cluster
Once a distance matrix gets read into mothur, the cluster command can be used to assign sequences to OTUs. Presently, mothur implements three clustering methods:
- OptiClust (
opti): OTUs are assembled using metrics to determine the quality of clustering (the default setting). - Nearest neighbor (
nearest): Each of the sequences within an OTU are at most X% distant from the most similar sequence in the OTU. - Furthest neighbor (
furthest): All of the sequences within an OTU are at most X% distant from all of the other sequences within the OTU. - Average neighbor (
average): This method is a middle ground between the other two algorithms. - AGC (
agc): Abundance-based greedy clustering. - DGC (
dgc): Distance-based greedy clustering. - Unique (
unique): Creates a list file from a name or count file where every unique sequence is assigned to it’s own OTU (i.e. Amplicon Sequence Variant)
If there is an algorithm that you would like to see implemented, please consider either contributing to the mothur project or contacting the developers and we’ll see what we can do. The opticlust algorithm is the default option. For this tutorial you should download the Final.zip file and decompress it.
If you use the OptiClust method (opti) in this command,
please cite the OptiClust paper:
Westcott SL, Schloss PD. 2017. OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units. mSphere 2:e00073-17.
See the citation file for a BibTeX entry.
Default settings
Either a phylip-formatted distance matrix or a column-formatted distance matrix must be inputted for cluster to be successful, the default output of the dist.seqs command is the column-format. If you have a favorite format, please let us know and we can work with you to incorporate that feature into mothur. Because the phylip format is so popular most software can generate this format for you.
phylip
To read in a phylip-formatted distance matrix you need to use the phylip option:
mothur > cluster(phylip=final.phylip.dist)
Whereas dotur required you to indicate whether the matrix was square or lower-triangular, mothur is able to figure this out for you.
Once you execute the command, mothur reads in the matrix and generates a progress bar:
mothur > cluster(phylip=final.phylip.dist)
*******************#****#****#****#****#****#****#****#****#****#****#
Reading matrix: |||||||||||||||||||||||||||||||||||||||||||||||||||
**********************************************************************
column & name or count
To read in a column-formatted distance matrix you must provide a filename for the name or count option. The .name file was generated during the unique.seqs command.
mothur > cluster(column=final.dist, name=final.names)
or
mothur > cluster(column=final.dist, count=final.count_table)
Again, the column-formatted distance matrix can be square or lower-triangle and mothur will figure it out.
name
A name file contains two columns. The first column contains the name of a reference sequence that is in a distance matrix and the second column contains the names of the sequences (separated by commas) that the reference sequence represents. The list of names in the second column should always contain at least the reference sequence name.
There are several reasons to be interested in providing a name file with your distance matrix. First, as sequencing collections increase in size, the number of duplicate sequences is increasing. This is especially the case with sequences generated via pyrosequencing. Sogin and colleagues 1 found that less than 50% of their sequences were unique. Because the alignments and distances for the duplicate sequences are the same, re-processing each duplicate sequence takes a considerable amount of computing time and memory.
Example from final.names:
...
GQY1XT001EYE6M GQY1XT001EYE6M,GQY1XT001D69D7,GQY1XT001A1LWJ
GQY1XT001EXZXC GQY1XT001EXZXC
GQY1XT001EXZLY GQY1XT001EXZLY
GQY1XT001EXOOM GQY1XT001EXOOM
GQY1XT001EX24Z GQY1XT001EX24Z,GQY1XT001AMCGM
GQY1XT001EWUBU GQY1XT001EWUBU,GQY1XT001DJLCH,GQY1XT001B50B7
GQY1XT001EWJBM GQY1XT001EWJBM
...
Second, if you pre-screen a clone library using ARDRA then you may only have a sequence for a handful of clones, but you know the number of times that you have seen a sequence like it. In such a case the second column of the name file would contain the sequence name as well as dummy sequence names
...
AA1234 AA1234,AA1234.1,AA1234.2
AA1235 AA1235
AA1236 AA1236,AA1236.1
AA1237 AA1237,AA1237.1,AA1237.2,AA1237.3
AA1238 AA1238,AA1238.1
...
