In the past 10 years, metabarcoding and metagenomics based on DNA sequencing and subsequent taxonomic assignment have become an important approach for understanding diversity and community organization at many taxonomic levels. This has led to the publication of more than 80 taxonomic classification methods (Gardner et al., 2019). There are three major strategies in classification methods: (1) composition-based, which do not align sequences but extract compositional features (e.g., kmers) to build models of probabilistic taxonomic inclusion; (2) alignment-based, which rely on alignments to directly compare query sequences to reference sequences but do not use trees; and (3) phylogenetic-based, which rely on a phylogenetic tree reconstruction method, in addition to alignments, to perform a placement of the query onto the tree. As a trade-off between speed and precision for processing next-generation sequencing (NGS) data, the vast majority of recent classification methods have either relied on alignment-based or composition-based strategies.

Composition-based tools reduce the reference database by indexing compositional features such as kmers for a rapid search of the database. These methods require an exact match between the kmer in the query sequence and the kmer in the reference database. As a result of hash indexing of kmers, kraken2 (Wood et al., 2019), for example, can classify >1 million reads within 1 min using the entire Geengenes or SILVA databases (Lu and Salzberg, 2020). Alignment-based tools use a fast local aligner such as BLAST (Altschul et al., 1990) to pairwise align queries to the reference database and define a score based on sequence similarity in the alignment between the read and reference sequence. However, alignment-based methods can be many orders of magnitude slower than composition-based tools since datasets with >10 million reads require weeks of BLASTN running time (Ainsworth et al., 2017). In both composition-based tools and alignment-based tools, a lowest common ancestor (LCA) algorithm is then typically used to assign at different taxonomic levels (Figure 1A). LCA works by assigning to the smallest possible clade that include all matches with a similarity less than the specified cut-off.

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Phylogenetic placement methods place a query sequence onto a phylogenetic tree of reference sequences. This placement requires a full multiple sequence alignment (MSA) of the reference sequences and a subsequent estimation of a phylogenetic tree. However, large datasets with high rates of evolution are hard to align accurately (Sievers et al., 2011) and phylogeny estimation methods produce poor trees when MSAs are not of high quality (Kapli et al., 2020). Furthermore, phylogenetic placement tends to be computationally demanding as both running time and memory usage scale linearly with the size of the reference database (Balaban et al., 2020). Even for reference databases that contain sequences as few as 1600 sequences, assignment for a single query using the most cited phylogenetic placement method, pplacer (Matsen et al., 2010), takes more than 7 min and requires over 10 GB of RAM (on a Dell PowerEdge server with 32 CPU threads and 512 GB of RAM). At this rate, a reference database that contains a metabarcode such as cytochrome oxidase 1 (COI) that has at least 1.5 million reference sequences, assigning just a single query would require 20.9 hr and 2.37 TB RAM. Scaling the query size to millions of queries would therefore be computationally intractable.

To address these challenges, the most recent implementations of phylogeny-based methods (Barbera et al., 2019) rely on reference database reduction techniques (i.e., using only representative taxa or consensus sequences for a sparse backbone tree) to handle the large amount of data that is routinely produced. Often a single species is selected to represent an entire clade (Czech et al., 2022). While this reduces the computational cost, it also reduces the granularity, and potentially the accuracy, of the assignments. As a trade-off between speed and precision, the vast majority of recent classification methods are either alignment-based or composition-based approaches (Hleap et al., 2020) since phylogeny-based methods have not scaled to handle the entirety of the rapidly growing reference databases of genome markers and the increasingly large amounts of NGS data.

Here, we describe a new method for phylogenetic placement, implemented in the program ’Tronko’ (https://github.com/lpipes/tronko, Mapper and Pipes, 2024), the first phylogeny-based taxonomic classification method designed to truly enable the use of modern-day reference databases and NGS data. The method is based on approximating the phylogenetic likelihood calculation by (1) only allowing the edge connecting the reference sequence to the tree to join at existing nodes in the tree and then (2) approximating the likelihood using a probabilistically weighted mismatch score based on pre-calculated fractional likelihoods stored in each node (see ‘Methods’). We argue that (2) approximates the full maximized likelihood assignment without requiring any numerical maximization under the approximating assumption that the read joins the tree in an existing node with a zero-length branch. The approximation is equivalent to calculating the expected average mismatch to each node in the phylogeny. The assignment method in Tronko uses the LCA criteria but, unlike composition-based and alignment-based approaches (Figure 1A), takes advantage of fractional likelihoods stored in all nodes of the tree with a cut-off that can be adjusted from conservative to aggressive (Figure 1B). In the simplest case, when the reference sequences form a single tree, Tronko uses a pre-calculated MSA, the phylogenetic tree based on the MSA, and pre-calculated posterior probabilities, which are proportional to the fractional likelihoods. However, in more typical cases, when a single tree/MSA is unsuitable for analyses, as the reference sequences encompass increasingly divergent species as well as an increasing volume of sequences, we present a fully customizable divide-and-conquer method for reference database construction that is based on dividing reference sequences into phylogenetic subsets that are realigned and with local trees re-estimated.

The construction of the database, MSAs, and trees facilitates fast phylogenetic assignment. The assignment algorithm then proceeds by (1) a BWA-MEM (Li and Durbin, 2009) search on all sequences in the database, (2) a pairwise sequence alignment between the query and the top hit in each alignment subset containing a BWA-MEM hit using either the Needleman–Wunsch algorithm (Needleman and Wunsch, 1970) or the wavefront alignment algorithm (Marco-Sola et al., 2021), and (3) a calculation of a score based on the approximate likelihood for each node in subsets with a BWA-MEM hit. An additional LCA assignment for all subsets can then be applied to summarize the results. For full details, see ‘Methods’.

