 252 0 4 64433
75%-tile: 1968 11550 253 0 5 96649
97.5%-tile: 1968 11550 253 0 6 125644
Maximum: 1982 13400 270 0 8 128865
Mean: 1967 11550 252 0 4
# of unique seqs: 16421
total # of seqs: 128865
So what does this mean? You’ll see that the bulk of the sequences start at position 1968 and end at position 11550. Some sequences start at position 1250 or 1968 and end at 10693 or 13400. These deviants from the mode positions are likely due to an insertion or deletion at the terminal ends of the alignments. Sometimes you’ll see sequences that start and end at the same position indicating a very poor alignment, which is generally due to non-specific amplification. To make sure that everything overlaps the same region we’ll re-run screen.seqs to get sequences that start at or before position 1968 and end at or after position 11550. Note that we need the count table so that we can update the table for the sequences we’re removing:
mothur > screen.seqs(fasta=stability.trim.contigs.good.unique.align, count=stability.trim.contigs.good.count_table, start=1968, end=11550)
mothur > summary.seqs(fasta=current, count=current)
Start End NBases Ambigs Polymer NumSeqs
Minimum: 1965 11550 250 0 3 1
2.5%-tile: 1968 11550 252 0 3 3217
25%-tile: 1968 11550 252 0 4 32165
Median: 1968 11550 252 0 4 64329
75%-tile: 1968 11550 253 0 5 96493
97.5%-tile: 1968 11550 253 0 6 125440
Maximum: 1968 13400 270 0 8 128656
Mean: 1967 11550 252 0 4
# of unique seqs: 16299
total # of seqs: 128656
Now we know our sequences overlap the same alignment coordinates, we want to make sure they only overlap that region. So we’ll filter the sequences to remove the overhangs at both ends. Since we’ve done paired-end sequencing, this shouldn’t be much of an issue, but whatever. In addition, there are many columns in the alignment that only contain gap characters (i.e. “-“). These can be pulled out without losing any information. We’ll do all this with filter.seqs:
mothur > filter.seqs(fasta=stability.trim.contigs.good.unique.good.align, vertical=T, trump=.)
At the end of running the command we get the following information:
Length of filtered alignment: 376
Number of columns removed: 13048
Length of the original alignment: 13424
Number of sequences used to construct filter: 16299
This means that our initial alignment was 13424 columns wide and that we were able to remove 13048 terminal gap characters using trump=. and vertical gap characters using vertical=T. The final alignment length is 376 columns. Because we’ve perhaps created some redundancy across our sequences by trimming the ends, we can re-run unique.seqs:
mothur > unique.seqs(fasta=stability.trim.contigs.good.unique.good.filter.fasta, count=stability.trim.contigs.good.good.count_table)
This identified 3 duplicate sequences that we’ve now merged with previous unique sequences. The next thing we want to do to further de-noise our sequences is to pre-cluster the sequences using the pre.cluster command allowing for up to 2 differences between sequences. This command will split the sequences by group and then sort them by abundance and go from most abundant to least and identify sequences that are within 2 nt of each other. If they are then they get merged. We generally favor allowing 1 difference for every 100 bp of sequence:
mothur > pre.cluster(fasta=stability.trim.contigs.good.unique.good.filter.unique.fasta, count=stability.trim.contigs.good.unique.good.filter.count_table, diffs=2)
We now have 6090 unique sequences. At this point we have removed as much
sequencing error as we can and it is time to turn our attention to
removing chimeras. We’ll do this using the VSEARCH algorithm that is
called within mothur using the
chimera.vsearch command. Again, this
command will split the data by sample and check for chimeras. Our
preferred way of doing this is to use the abundant sequences as our
reference. In addition, if a sequence is flagged as chimeric in one
sample, the default (dereplicate=F) is to remove it from all samples.
Our experience suggests that this is a bit aggressive since we’ve seen
rare sequences get flagged as chimeric when they’re the most abundant
sequence in another sample. This is how we do it:
mothur > chimera.vsearch(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.count_table, dereplicate=t)
When running mothur’s chimera commands, mothur will automatically remove the chimeras from your fasta and count files. If you want to do this in two steps - detecting chimeras and removing chimeras, please see the documentation for chimera.vsearch. Running summary.seqs we see what we’re left with:
mothur > summary.seqs(fasta=current, count=current)
Using 16 processors.