A count or name file is not required (unless you are using the column= option), but depending on the data set to be analyzed, could significantly accelerate the processing time of downstream calculations. In this simple example, the final dataset contains 51474 sequences. The distance matrix in the file final.phylip.dist is a lower triangle matrix for the 3772 unique sequences. While you could just read the matrix in and analyze the set of 3772 unqiue sequences, this would give a considerably different analysis than if you used the entire 51474 sequence data set. Considering the frequency of sequences is critical for pretty much every analysis in mothur, we want to use the name or count file to artificially inflate the matrix to its full size. In this case we use the namefile option:
mothur > cluster(phylip=final.phylip.dist, name=final.names)
mothur remembers that the distances for the reference sequence also apply to all of the sequences listed in the second column. Using a name file can considerably accelerate the amount of processing time required to analyze some data sets.
Next let’s run the cluster() command:
mothur > cluster(phylip=final.phylip.dist, name=final.names)
This command will generate the following output:
Clustering /Users/sarahwestcott/desktop/release/final.phylip.dist
iter time label num_otus cutoff tp tn fp fn sensitivity specificity ppv npv fdr accuracy mcc f1score
0 0 0.03 3772 0.03 0 7059666 0 52440 0 1 0 0.992627 0 0.992627 0 0
1 0 0.03 1261 0.03 27541 7053368 6298 24899 0.525191 0.999108 0.813883 0.996482 0.186117 0.995614 0.651823 0.638417
2 0 0.03 1184 0.03 30143 7052434 7232 22297 0.574809 0.998976 0.806502 0.996848 0.193498 0.995848 0.678933 0.671224
3 0 0.03 1176 0.03 30254 7052676 6990 22186 0.576926 0.99901 0.812319 0.996864 0.187681 0.995898 0.682669 0.67468
4 0 0.03 1175 0.03 30283 7052713 6953 22157 0.577479 0.999015 0.813272 0.996868 0.186728 0.995907 0.683404 0.675387
5 0 0.03 1176 0.03 30256 7052761 6905 22184 0.576964 0.999022 0.814187 0.996864 0.185813 0.99591 0.683487 0.67535
Running the cluster() command generates a list. The data outputted to the screen is the same as that in the sabund file. You will notice that the list file has a “.opti.” tag inserted after the name of the distance matrix. opti corresponds to the algorithm that was used to perform the clustering. In this case opticlust (opti) was used. Other possibilities include “an” for average neighbor, “fn” for furthest neighbor, “nn” for nearest neighbor. Vsearch clustering algorithms include: “agc” and “dgc”.
count
The count file is similar to the name file in that it is used to represent the number of duplicate sequences for a given representative sequence. mothur will use this information to form the correct OTU’s. Unlike, when you use a name file the list file generated will contain only the unique names, so be sure to include the count file in downstream analysis with the list file.
mothur > make.table(name=final.names)
Example from final.count_table:
Representative_Sequence total
GQY1XT001CFHYQ 467
GQY1XT001C44N8 3677
GQY1XT001C296C 4652
GQY1XT001ARCB1 2202
GQY1XT001CFWVZ 1967
GQY1XT001DHF2X 2137
GQY1XT001AEGCJ 2140
GQY1XT001CPCVN 2837
...
mothur > cluster(phylip=final.phylip.dist, count=final.count_table)
Options
method
The methods available in mothur include opticlust (opti), average neighbor (average), furthest neighbor (furthest), nearest neighbor (nearest), Vsearch agc (agc), Vsearch dgc (dgc) and Unique (unique). By default cluster() uses the opticlust algorithm; this can be changed with the method option.
mothur > cluster(column=final.dist, count=final.count_table, method=opti)
To obtain a average neighbor clustering of the data use the method option to produce the subsequent output:
mothur > cluster(column=final.dist, count=final.count_table, method=average, cutoff=0.15)
unique 4652 2678 442 124 69 59 35 32 20 17 16 18 12 9 7 6 6 3 2 9 9 7 9 6 5 6 3 21
0.01 4964 1896 316 115 65 40 24 20 19 13 10 12 9 11 4 9 5 7 4 2 3 7 7 7 4 6 8 51
0.02 5451 986 184 84 49 28 17 14 17 7 10 7 9 3 6 2 6 4 2 1 3 2 1 3 3 2 3 01
0.03 6129 624 144 49 39 17 14 11 8 10 7 5 8 6 5 1 4 5 0 2 1 0 3 2 1 0 1 11
0.04 6159 411 108 44 22 11 9 6 7 7 8 5 7 3 4 1 4 3 2 1 1 1 2 1 2 0 1 11
0.05 7023 258 92 36 23 11 2 5 2 6 8 4 6 2 5 1 3 3 1 1 0 1 2 1 0 0 1 01
changed cutoff to 0.0533794
For ASV clustering of the data use the unique method, which puts each unique sequence in the count file in its own OTU:
mothur > cluster(count=final.count_table, method=unique)
metric
The metric parameter allows to select the metric in the opticluster method. Options are Matthews correlation coefficient (mcc), sensitivity (sens), specificity (spec), true positives + true negatives (tptn), false positives + false negatives (fpfn), true positives (tp), true negative (tn), false positive (fp), false negative (fn), f1score (f1score), accuracy (accuracy), positive predictive value (ppv), negative predictive value (npv), false discovery rate (fdr). Default=mcc.
