Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data

Biological mock community

To construct the biological mock community (BioMock) we selected 26 identified fungal cultures (Table S1) from the Center for Forest Mycology Research (CFMR) culture collection (US Forest Service, Madison, WI, USA). These cultures were purposefully chosen to represent a taxonomic range of fungal species, including species with known ITS paralogs, species with GC rich ITS regions, a variety of ITS lengths, and species with a variety of homopolymers in the ITS region. To measure the sensitivity of our bioinformatics approach, we also included two ITS sequences from Leptoporus mollis that were cloned from the same culture as an example of intragenomic variation in the fungal ITS region. These two sequences are more than 3% divergent (95.9% identical) and thus would typically represent separate operational taxonomic units (OTUs) in a clustering pipeline, despite being from the same fungal isolate. All cultures were grown on cellophane on malt extract agar, and DNA was extracted from pure cultures following (Lindner & Banik, 2008). Following extraction, the gDNA was PCR amplified using the fungal ITS specific primers ITS-1F (Gardes & Bruns, 1993) and ITS4 (White et al., 1990). PCR products were then cloned into pGEM-T (Promega, Madison, WI, USA) and Sanger sequenced using the ITS1-F primer following the protocol in (Lindner & Banik, 2011). Sequences were verified via BLAST search and two clones of each isolate were selected and cultured in liquid Luria-Bertani (LB) media and incubated at 37 °C for 24 h. Plasmids were purified from the cultures in LB media using standard alkaline lysis. These plasmids will hereafter be termed “purified plasmids.” The purified plasmids were then Sanger sequenced with vector primers T7 and SP6 to verify the insertion of a single copy of the appropriate ITS fragment. Purified plasmid DNA was quantified using a Qubit^® 2.0 fluorometer and DNA concentrations were equilibrated to 10 nM using DNA-free molecular grade water. Following equilibration, 5 μl of each purified plasmid were combined to make an equimolar “BioMock” community of single-copy purified plasmids.

PCR has known biases, which are related to different sequence characteristics and are hard to predict in mixed DNA communities of unknown composition. To illustrate the impact of initial PCR bias on the number of reads obtained from each member of a mixed DNA community, we generated individual HTAS-compatible PCR products from each BioMock plasmid which were subsequently mixed (post-PCR) in an equimolar ratio. This was accomplished by PCR amplifying each individual plasmid with the same barcoded primer set. PCR products were purified using E-gel^® CloneWell™ 0.8% SYBR^® Safe agarose gels (ThermoFisher Scientific, Madison, WI, USA), quantified using a Qubit^® 2.0 fluorometer, and combined into an equimolar mixture post-amplification. This post-PCR combined mock community can be used to examine sequencing error on NGS platforms and is referred to as BioMock-standards.

Non-biological synthetic mock community

We used the well-annotated rRNA sequence from Saccharomyces cerevisiae as a starting point to design ITS-like synthetic sequences. The ITS adjacent regions of small subunit (SSU) and large subunit (LSU) of S. cerevisiae were chosen as anchoring points because of the presence of conserved priming sites ITS1/ITS1-F and ITS4. A 5.8S sequence was designed using S. cerevisiae as a base but nucleotides were altered so it would be compatible with several primers in the 5.8S region, including ITS2, ITS3, and fITS7. Random sequences were generated with constrained GC content and sequence length for the ITS1 and ITS2 regions. Twelve unique sequences were synthesized (Genescript, Piscataway Township, NJ, USA) and cloned into pUC57 harboring ampicillin resistance. The SynMock sequences and the script to produce them are available in the OSF repository (https://osf.io/4xd9r/) as well as packaged into AMPtk distributions (https://github.com/nextgenusfs/amptk). Each plasmid was purified by alkaline lysis, quantified, and an equimolar mixture was created as a template for HTAS library prep.

Preparation of HTAS libraries and NGS sequencing

High-throughput amplicon sequencing libraries were generated using a proofreading polymerase, Pfx50 (ThermoFisher Scientific, Madison, WI, USA), and thermocycler conditions were as follows: initial denaturation of 94 °C for 3 min, followed by 11 cycles of (94 °C for 30 s, 60 °C for 30 s (drop 0.5 °C per cycle), 68 °C for 1 min), then 26 cycles of (94 °C for 30 s, 55 °C for 30 s, and 68 °C for 1 min), with a final extension of 68 °C for 7 min. PCR products were cleaned using either E-gel^® CloneWell™ 0.8% SYBR^® Safe agarose gels (ThermoFisher Scientific, Madison, WI, USA) or Zymo Select-a-size spin columns (Zymo Research, Irvine, CA, USA). All DNA was quantified using a Qubit^® 2.0 fluorometer with the high-sensitivity DNA quantification kit (ThermoFisher Scientific, Madison, WI, USA).