Start End NBases Ambigs Polymer NumSeqs
Minimum: 1 376 249 0 3 1
2.5%-tile: 1 376 252 0 3 2955
25%-tile: 1 376 252 0 4 29545
Median: 1 376 252 0 4 59089
75%-tile: 1 376 253 0 5 88633
97.5%-tile: 1 376 253 0 6 115222
Maximum: 1 376 256 0 8 118176
Mean: 1 376 252 0 4
# of unique seqs: 2491
total # of seqs: 118176
Note that we went from 128,656 to 118,176 sequences for a reduction of 8.1%; this is a reasonable number of sequences to be flagged as chimeric. As a final quality control step, we need to see if there are any “undesirables” in our dataset. Sometimes when we pick a primer set they will amplify other stuff that gets to this point in the pipeline such as 18S rRNA gene fragments or 16S rRNA from Archaea, chloroplasts, and mitochondria. There’s also just the random stuff that we want to get rid of. Now you may say, “But wait I want that stuff”. Fine. But, the primers we use, are only supposed to amplify members of the Bacteria and if they’re hitting Eukaryota or Archaea, then its a mistake. Also, realize that chloroplasts and mitochondria have no functional role in a microbial community. But I digress. Let’s go ahead and classify those sequences using the Bayesian classifier with the classify.seqs command:
mothur > classify.seqs(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.count_table, reference=trainset9_032012.pds.fasta, taxonomy=trainset9_032012.pds.tax)
Now that everything is classified we want to remove our undesirables. We do this with the remove.lineage command:
mothur > remove.lineage(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.count_table, taxonomy=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pds.wang.taxonomy, taxon=Chloroplast-Mitochondria-unknown-Archaea-Eukaryota)
Note is that “unknown” only pops up as a classification if the classifier cannot classify your sequence to one of the domains. Also, keep in mind that if you aren’t classifying your sequences using the RDP reference taxonomy, you’ll need to customize what the lineages are called. For example, our modified version of the RDP calls mitochondria, “Mitochondria”. If you use greengenes, it calls them “f__mitochondria”. If you are using greengenes (or SILVA or anything else), you’ll need to change these names as appropriate.
If you run summary.seqs you’ll see that we now have 2466 unique sequences and a total of 118008 total sequences. This means that 162 of our sequences were in these taxonomic groups. Now, to create an updated taxonomy summary file that reflects these removals we use the summary.tax command:
mothur > summary.tax(taxonomy=current, count=current)
This creates a pick.tax.summary file with the undesirables removed. At this point we have curated our data as far as possible and we’re ready to see what our error rate is.
Assessing error rates
Measuring the error rate of your sequences is something you can only do if you have co-sequenced a mock community. This is something we include for every 95 samples we sequence. You should too because it will help you gauge your error rates and allow you to see how well your curation is going and whether something is wrong with your sequencing set up. First we want to pull the sequences out that were from our “Mock” sample using the get.groups command:
Note: If you are running this analysis on a Windows machine, the Mock
group name is likely capitalized due to how the make.file command
creates the group names. You will want to set groups=MOCK.
mothur > get.groups(count=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.count_table, fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.fasta, groups=Mock)
Selected 64 sequences from your fasta file.
Selected 4048 sequences from your count file.
This tells us that we had 64 unique sequences and a total of 4048 total sequences in our Mock sample. We can now use the seq.error command to measure the error rates:
mothur > seq.error(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.fasta, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.count_table, reference=HMP_MOCK.v35.fasta, aligned=F)
Multiply error rate by 100 to obtain the percent sequencing errors.Overall error rate: 6.5108e-05
Errors Sequences
0 3998
1 3
2 0
3 2
4 1
5 0
6 0
7 0
8 0
9 0
10 0
11 2
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 1
That rocks, eh? Our error rate is 0.0065%. This is the value that I would prefer to report. Some like to get excited about the number of spurious OTUs that are generated. To figure this out, we can now cluster the sequences into OTUs to see how many spurious OTUs we have:
mothur > dist.seqs(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.fasta, cutoff=0.03)
mothur > cluster(column=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.dist, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.count_table)
mothur > make.shared(list=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.opti_mcc.list, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.count_table, label=0.03)
mothur > rarefaction.single(shared=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.opti_mcc.shared)
This string of commands will produce a file for you called stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.opti_mcc.groups.rarefaction. Open it. You’ll see that for 4048 sequences, we’d have 35 OTUs from the Mock community. This number of course includes some stealthy chimeras that escaped our detection methods. If we used 3000 sequences, we would have about 31 OTUs. In a perfect world with no chimeras and no sequencing errors, we’d have 20 OTUs. This is not a perfect world. But this is pretty darn good! The reason I prefer the rate (i.e., 0.0065%) over the number of spurious OTUs is that the number of spurious OTUs is dependent on the sequencing depth while the rate is not.