mothur > cluster(column=final.dist, count=final.count_table, metric=tptn)
iter time label num_otus cutoff tp tn fp fn sensitivity specificity ppv npv fdr accuracy mcc f1score
0 0 0.03 3772 0.03 0 7059666 0 52440 0 1 0 0.992627 0 0.992627 0 0
1 0 0.03 1341 0.03 26876 7054616 5050 25564 0.51251 0.999285 0.841822 0.996389 0.158178 0.995696 0.654974 0.637129
2 0 0.03 1268 0.03 28633 7054797 4869 23807 0.546014 0.99931 0.854665 0.996637 0.145335 0.995968 0.681349 0.666333
3 0 0.03 1265 0.03 28733 7054853 4813 23707 0.547921 0.999318 0.856525 0.996651 0.143475 0.99599 0.683295 0.668318
initialize
The initialize parameter allows to select the initial randomization for the opticluster method. Options are singleton, meaning each sequence is randomly assigned to its own OTU, or oneotu meaning all sequences are assigned to one otu. We have found initialize=singleton to produce better clustering in less time. Default=singleton.
delta
The delta parameter allows to set the stable value for the metric in the opticluster method Default delta=0.0001. To reach a full convergence, set delta=0.
iters
The iters parameter allow you to set the maxiters for the opticluster method. Default=100.
cutoff
With the opticlust method the list file is created for the cutoff you set. The default cutoff is 0.03. With the average neighbor, furthest neighbor and nearest neighbor methods the cutoff should be significantly higher than the desired distance in the list file. We suggest cutoff=0.20. This will provide a boost in speed and less RAM will be required than if you didn’t set the cutoff for reading in the matrix. The cutoff can be set for the cluster command as follows:
mothur > cluster(column=final.dist, count=final.count_table, cutoff=0.05)
iter time label num_otus cutoff tp tn fp fn sensitivity specificity ppv npv fdr accuracy mcc f1score
0 0 0.05 3772 0.05 0 6918676 0 193430 0 1 0 0.972803 0 0.972803 0 0
1 0 0.05 699 0.05 126630 6892041 26635 66800 0.654655 0.99615 0.826216 0.990401 0.173784 0.986863 0.729012 0.730498
2 1 0.05 625 0.05 133759 6887952 30724 59671 0.691511 0.995559 0.813209 0.991411 0.186791 0.98729 0.743526 0.747439
3 0 0.05 620 0.05 134033 6887845 30831 59397 0.692928 0.995544 0.812991 0.99145 0.187009 0.987313 0.744201 0.748173
4 0 0.05 621 0.05 133952 6888036 30640 59478 0.692509 0.995571 0.813843 0.991439 0.186157 0.987329 0.744378 0.748289
5 0 0.05 621 0.05 133952 6888036 30640 59478 0.692509 0.995571 0.813843 0.991439 0.186157 0.987329 0.744378 0.748289
To obtain the OTUs for more than one cutoff using the opti, agc or dgc method, run the following:
mothur > cluster(column=final.dist, count=final.count_table, cutoff=0.03-0.05)
Clustering /Users/sarahwestcott/desktop/release/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 3772 0.03 0 7059666 0 52440 0 1 0 0.992627 1 0.992627 0 0