A single step PCR reaction was used to create Ion Torrent compatible sequencing libraries (PCR protocol described above), and primers were designed according to manufacturer’s recommendations. Briefly, the forward primer was composed of the Ion A adapter sequence, followed by the Ion key signal sequence, a unique Ion Xpress Barcode sequence (10–12 bp), a single base-pair linker (A), followed by the fITS7 primer (Ihrmark et al., 2012). The reverse primer was composed of the Ion trP1 adapter sequence followed by the conserved ITS4 primer (White et al., 1990). Sequencing on the Ion Torrent PGM was done according to manufacturer’s recommendations using an Ion PGM™ Hi-Q™ OT2 Kit, an Ion PGM™ Hi-Q™ Sequencing Kit, an Ion PGM™ sequencing chip (316v2 or 318v2), and raw data were processed with the Ion Torrent Suite v5.0.3 with the “–disable-all-filters” flag given to the BaseCaller. Libraries for Illumina MiSeq were generated by a two-step dual indexing strategy. All samples were PCR amplified with Illumina-fITS7 and Illumina-ITS4 primers using PCR protocol described above. PCR products were cleaned and then dual-barcoded using an eight cycle PCR reaction using the Illumina Nextera Kit and subsequently sequenced using 2 × 300 bp sequencing kit on the Illumina MiSeq at the University of Wisconsin Biotechnology Center DNA Sequencing Facility. All primers utilized in this study are available via the OSF repository (https://osf.io/4xd9r/).

Data processing using AMPtk

AMPtk is publically available at https://github.com/nextgenusfs/amptk. All primary data and data analysis done in this manuscript are available via the Open Science Framework (https://osf.io/4xd9r/). AMPtk is written in Python and relies on several modules: edlib (Šošic & Šikic, 2017), biopython (Cock et al., 2009), biom-format (McDonald et al., 2012), pandas (McKinney, 2010), numpy (van der Walt, Colbert & Varoquaux, 2011), and matplotlib modules (Hunter, 2007). External dependencies are USEARCH v9.1.13 (Edgar, 2010) or greater and VSEARCH v2.2.0 (Rognes et al., 2016) or greater. In order to run the DADA2 (Callahan et al., 2016) method R is required along with the shortRead (Morgan et al., 2009) and DADA2 packages. The major steps for processing HTAS data are broken down into (i) pre-processing reads, (ii) clustering into OTUs, (iii) filtering OTU table, and (iv) assigning taxonomy.

HTAS software pipeline comparison

For comparison of software pipelines, sequencing data from the Ion Torrent PGM and Illumina MiSeq consisted of a single SynMock library, a single BioMock library, and 16 environmental sample libraries. To compare the outputs of these pipelines, OTUs and a corresponding OTU tables were generated using AMPtk v1.2.1, QIIME2 v2018.2, UPARSE (USEARCH v10.0.240), and PIPITS v2.0 (Illumina data only). Read length was set to 300 bp where appropriate for each tool and default or recommended settings were used for each software pipeline with the following exceptions in UPARSE: for processing the Illumina data VSEARCH was used to quality filter, de-replicate (identify unique sequences), and generate an OTU table because the file size was too large to be supported by 32-bit version of USEARCH. QIIME2 workflow utilized the “qiime cutadapt” module to de-multiplex the Ion Torrent PGM data and to remove primers from both datasets. Clustering was done in QIIME2 using DADA2 followed by 97% clustering using VSEARCH, which is the same methodology used in the “amptk dada2” method. SynMock and BioMock communities were analyzed using “amptk filter” command for each pipeline to map the OTUs to the corresponding mock communities. Error rate for each mock community was calculated as a percent of the total number of mismatches divided by the total number of base pairs. Scripts used for analysis are available at the Open Science Framework repository (https://osf.io/4xd9r/).