Preparing for analysis
We’re almost to the point where you can have some fun with your data (I’m already having fun, aren’t you?). We’d like to do two things- assign sequences to OTUs, ASVs, and phylotypes. First, we want to remove the Mock sample from our dataset using the remove.groups command:
mothur > remove.groups(count=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.count_table, fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.fasta, taxonomy=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pds.wang.pick.taxonomy, groups=Mock)
mothur > rename.file(fasta=current, count=current, taxonomy=current, prefix=final)
Current files saved by mothur:
accnos=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pds.wang.accnos
column=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.dist
fasta=final.fasta
list=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.opti_mcc.list
shared=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.pick.pick.opti_mcc.shared
taxonomy=final.taxonomy
contigsreport=stability.contigs_report
count=final.count_table
processors=10
summary=stability.trim.contigs.good.unique.good.filter.unique.precluster.denovo.vsearch.summary
OTUs
Now we have a couple of options for clustering sequences into OTUs. For a small dataset like this, we can do the traditional approach using dist.seqs and cluster:
mothur > dist.seqs(fasta=final.fasta, cutoff=0.03)
mothur > cluster(column=final.dist, count=final.count_table)
You did not set a cutoff, using 0.03.
Clustering final.dist
iter time label num_otus cutoff tp tn fp fn sensitivity specificity ppv npv fdr accuracy mcc f1score
0.03
0 0 0.03 2430 0.03 0 2.9251e+06 0 26136 0 1 00.991144 1 0.991144 0 0
1 0 0.03 524 0.03 22146 2.92339e+06 1706 3990 0.847337 0.999417 0.928476 0.998637 0.928476 0.99807 0.886023 0.886053
2 0 0.03 490 0.03 22532 2.92328e+06 1817 3604 0.862106 0.999379 0.925377 0.998769 0.925377 0.998163 0.892265 0.892622
3 0 0.03 488 0.03 22541 2.92329e+06 1813 3595 0.86245 0.99938 0.925556 0.998772 0.925556 0.998168 0.892532 0.89289
4 0 0.03 488 0.03 22541 2.92329e+06 1812 3595 0.86245 0.999381 0.925594 0.998772 0.925594 0.998168 0.892551 0.892907
The alternative is to use our cluster.split command. In this approach, we use the taxonomic information to split the sequences into bins and then cluster within each bin. In our testing, the MCC values when splitting the datasets at the class and genus levels were within 98.0 and 93.0%, respectively, of the MCC values obtained from the entire test dataset. These decreases in MCC value resulted in the formation of as many as 4.7 and 22.5% more OTUs, respectively, than were observed from the entire dataset. The use of the cluster splitting heuristic was probably not worth the loss in clustering quality. However, as datasets become larger, it may be necessary to use the heuristic to clustering the data into OTUs. The advantage of the cluster.split approach is that it should be faster, use less memory, and can be run on multiple processors. In an ideal world we would prefer the traditional route because “Trad is rad”, but we also think that kind of humor is funny.… In this command we use taxlevel=4, which corresponds to the level of Order.
mothur > cluster.split(fasta=final.fasta, count=final.count_table, taxonomy=final.taxonomy, taxlevel=4, cutoff=0.03)
label cutoff numotus tp tn fp fn sensitivity specificity ppv npv fdr accuracy mcc f1score
0.03 0.03 534 22285 2.92357e+06 1853 3523 0.8635 0.9994 0.9232 0.99880.9232 0.9982 0.892 0.8924
Next we want to know how many sequences are in each OTU from each group
and we can do this using the make.shared
command. Here we tell mothur that we’re really only interested in the
0.03 cutoff level:
mothur > make.shared(list=final.opti_mcc.list, count=final.count_table, label=0.03)
We probably also want to know the taxonomy for each of our OTUs. We can get the consensus taxonomy for each OTU using the classify.otu command:
mothur > classify.otu(list=final.opti_mcc.list, count=final.count_table, taxonomy=final.taxonomy, label=0.03)
Opening final.opti_mcc.0.03.cons.taxonomy you’ll see something that looks like...
