The command can be used to assign sequences to OTUs or fit sequences to existing OTUs. Currently, mothur has two methods for doing this:

  • Closed: Fit reads to existing OTUs, scrapping any reads unable to be fitted.
  • Open: Fit reads to existing OTUs, any unfitted reads are clustered separately into new OTUs.

For this tutorial you should download the file and decompress it.

If you use the command, please cite the OptiFit paper:

Sovacool KL, Westcott SL, Mumphrey MB, Dotson GA, Schloss PD. 2022. OptiFit: an Improved Method for Fitting Amplicon Sequences to Existing OTUs. mSphere 7:e00916-21.

See the citation file for a BibTeX entry.

Default settings

You may run the command in de novo or reference mode. 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.

de novo fitting

The de novo method allows you to use your dataset as a reference. mothur will randomly select a portion of your dataset to be the reference. The reference is then clustered with OptiClust, and the remaining sequences are fitted to the reference otus with OptiFit. This process is repeated n times with the best OTU assignments chosen as the output.

mothur >, count=marine.0_2.01.count_table)

The best sensspec results were found on the 4th iteration:

iter   label   cutoff  numotus tp  tn  fp  fn  sensitivity specificity ppv npv fdr accuracy    mcc f1score
4  0.03    0.03    5581    315417  114823776   76333   53810   0.854263    0.999336    0.805149    0.999532    0.805149    0.998871    0.828779    0.828979

reference fitting

The reference fitting methods use a reference list and fit the new dataset’s sequences into the reference otus. Perhaps you have a study where 20 patients were sampled and you clustered sequences into de novo OTUs with OptiClust, and you want to fit a new patient’s data to the existing OTUs. There are several ways to fit new data to an existing dataset.

The reference method takes a user-provided reference and fits the new dataset’s sequences into the reference otus.

mothur > unique.seqs(fasta=silva.v4.fasta, format=count)
mothur > dist.seqs(fasta=current, cutoff=0.03)
mothur > cluster(column=current, count=current)
mothur >, column=marine.0_2.01.dist, count=marine.0_2.01.count_table, reffasta=silva.v4.unique.fasta, refcolumn=silva.v4.unique.dist, reflist=silva.v4.unique.opti_mcc.list)

The sens.spec results:

iter   label   cutoff  numotus tp  tn  fp  fn  sensitivity specificity ppv npv fdr accuracy    mcc f1score
1  0.03    0.03    3080    284781  114851257   48852   84446   0.77129 0.999575    0.853576    0.999265    0.853576    0.998844    0.810817    0.810349


The accnos parameter may be used to indicate a subset of your data as the reference. You can merge the count and fasta files containing the old and new data. Mothur will cluster the references using OptiClust and then fit the query reads to the reference OTUs.

 mothur > list.seqs(fasta=reference.fasta)
 mothur > merge.files(input=reference.fasta-query.fasta, output=combined.fasta)
 mothur > merge.count(count=reference.count_table-query.count_table, output=combined.count_table)
 mothur > dist.seqs(fasta=combined.fasta, cutoff=0.03)
 mothur >, column=current, fasta=combined.fasta, count=combined.count_table)


The reflist parameter can be used to provide a list file for the references. This can be helpful if you want to use existing OTUs from an earlier run of mothur’s OptiClust algorithm.

 mothur > merge.files(input=reference.fasta-query.fasta, output=combined.fasta)
 mothur > merge.count(count=reference.count_table-query.count_table, output=combined.count_table)
 mothur > dist.seqs(fasta=combined.fasta, cutoff=0.03)
 mothur >, column=current, fasta=combined.fasta, count=combined.count_table)


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 option is NOT RECOMMENDED.

mothur >, count=marine.0_2.01.count_table)

Again, the column-formatted distance matrix can be square or lower-triangle and mothur will figure it out.


The count file 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 >, count=marine.0_2.01.count_table)


The reffasta parameter allows you to enter a fasta file for your reference dataset.

refcolumn && refphylip

The refcolumn and refphylip parameters allow you to enter a reference data distance file, to reduce processing time. It is not required, but recommended when using reference mode.


The reflist parameter allows you to enter a list file for your reference dataset.


The accnos parameter allows you to assign reference seqeunces by name. This can save time by allowing you to provide a distance matrix containing all the sequence distances rather than a sample matrix and reference matrix and mothur calculating the distances between the sample and reference.


The options for the method parameter are open or closed. The default is open.

  • Closed: Fit reads to existing OTUs, scrapping any reads unable to be fitted.
  • Open: Fit reads to existing OTUs, any unfitted reads are clustered separately into new OTUs with OptiClust.


The printref option controls whether the reference sequences are printed to list file and included in the final sensspec calculations. Set printref=t (true) to include the reference sequences, or printref=f (false) to exclude the reference sequences.

We recommend using printref=f when the reference sequences are from a public external database such as SILVA or Greengenes, so that only the sequences from your dataset of interest are included in the list and sensspec files.

Using printref=t may be more appropriate when the reference is created by selecting a random sample from a dataset followed by using the remaining sequences as the query for OptiFit.


The metric parameter allows to select the metric to optimize in the OptiClust method. Options are Matthews correlation coefficient (mcc - default), 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). The default is mcc.

mothur >, count=marine.0_2.01.count_table, metric=tptn)

iter   label   cutoff  numotus tp  tn  fp  fn  sensitivity specificity ppv npv fdr accuracy    mcc f1score
2  0.03    0.03    5996    294554  114847888   52221   74673   0.797759    0.999546    0.84941 0.99935 0.84941 0.998899    0.82263 0.822774


The fitpercent parameter allow you to set percentage of reads to be fitted. Default=50. Max=100, min=0.01.


The delta parameter allows to set the stable value for the metric in the opticluster algorithm. Default delta=0.0001. To reach a full convergence, set delta=0.


The iters parameter allows you to set the maxiters for the OptiClust algorithm. Default=100.


The denovoiters parameter allow you to set the maxiters for the de novo sampling. Default=10.


With the opticlust method the list file is created for the cutoff you set. The default cutoff is 0.03.


If you want greater precision, there is a precision option in the command.


To read in a phylip-formatted distance matrix you need to use the phylip option.

We DO NOT recommend using the name file. Instead we recommend using a count file. The count file reduces the time and resources needed to process commands. It is a smaller file and can contain group information.

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:


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. 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.

Finer points


You may notice that if you run the same command multiple times for the same dataset, you might get slightly different output.

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


  • 1.47.0 First Introduced