In silico analysis of the fungal ITS region

To gain baseline data on potential amplicons of the ITS1 or ITS2 regions, the ITS1 and ITS2 regions were extracted with the “amptk database” command using priming sites specific for each region (ITS1: ITS1-F and ITS2 primer sequences; ITS2: fITS7 and ITS4 primer sequences) from the UNITE+INSD v7.2 database (Abarenkov et al., 2010) consisting of 736,375 ITS sequences. For comparison, the commonly sequenced V3–V4 region was extracted using the “amptk database” command from prokaryotic 16S sequences from the Silva v128 database (Quast et al., 2013). A length histogram for each dataset as well as summary statistics were generated (Fig. 1B; Table 1), indicating that all three of these molecular barcodes have an average length of ∼250 bp (Table 1); however, there was considerable variation in the lengths of the ITS region in comparison to the V3/V4 region of 16S (Fig. 1B). Stretches of homopolymer sequences can also be problematic for some NGS platforms (454 and PGM), and thus the number of sequences in this dataset that contained homopolymer stretches greater than six nucleotides were calculated using the “find_homopolymers.py” script in the AMPtk distribution (Table 1). Given the small percentage of ITS1 and ITS2 regions that are greater than 450 bp (the current upper limit of the Ion Torrent PGM platform), the number of taxa in the reference database that are unlikely to sequence on the Ion Torrent due to amplicon length is relatively small (Table 1). Illumina MiSeq is now capable of paired end 300 bp read lengths (2 × 300); however, reads need to overlap for proper processing in NGS software platforms and thus a ∼500 bp size limit would also be able to sequence most taxa in the reference database using either the ITS1 or ITS2 region.

Existing data processing workflows perform poorly with fungal ITS sequences

We cloned known ITS sequences from 26 cultures from the CFMR culture collection that varied in length (237–548 bp), ranged in GC content (43.8–68.4%), and contained sequences with homopolymer stretches with one sequence containing two 9 bp stretches. These plasmids were combined into BioMock and BioMock-standards as described in materials and methods section. The value of the BioMock-standards is that the library was combined after PCR, and thus the standards are free from PCR-induced artifacts that may arise from PCR amplification of a mixed community. Clustering amplicons into OTUs is common practice in molecular ecology and there are many software solutions/algorithms (such as QIIME (Caporaso et al., 2010), UPARSE (Edgar, 2013), Mothur (Schloss et al., 2009), and DADA2 (Callahan et al., 2016)) that have been developed to deal appropriately with errors associated with NGS platforms. Many studies using 16S amplicon data have focused on comparing clustering methods (Callahan et al., 2016; Edgar, 2013), while others have focused on quality filtering reads prior to clustering (Edgar & Flyvbjerg, 2015). At the time these data were generated (2014–2015), there were very few options for pre-processing data from the Ion Torrent platform; thus we used the compatible 454 method in QIIME v1.9 to de-multiplex the sequencing data (minimum length = 100, maximum length = 550, maximum length homopolymers = 10, maximum primer mismatch = 2). These data were then clustered using UCLUST, SWARM, and USEARCH in QIIME v1.9 as well as UPARSE (usearch v7.0.1090). However the number of OTUs was highly over-estimated for our mock communities and the error rates were very high (Table S2). The best performing clustering pipeline was UPARSE; however the number of OTUs predicted for the 12 member SynMock was unacceptably high, resulting in 82 OTUs with the Ion Torrent PGM data while the 38 OTUs for Illumina MiSeq data were slightly more accurate (Table S2). Based on advances in the UPARSE pipeline and the importance of quality trimming outlined by Edgar & Flyvbjerg (2015), we speculated that these high error rates and inflated OTU counts could be due to the pre-processing of reads (finding barcodes/primers and quality trimming the sequence data) allowing errors in the data. We were unable to run our data through Mothur due to the inability to do a multiple sequence alignment and subsequent distance matrix of the ITS region. It is important to note that with the exception of USEARCH/UPARSE, these software solutions have been built with 16S amplicons in mind and several have been optimized for Illumina data.