OTU Size Taxonomy
Otu001 12288 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Porphyromonadaceae"(100);"Porphyromonadaceae"_unclassified(100);
Otu002 8892 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Porphyromonadaceae"(100);"Porphyromonadaceae"_unclassified(100);
Otu003 7794 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Porphyromonadaceae"(100);"Porphyromonadaceae"_unclassified(100);
Otu004 7476 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Porphyromonadaceae"(100);Barnesiella(100);
Otu005 7450 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Porphyromonadaceae"(100);"Porphyromonadaceae"_unclassified(100);
Otu006 6621 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Porphyromonadaceae"(100);"Porphyromonadaceae"_unclassified(100);
Otu007 6304 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);Bacteroidaceae(100);Bacteroides(100);
Otu008 5337 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Rikenellaceae"(100);Alistipes(100);
Otu009 3606 Bacteria(100);"Bacteroidetes"(100);"Bacteroidia"(100);"Bacteroidales"(100);"Porphyromonadaceae"(100);"Porphyromonadaceae"_unclassified(100);...
This is telling you that Otu008 was observed 5337 times in your samples and that all of the sequences (100%) were classified as being members of the Alistipes.
ASVs
OTUs generally represent sequences that are not more than 3% different
from each other. In contrast, ASVs (aka ESVs) strive to differentiate
sequences into separate OTUs if they are different from each other. There
are challenges with this approach including the possibility of separating
operons from the same genome into separate ASVs and that an ASV is
typically really a cluster of sequences that are one or two bases apart
from each other. Regardless, some people want to give this a go. The
method built into mothur for identifying ASVs is pre.cluster.
We did this above and then removed chimeras and contaminant sequences. We
can convert the fasta and count_table files we used to form OTUs to a shared
file using the make.shared command.
mothur > make.shared(count=final.count_table)
This results in a shared and list file. The shared file we can use like the shared file from forming OTUs or phylotypes. The list file we can use to generate a consensus taxonomy for each ASV.
mothur > classify.otu(list=final.asv.list, count=final.count_table, taxonomy=final.taxonomy, label=ASV)
Phylotypes
For some analyses you may desire to bin your sequences in to phylotypes according to their taxonomic classification. We can do this using the phylotype command:
mothur > phylotype(taxonomy=final.taxonomy)
The cutoff numbering is a bit different for phylotype compared to cluster/cluster.split. Here you see 1 through 6 listed; these correspond to Genus through Kingdom levels, respectively. So if you want the genus-level shared file we’ll do the following:
mothur > make.shared(list=final.tx.list, count=final.count_table, label=1)
We also want to know who these OTUs are and can run classify.otu on our phylotypes:
mothur > classify.otu(list=final.tx.list, count=final.count_table, taxonomy=final.taxonomy, label=1)
Phylogenetic
If you are interested in using methods that depend on a phylogenetic tree such as calculating phylogenetic diversity or the unifrac commands, you’ll need to generate a tree. This process gets mess as your number of sequences increases. But here’s how we’d do it using dist.seqs and clearcut...
mothur > dist.seqs(fasta=final.fasta, output=lt)
mothur > clearcut(phylip=final.phylip.dist)
Analysis
Moving on, let’s do something more interesting and actually analyze our data. We’ll focus on the OTU-based dataset. The analysis using ASVs or phylotypes is essentially the same. Also, remember that our initial question had to do with the stability and change in community structure in these samples when comparing early and late samples. Keep in mind that the group names have either a F or M (sex of animal) followed by a number (number of animal) followed by a D and a three digit number (number of days post weaning).
We now want to do is see how many sequences we have in each sample. We’ll do this with the count.groups command:
mothur > count.groups(shared=final.opti_mcc.shared)
We see that our smallest sample had 2403 sequences in it. That is a reasonable number. Despite what some say, subsampling and rarefying your data is an important thing to do. We’ll generate a subsampled file for our analyses with the sub.sample command:
mothur > sub.sample(shared=final.opti_mcc.shared, size=2403)
OTU-based analysis
Alpha diversity
Let’s start our analysis by analyzing the alpha diversity of the samples. First we will generate rarefaction curves describing the number of OTUs observed as a function of sampling effort. We’ll do this with the rarefaction.single command:
mothur > rarefaction.single(shared=final.opti_mcc.shared, calc=sobs, freq=100)
This will generate files ending in *.rarefaction, which again can be plotted in your favorite graphing software package. Alas, rarefaction is not a measure of richness, but a measure of diversity. If you consider two communities with the same richness, but different evenness then after sampling a large number of individuals their rarefaction curves will asymptote to the same value. Since they have different evennesses the shapes of the curves will differ. Therefore, selecting a number of individuals to cutoff the rarefaction curve isn’t allowing a researcher to compare samples based on richness, but their diversity. Finally, let’s get a table containing the number of sequences, the sample coverage, the number of observed OTUs, and the Inverse Simpson diversity estimate using the summary.single command. To standardize everything, let’s randomly select 2403 sequences from each sample 1000 times and calculate the average (note: that if we set subsample=T, then it would use the size of the smallest library):
mothur > summary.single(shared=final.opti_mcc.shared, calc=nseqs-coverage-sobs-invsimpson, subsample=T)
These data will be outputted to a table in a file called final.opti_mcc.groups.ave-std.summary. Interestingly, the sample coverages were all above 97%, indicating that we did a pretty good job of sampling the communities. Plotting the richness or diversity of the samples would show that there was little difference between the different animals or between the early and late time points. You could follow this up with a repeated-measures ANOVA and find that there was no significant difference based on sex or early vs. late.