1 0 0.03 1252 0.03 30175 7051303 8363 22265 0.57542 0.998815 0.782993 0.996852 0.782993 0.995694 0.66919 0.663347
2 1 0.03 1167 0.03 32229 7050338 9328 20211 0.614588 0.998679 0.775537 0.997142 0.775537 0.995847 0.688374 0.685745
3 0 0.03 1162 0.03 32317 7050391 9275 20123 0.616266 0.998686 0.777 0.997154 0.777 0.995866 0.689977 0.687362
4 0 0.03 1161 0.03 32167 7050686 8980 20273 0.613406 0.998728 0.781758 0.997133 0.781758 0.995887 0.690497 0.687425
5 0 0.03 1158 0.03 32165 7050714 8952 20275 0.613368 0.998732 0.78228 0.997133 0.78228 0.995891 0.690708 0.687602
6 0 0.03 1158 0.03 32177 7050699 8967 20263 0.613596 0.99873 0.782058 0.997134 0.782058 0.99589 0.690739 0.68766
0.05
0 0 0.05 3772 0.05 0 6918676 0 193430 0 1 0 0.972803 1 0.972803 0 0
1 1 0.05 685 0.05 130804 6888303 30373 62626 0.676234 0.99561 0.811555 0.99099 0.811555 0.986924 0.734292 0.737741
2 0 0.05 628 0.05 133982 6887553 31123 59448 0.692664 0.995502 0.811496 0.991443 0.811496 0.987265 0.74334 0.747386
3 1 0.05 624 0.05 133831 6887977 30699 59599 0.691883 0.995563 0.813414 0.991422 0.813414 0.987304 0.743829 0.747743
4 0 0.05 623 0.05 133899 6887991 30685 59531 0.692235 0.995565 0.81356 0.991431 0.81356 0.987315 0.744092 0.74801
5 1 0.05 623 0.05 133916 6888006 30670 59514 0.692323 0.995567 0.813654 0.991434 0.813654 0.98732 0.744185 0.748101
vsearch
The vsearch parameter allows you to specify the name and location of your vsearch executable for use with the agc and doc methods. By default mothur will look in your path and mothur’s executable location. You can set the vsearch location as follows: vsearch=/usr/bin/vsearch.
mothur > cluster(fasta=final.fasta, count=final.count_table, vsearch=/usr/bin/vsearch.2.11.1, method=agc)
precision
If you want greater precision, there is a precision option in the cluster() command:
mothur > cluster(column=final.dist, count=final.count_table, method=average, precision=1000, cutoff=0.10)
unique 4652 2678 442 124 69 59 35 32 20 17 16 18 12 9 7 6 6 3 2 9 9 7 9 6 5 6 3 21
0.004 4964 2300 411 112 72 38 31 23 21 10 16 15 12 14 12 4 3 8 4 5 6 7 8 4 7 5 5 31
0.005 4964 2276 396 103 70 40 29 23 19 10 15 16 13 12 9 6 5 6 4 7 6 6 8 4 6 6 6 41
0.006 4964 2259 371 110 71 36 28 25 21 8 15 16 12 11 5 7 6 6 5 7 4 6 9 4 4 5 7 51
0.007 4964 2253 361 104 65 38 27 25 21 8 15 15 10 11 5 7 7 7 5 6 4 6 9 6 4 6 7 51
0.008 4964 1970 361 121 69 39 23 24 20 12 14 12 13 11 7 9 6 7 3 5 4 8 6 5 5 8 5 71
0.009 4964 1937 353 111 68 36 27 22 20 11 13 13 9 10 4 10 7 8 4 4 3 7 4 6 4 5 8 71
0.010 4964 1901 313 114 68 39 25 20 20 13 10 12 8 11 2 8 6 7 4 2 3 7 7 7 6 5 8 51
0.011 4983 1603 296 127 64 33 27 20 17 14 9 11 10 7 3 6 8 7 5 4 3 6 7 7 3 4 6 61
0.012 5011 1500 282 118 57 27 27 19 14 18 7 9 7 6 5 5 8 4 4 5 3 4 4 6 5 3 3 61
0.013 5371 1453 258 113 55 25 26 16 13 14 7 9 6 6 7 5 7 4 3 4 3 3 4 5 5 4 4 51
0.014 5380 1419 247 100 55 19 22 19 12 12 8 8 8 6 5 5 6 4 3 1 6 3 2 6 3 4 5 61