One major difference in 16S amplicons versus those of ITS1/ITS2 is that the lengths of 16S amplicons are nearly identical, while ITS1/ITS2 amplicons vary in length (Fig. 1B). The variable length coupled with sequence divergence of the ITS region between diverse taxa, makes the ITS region impossible to align in many cases (Schoch et al., 2012) and thus represents a major limitation in data processing (i.e., sequence alignment is required for default Mothur OTU clustering). To illustrate the importance of properly pre-processing ITS data, we clustered the ITS1 and ITS2 regions using UPARSE while using the full length ITS1/ITS2 UNITE reference database as a benchmark (Fig. 2). Using the UNITE database, we then explored the outcome of trimming/truncating the sequences to different length thresholds, a common practice in OTU clustering pipelines. The UPARSE algorithm uses global alignment and as such terminal mismatches count in the alignment (as opposed to local alignment where terminal mismatches are ignored); thus the UPARSE pipeline expects that reads are truncated to a set length. The original UPARSE algorithm achieves this by truncating all reads to a set length threshold and discards reads that are shorter than the length threshold. Therefore real ITS sequences are discarded (Fig. 2). We then came up with two potential solutions to fix this unintended outcome: (i) truncate reads that were longer than the threshold and keep all shorter reads (full length), and (ii) truncate longer reads and pad the shorter reads with N’s out to the length threshold (padding). Using the UNITE v7.2 database of curated sequences (general release June 28th, 2017) as input, both “full-length” and “padding” improved UPARSE results with the “full length” method recovering more than 99% of the expected OTUs (Fig. 2). It should be noted that the recent version of USEARCH (versions >v8.0.1611) includes a similar method to allow reads to be padded with N’s out to a user-defined length threshold (https://www.drive5.com/usearch/manual/cmd_fastx_truncate.html).

Due to the intrinsic nature of the variable length ITS amplicons, we needed a data processing solution that would be flexible enough to maintain the full length of the reads, trim reads without data loss, prepare sequencing reads for downstream clustering algorithms, handle a large number of sequences, and support all major NGS platforms. Using the mock communities described herein as a means to validate the results of all data processing steps, we wrote a flexible series of scripts for processing Illumina, Ion Torrent, as well as Roche 454 data that are packaged into AMPtk (amplicon tool kit). A flow diagram of AMPtk is illustrated in Fig. 3 and a more thorough description of AMPtk is provided in the material and methods section. A manual for AMPtk is available at http://amptk.readthedocs.io/en/latest/. After data are pre-processed with AMPtk via a platform specific method, AMPtk then functions as a wrapper for several popular algorithms including UPARSE, DADA2, UNOISE2, and UNOISE3. Data presented in Fig. 2 were generated using AMPtk v1.0.1 while the rest of the data were generated using AMPtk v1.2.1.

AMPtk is a fast and accurate HTAS pipeline

Molecular ecology is a rapidly changing field because methods must keep pace with technological advances in DNA sequencing. Therefore, we compared AMPtk to a handful of currently popular HTAS software pipelines: QIIME2 (https://qiime2.org), USEARCH (Edgar, 2013), and PIPITS v2.0 (Gweon Hyun et al., 2015). PIPITS is not a universal HTAS pipeline as it only works with paired-end Illumina data; however, we included it because it uses the ITSx software (Bengtsson‐Palme et al., 2013) to extract the ITS region. For analysis of the Ion Torrent PGM data AMPtk performed the best by recovering all members of the SynMock and BioMock communities, combined with the lowest error rate, the fastest run time (17 min), and the most read counts recovered in the OTU table (Table 2). Both USEARCH and QIIME2 resulted in OTUs that contained either primer or adapter sequence contamination, largely due to incomplete removal of the reverse primer sequence (Table 2). A similar trend was found in processing the Illumina MiSeq data; AMPtk recovered all members of the SynMock and BioMock communities, had low error rates, and recovered the most read counts in the OTU table (Table 3). USEARCH was slightly faster than AMPtk (55 vs 67 min, respectively), however, it had high error rates for the SynMock community (1.047%) as well as missed a member of the SynMock community (Table 3). The mock community members that were missed by USEARCH or QIIME2 were those that were shorter amplicon sequences, i.e., both pipelines missed mock6 (ITS2 region is 161 bp). All pipelines produced more OTUs in each mock community sample than were expected; some of these OTUs are a result of tag-switching and a few others are chimeras that slip through the chimera detection algorithms in UPARSE or DADA2. These OTUs can be filtered out of the dataset using the “amptk filter” command, however, for transparency and to measure the pipeline performance, the raw data are presented (Tables 2 and 3). PIPITS was unable to identify any members of the SynMock community because the non-biological SynMock sequences are not identified by ITSx and thus the SynMock cannot be processed by pipelines that use ITSx as a method to pull out the ITS1 or ITS2 regions (Table 3).