Beta diversity measurements
Now we’d like to compare the membership and structure of the various samples using an OTU-based approach. Let’s start by calculating the similarity of the membership and structure found in the various samples. We’ll do this with the dist.shared command that will allow us to rarefy our data to a common number of sequences.
mothur > dist.shared(shared=final.opti_mcc.shared, calc=braycurtis-jclass, subsample=t)
These two distance matrices (i.e. final.opti_mcc.jclass.0.03.lt.ave.dist and final.opti_mcc.braycurtis.0.03.lt.ave.dist) can then be visualized using the pcoa or nmds plots. Principal Coordinates (PCoA) uses an eigenvector-based approach to represent multidimensional data in as few dimesnsions as possible. Our data is highly dimensional (~9 dimensions).
mothur > pcoa(phylip=final.opti_mcc.braycurtis.0.03.lt.ave.dist)
The output of these commands are three files ending in *dist, *pcoa, and *pcoa.loadings. The final.opti_mcc.braycurtis.0.03.lt.ave.pcoa.loadings file will tell you what fraction of the total variance in the data are represented by each of the axes. In this case, the first and second axis represent about 48 and 18% of the variation (66% of the total) for the braycurtis distances. The output to the screen indicates that the R-squared between the original distance matrix and the distance between the points in 2D PCoA space was 0.93, but that if you add a third dimension the R-squared value increases to 0.97. All in all, not bad.
Alternatively, non-metric multidimensional scaling (NMDS) tries to preserve the distance between samples using a user-defined number of dimensions. We can run our data through NMDS with 2 dimensions with the following commands
mothur > nmds(phylip=final.opti_mcc.braycurtis.0.03.lt.ave.dist)
Opening the final.opti_mcc.braycurtis.0.03.lt.ave.nmds.stress file we can inspect the stress and R\^2 values, which describe the quality of the ordination. Each line in this file represents a different iteration and the configuration obtained in the iteration with the lowest stress is reported in the final.opti_mcc.braycurtis.0.03.lt.ave.nmds.axes file. In this file we find that the lowest stress value was 0.19 with an R-squared value of 0.89; that stress level is a little higher than we’d like. You can test what hapens with three dimensions by the following:
mothur > nmds(phylip=final.opti_mcc.braycurtis.0.03.lt.ave.dist, mindim=3, maxdim=3)
The stress value drops to 0.12 and the R2 value goes up to 0.93. Not bad. In general, you would like a stress value below 0.20 and a value below 0.10 is even better. Although not so clear for this dataset, we typically find that NMDS is better than PCoA because the R^2 value is better. Although there are tools out there to plot the three dimensions of the NMDS data, this is a bad idea since no one can see 3D on a 2D page. Looking at the 2D data by PCoA or NMDS makes it clear that the early and late samples cluster separately from each other. Ultimately, ordination is a data visualization tool.
We might ask if the spatial separation that we see between the early and late plots in the NMDS plot is statistically significant. To do this we have two statistical tools at our disposal. The first analysis of molecular variance (amova), tests whether the centers of the clouds representing a group are more separated than the variation among samples of the same treatment. This is done using the distance matrices we created earlier and does not actually use ordination.