0.015 5447 1249 236 111 54 22 21 17 13 13 8 6 7 5 7 3 8 2 3 1 5 3 3 4 4 3 5 31
0.016 5450 1207 223 100 54 22 19 16 14 11 9 7 8 4 6 5 7 2 2 2 4 2 2 3 4 3 4 31
0.017 5450 1173 200 97 56 25 21 14 15 10 9 7 8 4 6 5 7 1 1 1 4 2 2 3 3 3 4 31
0.018 5450 1154 192 89 52 26 19 14 15 11 9 7 8 3 4 5 7 2 1 1 3 3 2 3 3 3 3 21
0.019 5450 1020 207 85 53 29 19 15 15 13 8 6 7 2 6 5 5 2 2 1 3 3 1 4 3 2 3 11
0.020 5451 987 186 80 49 29 16 16 16 9 9 7 8 3 7 2 6 4 2 1 3 2 1 3 3 2 3 01
0.021 5451 968 172 70 44 29 16 19 14 9 9 8 9 3 6 2 6 5 2 1 2 2 1 3 3 1 2 11
0.022 6114 884 185 69 39 29 13 18 14 9 8 7 9 4 5 2 7 5 2 2 1 1 2 3 3 1 2 11
0.023 6114 846 180 66 39 25 14 17 12 8 10 6 9 4 6 2 6 5 2 2 1 1 2 2 2 2 2 11
0.024 6114 817 166 69 41 23 11 16 13 10 10 5 8 4 5 0 8 6 2 2 2 1 2 2 2 1 3 11
0.025 6115 802 162 62 40 23 11 14 12 9 12 6 7 4 5 0 8 6 1 1 1 1 3 2 1 1 2 01
0.026 6115 744 168 62 37 20 13 11 11 10 11 7 7 5 3 1 5 6 0 2 2 1 3 2 1 1 2 01
0.027 6115 725 163 57 38 18 15 10 11 10 11 7 7 4 4 1 4 6 0 2 2 1 3 1 1 0 3 01
0.028 6115 705 146 52 40 17 15 12 12 9 8 7 9 3 5 1 4 5 0 2 2 1 2 2 1 0 2 01
0.029 6118 671 147 51 39 16 15 13 10 7 7 8 9 5 5 1 4 5 0 2 2 1 2 2 1 0 2 01
0.030 6129 622 148 50 39 17 14 12 9 8 6 7 9 6 5 1 4 5 0 2 1 0 3 2 1 0 1 11
0.031 6129 603 133 54 37 17 15 10 9 9 7 5 7 8 6 1 4 5 0 2 1 0 3 2 1 0 1 11
0.032 6130 583 127 46 38 15 16 7 9 8 7 6 6 6 6 2 4 5 0 2 0 1 3 2 1 0 0 11
0.033 6136 533 130 50 37 14 14 6 8 7 6 7 7 5 5 2 4 4 0 2 0 1 3 2 1 0 0 11
0.034 6153 525 125 47 35 15 13 6 8 8 6 7 7 4 3 2 4 4 0 2 1 1 3 2 1 0 0 11
changed cutoff to 0.0342406
Remember that the 16S rRNA gene is roughly 1,500 bp long. So it would seem silly to have a precision greater than 1,000. Just because you can calculate a number to 20 digits, doesn’t mean they’re all significant.
sim
The sim parameter is used to indicate that your input file contains similarity values instead of distance values. The default is false, if sim=true then mothur will convert the similarity values to distances.
Clustering with vsearch
The vsearch program is written by the vsearch team. You can now use vsearch clustering methods through mothur.
fasta
Vsearch requires a fasta file to cluster.
mothur > cluster(fasta=final.fasta, count=final.count_table, method=agc)
Vsearch methods
The available clustering methods are agc and dgc.
mothur > cluster(fasta=final.fasta, count=final.count_table, method=dgc)
Finer points
Missing distances
Perhaps the second most commonly asked question is why there isn’t a line for distance 0.XX. If you notice the previous example the distances jump from 0.003 to 0.006. Where are 0.004 and 0.005? mothur only outputs data if the clustering has been updated for a distance. So if you don’t have data at your favorite distance, that means that nothing changed between the previous distance and the next one. Therefore if you want OTU data for a distance of 0.005 in this case, you would use the data from 0.003.