Read abundances do not represent community abundances: PCR introduces bias

Next-generation sequencing platforms are quantitative if the library to be sequenced is unbiased, as is typically the case with RNA-sequencing and whole genome sequencing library prep protocols. However, PCR of mixed communities has long been shown to introduce bias in NGS workflows (Aird et al., 2011; Kebschull & Zador, 2015; Pinto & Raskin, 2012) and use of 16S mock communities resulted in read abundance bias (Bokulich et al., 2013; Edgar, 2017; Kozich et al., 2013). For HTAS this is an important caveat, as molecular ecologists are interested in diversity metrics of environmental communities as well as their relative abundance. Through the use of mock communities, several studies have pointed out that read abundance from fungal HTAS are not representative of relative biological abundance (Amend, Seifert & Bruns, 2010; De Filippis et al., 2017). However, it was recently reported that for a fungal ITS mock community of eight members, abundances were meaningful (Taylor et al., 2016) and correlations between template abundance and read abundance have been previously reported (Ihrmark et al., 2012). Due to disagreements in the literature many studies use abundance-based metrics to analyze HTAS, perhaps due to the unintended consequences of using presence/absence metrics on data that suffer from tag-switching. We reasoned we could investigate this issue using the ITS BioMock artificial community, which would not suffer from bias associated with DNA extraction, ITS copy numbers, and intraspecific variation. We compared the relative read abundances of BioMock-standards to three different combinations of BioMock on both the Ion Torrent PGM and Illumina MiSeq platforms (Fig. 4). The BioMock-standards consist of an equimolar mixture of 26 PCR products thereby removing the PCR bias from mixed DNA samples, while the BioMock communities consist of an equimolar mixture of 23 single-copy plasmids. These data show that even in an extreme example of an equally mixed community of cloned ITS sequences, read abundance does not represent actual abundance in the mock community (Fig. 4). The majority of the bias is introduced at the initial PCR step, as the post-PCR combined BioMock-standards result in a more equal distribution of reads, albeit not a perfect distribution. We also tested PCR conditions, DNA concentrations, and sample reproducibility on the Ion Torrent PGM and in all conditions tested we still observed unequal read abundances (Fig. S1).

While the bias via PCR is consistent between sequencing platforms, there is no obvious correlation between length of the read, GC content, nor stretches of homopolymers affecting efficient PCR amplification. For example, Wolfiporia dilatophya (mock11) contains no homopolymer stretches larger than 5, has GC distribution of 54.6%, and is near the median in length, yet it does not PCR amplify well in the BioMock community (Fig. 4). These data also show a size limitation in the Ion Torrent PGM workflow, as Wolfiporia cocos (mock26) sequences very poorly due to its long ITS2 region of 548 bp (Fig. 4). Three members of the original 26 members of the BioMock community were dropped (mock24, mock25, mock26) due to persistent problems getting them to amplify/sequence in repeated HTAS on the Ion Torrent platform (Fig. S1). While mock26 (W. cocos) was likely difficult to sequence due to its long size, mock24 and mock25 consistently failed to amplify in the equal mixture of plasmids for unknown reasons.

In HTAS experiments, considerable effort is made to try to sequence to an equal depth for each sample. However, in practice this rarely works perfectly and thus a typical HTAS dataset has a 2–4× range in number of reads per sample. The depth of sequence range for the HTAS runs presented here is within a range of 2× for each run and the smallest number of reads per sample in any of our sequencing runs was nearly 60,000 (Table S5). Unequal sequencing depth has been used as rationale for explaining the lack of correlation between read abundance and actual abundance. Therefore, random subsampling of reads in each sample prior to clustering (also called rarefying) has been widely used in the literature, despite a compelling statistical argument that this method is flawed (McMurdie & Holmes, 2014). Randomly subsampling reads for each sample using our BioMock community yielded nearly identical read abundance biases (Fig. S2). Sequencing depth has been shown to be an important variable for HTAS experiments (Smith & Peay, 2014), therefore it has been recommended that a reads per sample cutoff be used with processing environmental datasets (while each dataset is different and should be tested empirically, typically we use 5,000–10,000 as a minimum number of reads per sample).