We can test to determine whether the clustering within the ordinations is statistically significant or not using by using the amova command. To run amova, we will first need to create a design file that indicates which treatment each sample belongs to. There is a file called mouse.time.design in the folder you downloaded that looks vaguely like this:
group treatment
F3D0 Early
F3D1 Early
F3D141 Late
F3D142 Late
F3D143 Late
F3D144 Late
F3D145 Late
F3D146 Late
F3D147 Late
F3D148 Late
F3D149 Late
F3D150 Late
F3D2 Early
F3D3 Early
F3D5 Early
F3D6 Early
F3D7 Early
F3D8 Early
F3D9 Early
We can then run amova with this file as follows...
mothur > amova(phylip=final.opti_mcc.braycurtis.0.03.lt.ave.dist, design=mouse.time.design)
Early-Late Among Within Total
SS 0.505939 0.682134 1.18807
df 1 17 18
MS 0.505939 0.0401255
Fs: 12.6089
p-value: <0.001*
Here we see from the AMOVA that the “cloud” early and late time points has a significantly different centroid for this mouse. Thus, the observed separation in early and late samples is statistically significant. We can also see whether the variation in the early samples is significantly different from the variation in the late samples using the homova command:
mothur > homova(phylip=final.opti_mcc.braycurtis.0.03.lt.ave.dist, design=mouse.time.design)
HOMOVA BValue P-value SSwithin/(Ni-1)_values
Early-Late 1.42156 <0.001* 0.0575547 0.0246329
We see that there is a significant difference in the variation with the early samples having a larger amount of variation (0.058) than the late samples (0.025). This was what we found in the original study - the early samples were less stable than the late samples.
Next, we might ask which OTUs are responsible for shifting the samples along the two axes. We can determine this by measuring the correlation of the relative abundance of each OTU with the two axes in the NMDS dataset. We do this with the corr.axes command:
mothur > corr.axes(axes=final.opti_mcc.braycurtis.0.03.lt.ave.pcoa.axes, shared=final.opti_mcc.0.03.subsample.shared, method=spearman, numaxes=3)
This command generates the final.opti_mcc.0.03.subsample.spearman.corr.axes file. The data for the first five OTUs look like this...
OTU axis1 p-value axis2 p-value axis3 p-value length
Otu001 -0.082529 0.726234 0.776120 0.000094 -0.524144 0.021244 0.940160
Otu002 0.036939 0.875465 0.855764 0.000003 -0.270889 0.250438 0.898375
Otu003 -0.033333 0.887537 0.510526 0.025516 0.212281 0.367785 0.553906
Otu004 -0.802106 0.000036 -0.179903 0.445305 0.227293 0.334885 0.852878
Otu005 -0.854761 0.000003 -0.105309 0.655027 -0.358052 0.128741 0.932688
...
This helps to illustrate the power of OTUs over phylotypes since each of these OTUs is behaving differently. These data can be plotted in what’s known as a biplot where lines radiating from the origin (axis1=0, axis2=0, axis3=0) to the correlation values with each axis are mapped on top of the PCoA or NMDS plots. Later, using the metastats command, we will see another method for describing which populations are responsible for differences seen between specific treatments. An alternative approach to building a biplot would be to provide data indicating metadata about each sample. For example, we may know the weight, height, blood pressure, etc. of the subjects in these samples. For discussion purposes the file mouse.dpw.metadata is provided and looks something like this:
group dpw
F3D0 0
F3D1 1
F3D141 141
F3D142 142
...
We can then run corr.axes again with the metadata option:
mothur > corr.axes(axes=final.opti_mcc.braycurtis.0.03.lt.ave.pcoa.axes, metadata=mouse.dpw.metadata, method=spearman, numaxes=3)
Opening the file mouse.dpw.spearman.corr.axes, we see:
Feature axis1 p-value axis2 p-value axis3 p-value length
dpw -0.754386 0.000190 -0.029825 0.899309 0.363158 0.123378 0.837778
Indicating that as the dpw decreases, the communities shift to the negative direction along axis 1 and as it increases the communities shift to in the positive direction along axis 3.