Variability
You may notice that if you run the same command multiple times for the same dataset you might get slightly different out for some distances:
mothur > cluster(column=final.dist, count=final.count_table)
iter time label num_otus cutoff tp tn fp fn sensitivity specificity ppv npv fdr accuracy mcc f1score
0 0 0.03 3772 0.03 0 7059666 0 52440 0 1 0 0.992627 0 0.992627 0 0
1 0 0.03 1249 0.03 27469 7053405 6261 24971 0.523818 0.999113 0.814379 0.996472 0.185621 0.995609 0.651167 0.637554
2 0 0.03 1178 0.03 30311 7052546 7120 22129 0.578013 0.998991 0.809783 0.996872 0.190217 0.995887 0.682234 0.674545
3 0 0.03 1174 0.03 30877 7052133 7533 21563 0.588806 0.998933 0.803879 0.996952 0.196121 0.995909 0.686061 0.679736
4 0 0.03 1172 0.03 31138 7051940 7726 21302 0.593783 0.998906 0.801204 0.996988 0.198796 0.995919 0.687808 0.682073
5 0 0.03 1173 0.03 31237 7051921 7745 21203 0.595671 0.998903 0.801319 0.997002 0.198681 0.99593 0.688956 0.683358
6 0 0.03 1173 0.03 31268 7051953 7713 21172 0.596262 0.998907 0.802134 0.997007 0.197866 0.995939 0.689655 0.684044
7 0 0.03 1172 0.03 31336 7051924 7742 21104 0.597559 0.998903 0.801883 0.997016 0.198117 0.995944 0.6903 0.684805
8 0 0.03 1172 0.03 31394 7051880 7786 21046 0.598665 0.998897 0.801276 0.997024 0.198724 0.995946 0.690677 0.685309
9 0 0.03 1173 0.03 31367 7051922 7744 21073 0.59815 0.998903 0.801999 0.997021 0.198001 0.995948 0.690694 0.685236
mothur > cluster(column=final.dist, count=final.count_table)
iter time label num_otus cutoff tp tn fp fn sensitivity specificity ppv npv fdr accuracy mcc f1score
0 0 0.03 3772 0.03 0 7059666 0 52440 0 1 0 0.992627 0 0.992627 0 0
1 0 0.03 1250 0.03 29483 7051666 8000 22957 0.562223 0.998867 0.78657 0.996755 0.21343 0.995647 0.66296 0.655739
2 0 0.03 1165 0.03 31938 7050687 8979 20502 0.609039 0.998728 0.780556 0.997101 0.219444 0.995855 0.687484 0.684212
3 0 0.03 1167 0.03 32087 7050748 8918 20353 0.61188 0.998737 0.782514 0.997122 0.217486 0.995884 0.68997 0.686757
4 0 0.03 1169 0.03 31986 7051007 8659 20454 0.609954 0.998773 0.78696 0.997108 0.21304 0.995907 0.690857 0.687243
5 0 0.03 1168 0.03 31948 7051085 8581 20492 0.60923 0.998785 0.788275 0.997102 0.211725 0.995912 0.691029 0.687283
6 0 0.03 1170 0.03 31966 7051075 8591 20474 0.609573 0.998783 0.788175 0.997105 0.211825 0.995913 0.69118 0.687463
7 0 0.03 1170 0.03 31932 7051142 8524 20508 0.608924 0.998793 0.789302 0.9971 0.210698 0.995918 0.69131 0.687478
8 0 0.03 1170 0.03 31905 7051185 8481 20535 0.60841 0.998799 0.790001 0.997096 0.209999 0.99592 0.691326 0.687415
The variability is caused by randomizing the order of the sequences before
clustering begins. You can set a seed to get reproducible results
with the set.seed command prior to running cluster.fit.
Revisions
- 1.27.0 - reduced memory by 50% and increased speed by 55%.
- 1.28.0 - added count parameter
- 1.34.0 - Bug Fix: nearest method caused crash.
- 1.35.0 - Clustering commands did not include the count file info. when printing list file OTU order. Only effects clustering commands. *.pick commands must preserve otuLabels order. - https://forum.mothur.org/viewtopic.php?f=3&t=3460&p=10483#p10483.
- 1.37.0 - Adds vsearch clustering methods: agc and dgc. #169
- 1.38.0 - Fixes bug with age method.
- 1.38.1 - Removes hard parameter.
- 1.39.0 - Adds opticlust method. opti new default clustering method.
- 1.39.0 - Adds agc and dgc methods of Windows users.
- 1.39.1 - Corrects printing issues with opticlust method.
- 1.40.4 - Bug Fix: Cluster commands printing of list file. #454
- 1.40.4 - Bug Fix: method agc and dgc do not require distance matrix. #456
- 1.41.0 - Adds multiple cutoffs to cluster command for opti, agc and dgc. #305
- 1.41.0 - Nearest neighbor bug fix.
- 1.43.0 - Changes datatype for opti method to correct overflow.
- 1.44.0 - Adds vsearch parameter so that you can specify location of vsearch executable. #682
- 1.45.0 Updates vsearch to 2.16.0. https://github.com/torognes/vsearch/releases/tag/v2.16.0