A non-biological synthetic mock community to measure tag-switching among samples

Tag-switching is a phenomenon that has been described on Roche 454 platform (Carlsen et al., 2012) as well as Illumina platforms (Kircher, Sawyer & Meyer, 2012; Wright & Vetsigian, 2016). A consensus on a mechanism of tag-switching during the sequencing run has yet to be reached. Tag-switching is a significant challenge to overcome as sample crossover has the potential to over-estimate diversity and lead to inaccurate representations of microbial communities, especially considering that read abundance is an unreliable proxy for biological abundance (Fig. 4). Using our BioMock sequencing results, we also discovered this phenomenon on both Ion Torrent and Illumina platforms. We calculated the rate of tag-switching in our BioMock Ion Torrent sequencing run to be 0.033% and on Illumina MiSeq between 0.233% and 0.264%. We also confirmed that tag-switching was happening on the Illumina flow-cell by re-sequencing a subset of Illumina libraries that had shown high tag-switching on the first MiSeq flowcell that did not contain the BioMock (Fig. S3). One problem that we noticed in measuring tag-switching using a mock community of actual ITS sequences (BioMock) was that these same taxa in the mock community could be present in environmental samples, which would lead to inaccurate estimation of tag-switching. In our environmental data, it was likely that at least one of the BioMock members was present in several of the environmental samples, suggesting the calculated tag-switching could be over-estimated. To overcome this problem, we designed a non-biological SynMock community composed of ITS-like sequences that contained conserved priming sites (SSU and LSU regions), ITS1 region, 5.8S region, and an ITS2 region (Fig. 5). We designed the ITS1 and ITS2 portions of the sequences to be non-biological; that is, no similar sequences are known to occur in nature (based on searches of known databases and based on the infinitesimally low probability that a randomly generated sequence would match something found in nature) and therefore these non-biological sequences can be used to accurately track tag-switching in HTAS studies. Using the summary statistics from the analysis of the UNITE reference database for guidance, we also varied the length, GC content, and homopolymer stretches to be representative of real fungal ITS sequences.

The SynMock was tested as a spike-in control on both the Ion Torrent and Illumina MiSeq platforms. The raw data were processed using AMPtk and clustered using UPARSE. These data illustrate that the synthetic sequences are able to be processed simultaneously with real ITS sequences and provide a way to track the level of tag-switching between multiplexed samples (Fig. 6). The increased benefit of being able to track the SynMock sequences as they “bleed” out of the sample allows for a more accurate measurement of tag-switching. Using default Illumina de-multiplexing (allowing one mismatch in the index sequence), tag-switching using the SynMock community was 0.057% (Fig. 6C). To determine if allowing mismatches in the index reads was increasing tag-switching, we reprocessed the data with 0 mismatches and found that tag-switching was reduced to 0.036%. While tag-switching was reduced by nearly half, the tradeoff was that 0 mismatches resulted in approximately 10% fewer reads. For most datasets, a loss of 10% of the sequencing reads should not be problematic, especially if the benefit is to reduce sample mis-assignment in the data. We noted that in our Illumina dual-indexing library prep that there was increased tag-switching on samples that had a shared reverse index (i7), suggesting that errors are increased at later stages of an Illumina sequencing run (Fig. 6B). A similar pattern of increased tag-switching correlating with relaxed primer mismatch settings was observed with Ion Torrent PGM data, although not as drastic. Allowing one mismatch in the barcode resulted in 0.167% tag-switching while allowing 0 mismatches in the barcode resulted in 0.156% tag-switching (Fig. 6C). While these data would suggest that tag-switching is perhaps higher in Ion Torrent PGM datasets, we have subsequently used the SynMock on more than 10 different HTAS Ion Torrent PGM experiments and have since seen much lower levels of tag-switching, occasionally approaching zero.

Many environmental samples can contain hundreds of taxa and thus a legitimate concern is that the 12-member SynMock community does not represent a realistic community in terms of diversity in a sample. To test if the SynMock was able to be recovered in a more complex community, we mixed SynMock together with two environmental samples that had more than 200 OTUs in previous sequencing runs. These mixed samples show that SynMock could be recovered from a complex community and the sequences behave like real ITS sequences (Fig. 6A). While many studies have set a read count threshold to filter “noisy” data from OTU tables, this threshold has been typically selected arbitrarily, i.e., OTUs with read counts less than 100 or less than 10% of the total, etc. Use of the SynMock spike-in control allowed for data-driven thresholds to be measured and moreover for the ability to filter the OTU table based on the calculated tag-switching. The AMPtk filter command calculates tag-switching by mapping the OTUs to the mock community and then provides a way to filter the OTU table based on this calculated value. AMPtk filters across each OTU in the table such that if a read abundance value for a particular sample is below the calculated tag-switching threshold it is converted to zero, and therefore difficult to sequence or “low abundance” OTUs are not indiscriminately dropped. Taken together, these data illustrate the utility of a non-biological SynMock community in parameterizing data processing steps and importantly providing a method in AMPtk to reduce tag-switching from HTAS datasets. AMPtk provides an easy to use method to accurately process variable length amplicons, cluster them into OTUs or denoise sequences, generate an OTU table, filter the OTU table for tag-switching, and assign taxonomy.