Another tool we can use is get.communitytype to see whether our data can be partitioned in to separate community types
mothur > get.communitytype(shared=final.opti_mcc.0.03.subsample.shared)
K NLE logDet BIC AIC Laplace
1 11276.49 608.03 11897.77 11698.49 11192.72
2 11474.63 387.66 12718.66 12319.63 10891.96
3 12389.27 72.89 14256.05 13657.27 11260.50
4 13368.46 -370.10 15857.98 15059.46 11629.49
5 14317.94 -843.60 17430.22 16431.94 11953.51
We see that the minimum Laplace value is for a K value of 2 (10891.96). This indicates that our samples belonged to two community types. Opening final.opti_mcc.0.03.subsample.0.03.dmm.mix.design we see that all of the late samples and the Day 0 sample belonged to Partition_2 and the other early samples belonged to Partition_1 (or vice versa). We can look at the final.opti_mcc.0.03.subsample.0.03.dmm.mix.summary file to see which OTUs were most responsible for separating the communities:
OTU P0.mean P1.mean P1.lci P1.uci P2.mean P2.lci P2.uci Difference CumFraction
Otu005 3.36 0.44 0.26 0.72 10.63 9.38 12.04 10.19 0.15
Otu004 6.19 3.70 2.98 4.59 8.60 7.55 9.79 4.90 0.22
Otu006 5.67 3.77 3.05 4.67 7.31 6.38 8.36 3.53 0.27
Otu008 3.93 5.70 4.73 6.87 2.89 2.41 3.46 2.81 0.31
Otu010 2.00 0.93 0.63 1.35 3.32 2.79 3.95 2.40 0.34
Otu001 9.49 10.70 9.13 12.53 8.50 7.47 9.69 2.19 0.37
Otu007 5.63 6.81 5.69 8.16 4.78 4.11 5.57 2.03 0.40
Otu013 1.18 2.54 1.98 3.26 0.59 0.42 0.84 1.95 0.43
Otu009 3.33 4.18 3.40 5.14 2.67 2.23 3.21 1.51 0.45
...
Again we can cross-reference these OTU labels with the consensus classifications in the stability.opti_mcc.cons.taxonomy file to get the names of these organisms.
Population-level analysis
In addition to the use of corr.axes and get.communitytype we have several tools to differentiate between different groupings of samples. The first we’ll demonstrate is metastats, which is a non-parametric T-tetst that determines whether there are any OTUs that are differentially represented between the samples from men and women in this study. Run the following in mothur:
mothur > metastats(shared=final.opti_mcc.0.03.subsample.shared, design=mouse.time.design)
Looking in the first 5 OTUs from final.opti_mcc.0.03.subsample.0.03.Late-Early.metastats file we see the following...
OTU mean(group1) variance(group1) stderr(group1) mean(group2) variance(group2) stderr(group2) p-value
Otu001 0.086434 0.000119 0.003447 0.112082 0.002525 0.016750 0.152847
Otu002 0.073616 0.000286 0.005344 0.079022 0.000466 0.007192 0.540460
Otu003 0.067832 0.000092 0.003025 0.070837 0.000236 0.005122 0.638362
Otu004 0.088556 0.000162 0.004030 0.043141 0.000398 0.006654 0.000999
Otu005 0.109904 0.000560 0.007483 0.014889 0.000953 0.010289 0.000999
Otu006 0.075822 0.000096 0.003105 0.040181 0.000138 0.003909 0.000999
Otu007 0.051935 0.000202 0.004494 0.072918 0.002084 0.015217 0.229770
Otu008 0.034166 0.000472 0.006868 0.055163 0.000484 0.007335 0.045954
Otu009 0.027923 0.000070 0.002640 0.040412 0.000221 0.004953 0.036963
Otu010 0.041323 0.000354 0.005950 0.011190 0.000132 0.003831 0.000999
...
These data tell us that OTUs 4, 5 and 6 were significantly different between the early and late samples. Keep in mind that the p-values we output from metastats still need to be corrected for multiple comparisons
Another non-parametric tool we can use as an alternative to metastats is lefse:
mothur > lefse(shared=final.opti_mcc.0.03.subsample.shared, design=mouse.time.design)
Number of significantly discriminative features: 80 ( 83 ) before internal wilcoxon.
Number of discriminative features with abs LDA score > 2 : 80.
Looking at the top of the lefse summary file we see:
OTU logMaxMean Class LDA pValue
Otu001 5.04954 Early NA NA
Otu002 4.89775 Early NA NA
Otu003 4.85026 Early NA NA
Otu004 4.94722 Late 4.37976 0.00044411
Otu005 5.04101 Late 4.66273 0.00044411
Otu006 4.87979 Late 4.24045 0.000238563
Otu007 4.86284 Early NA NA
Otu008 4.74164 Early 3.7859 0.0453786
Otu009 4.60652 Early NA NA
Otu010 4.6162 Late 4.14149 0.00190961
...
OTUs 4, 5, and 6 are significantly different between the two groups and are significantly elevated in the late samples
ASV-based analysis
ASV-based analysis is the same as OTU-based analysis, but at a different taxonomic scale. We will leave you on your own to replicate the OTU-based analyses described above with the ASV data.
Phylotype-based analysis
Phylotype-based analysis is the same as OTU-based analysis, but at a different taxonomic scale. We will leave you on your own to replicate the OTU-based analyses described above with the phylotype data.
Phylogeny-based analysis
OTU and phylotype-based analyses are taxonomic approaches that depend on a binning procedure. In contrast, phylogeny-based approaches attempt similar types of analyses using a phylogenetic tree as input instead of a shared file. Because of this difference these methods compare the genetic diversity of different communities.
Alpha diversity
When using phylogenetic methods, alpha diversity is calculated as the total of the unique branch length in the tree. This is done using the phylo.diversity command. Because of differences in sampling depth we will rarefy the output:
mothur > phylo.diversity(tree=final.phylip.tre, count=final.count_table, rarefy=T)
This will generate a file ending in rarefaction.
Beta diversity
The unifrac-based metrics are used to assess the similarity between two communities membership (unifrac.unweighted) and structure (unifrac.weighted). We will use these metrics and generate PCoA plots to compare our samples. There are two beta-diversity metrics that one can use - unweighted and weighted. We will also have mothur subsample the trees 1000 times and report the average:
mothur > unifrac.unweighted(tree=final.phylip.tre, count=final.count_table, distance=lt,random=F, subsample=t)
mothur > unifrac.weighted(tree=final.phylip.tre, count=final.count_table, distance=lt, random=F, subsample=t)
These commands will distance matrices (final.phylip.1.weighted.ave.dist) that can be analyzed using all of the beta diversity approaches described above for the OTU-based analyses.
Putting it all together
It is perfectly acceptable to enter the commands for your analysis from
within mothur. We call this the interactive
mode. If you are doing a lot these types
of analysis or you want to use this SOP on your own data without
thinking too much there are a couple of other options available.
Batch mode
In the folder that you downloaded from the wiki is a file called
stability.batch. If you look at it you’ll see all of the commands you
ran, but instead of listing out the file names it uses the current
option throughout. You can copy and paste from this file and get the
same output as we got above. The beauty of the batch mode is that you
can run mothur from your command line without much typing. For example
you would run the following:
$ ./mothur stability.batch
Don’t enter the “$” that represents the prompt. Sit back and wait and let it rip. This is what we call the batch mode. When we do this it takes about 2.25 minutes to run. The other wonderful thing about this approach is that you can use this very file changing the name of the file you list in make.contigs. You’ll also notice that you can enter comments into your batch files using the “#” character.
Command line mode
The third way we have of running mothur is by entering mothur commands
directly using the command line mode.
This is done like so:
$ ./mothur "#make.contigs(file=stability.files)"
This command will fire mothur up, run make.contigs, and then quit. This
is useful for people that want to script commands and go back and forth
between different programs. The key ingredients here are the quotes
around the commands and the “#” character that tells mothur this is
not a batch file. If you really went nuts you could combine commands
using “;” characters like so:
$ ./mothur "#make.contigs(file=stability.files, maxambig=0, maxlength=275);unique.seqs(count=current);align.seqs(fasta=current, reference=silva.v4.fasta);screen.seqs(fasta=current, count=current, start=1968, end=11550, maxhomop=8);filter.seqs(fasta=current, vertical=T, trump=.); pre.cluster(fasta=current, count=current, diffs=2);unique.seqs(fasta=current, count=current);chimera.vsearch(fasta=current, count=current, dereplicate=t);classify.seqs(fasta=current, count=current, reference=trainset9_032012.pds.fasta, taxonomy=trainset9_032012.pds.tax, cutoff=80); remove.lineage(fasta=current, count=current, taxonomy=current, taxon=Chloroplast-Mitochondria-unknown-Archaea-Eukaryota);remove.groups(count=current, fasta=current, taxonomy=current, groups=Mock);dist.seqs(fasta=current, cutoff=0.03);cluster(column=current, count=current, cutoff=0.03);make.shared(list=current, count=current, label=0.03);classify.otu(list=current, count=current, taxonomy=current, label=0.03); phylotype(taxonomy=current);make.shared(list=current, count=current, label=1);classify.otu(list=current, count=current, taxonomy=current, label=1);"
Finally, another great resource when running mothur is the logfile. If
you go to your folder where you are running mothur, you should find one
or more file that looks like mothur.1364488920.logfile. Open that up and
you’ll see all of the commands you entered and the output that was put
to the screen. If anything ever goes wrong and you need to email us,
please include this file!
Revisions
- 6/24/19 - Updated to reflect version 1.42.3 outputs.
- 1/11/21 - Updates to reflect version 1.47.0 outputs.
- 8/30/22 - Updates to reflect changes with 1.48.0 syntax