To compare the new method (Tronko) to previous methods, we constructed reference databases for COI and 16S for common amplicon primer sets using CRUX (Curd et al., 2019; sSee ‘Methods’ for the exact primers used). We first compared Tronko to pplacer and APPLES-2 for reference databases containing a reduced amount of sequences (<1600 sequences) to compare the speed and memory requirements with comparable phylogenetic-based assignment methods. Tronko shows speed-ups >20 times over pplacer, with a vastly reduced memory requirement illustrating the computational advantage of the approximations in Tronko (Figure 1—figure supplement 2). Tronko demonstrates a speed-up >2 times over APPLES-2 with a similar memory footprint. In terms of accuracy, all methods had a 100% true-positive rate at the species level. Additionally, in terms of the species assignment rate (the percentage of queries that were assigned at the species level), Tronko assigns the most queries.

Next, in addition to pplacer and APPLES-2, we evaluated Tronko’s performance to kmer-based kraken2 (Wood et al., 2019), which previously has been argued to have the lowest false-positive rate (Lu and Salzberg, 2020), and two other popular alignment-based methods: MEGAN (Huson et al., 2007) and metaphlan2 (Truong et al., 2015). We used two types of cross-validation tests: leave-one-species-out and leave-one-individual-out analyses. The leave-one-species-out test involves removing an entire species from the reference database, simulating NGS reads from that species, and then attempting to assign those reads with that species missing from the database. The leave-one-individual-out test involves removing a single individual from the reference database, simulating NGS reads from that individual, and then attempting to assign those reads with that individual missing from the database. In both tests, singletons (i.e., cases in which only one species was present in a genera or cases in which only one individual represented a species) were exempt from the tests.

We performed a leave-one-species-out test comparing Tronko (with LCA cut-offs for the score of 0, 5, 10, 15, and 20 with both Needleman–Wunsch alignment and wavefront alignment) to kraken2, metaphlan2, and MEGAN for 1467 COI sequences from 253 species from the order Charadriiformes using 37,515 (150 bp × 2) paired-end sequences and 768,807 single-end sequences (150 bp and 300 bp in length) using 0, 1, and 2% error/polymorphism (Figure 2). We use the term ‘error/polymorphism’ to represent a simulated change in nucleotide that can be either an error in sequencing or a polymorphism. We display confusion matrices to display the clades in which each method has an incorrect assignment (Figure 3). See Figure 2—figure supplement 1 for results with the wavefront alignment algorithm (Marco-Sola et al., 2021).

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Figure 3

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Using leave-one-species-out and simulating reads (both paired-end and single-end) with a 0–2% error (or polymorphism), Tronko detected the correct genus more accurately than the other methods even when using an aggressive cut-off (i.e., when cut-off ${\displaystyle =}$ 0) (Figure 3F). Using 150 bp paired-end reads with 1% error, Tronko had a misclassification rate of only 9.8% with a recall rate of 70.1% at the genus level using a cut-off set to 15 while kraken2, MEGAN, and metaphlan2 had misclassification rates of 33.5, 10.0, and 27.7%, respectively, with recall rates of 90.6, 52.1, and 95.0% (see Figure 2B). Tronko had a lower misclassification rate relative to the recall rate out of all methods for 150 bp × 2 paired-end reads with 0% error/polymorphism (Figure 2A), 1% error/polymorphism (Figure 2B), and 2% error/polymorphism (Figure 2 and Figure 3D–I), for 150 bp reads with 0% error/polymorphism (Figure 2D), 1% error/polymorphism (Figure 2E), and 2% error/polymorphism (Figure 2F), and for 300 bp reads with 0% error/polymorphism (Figure 2G), 1% error/polymorphism (Figure 2H), and 2% error/polymorphism (Figure 2I). See ‘Methods’ for definitions of recall and misclassification rates. Tronko also accurately assigned genera from the Scolopacidae family (top left of matrices in Figure 3) using Needleman–Wunsch with a cut-off of 10 compared to kraken2, metaphlan2, and pplacer.

Next, we performed a leave-one-individual-out test for the same COI reference sequences using 746,352 single-end reads and 36,390 paired-end reads (Figure 4, Figure 4—figure supplement 1F–J). See Figure 4—figure supplement 2 for results with wavefront alignment algorithm (Marco-Sola et al., 2021). Using single-end reads of lengths 150 bp and 300 bp, Tronko has a lower misclassification rate and higher recall rate than kraken2, metaphlan2, and MEGAN. Using 150 bp paired-end reads with 0% error (Figure 4D), Tronko had a misclassification rate at only 0.1% with a recall rate of 58.6% at the species level using a cut-off set to 10 while kraken2, MEGAN, and metaphlan2 had misclassification rates of 1.5, 0.1, and 11.0%, respectively, with recall rates of 85.4, 60.7, and 98.14%. Both metaphlan and kraken2 have a number of mis-assignments within the family of Laridae (see blue points across the diagonal in Figure 4—figure supplement 1A and B) and Tronko is able to accurately assign species within this family or assign at the genus or family level. We also observe that for increasing error rates, kraken2 and metaphlan2 have a substantial increase in misclassification rate. We believe it would be slightly misleading to display results for pplacer and APPLES-2 here due to the lack of an implementation to calculate the LCA on similar likelihoods. See Figure 4—figure supplement 2 for results for pplacer and APPLES-2 along with wavefront alignment algorithm.

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In order to replicate real-world scenarios, we added a leave-one-species-out test using 16S from 2323 bacterial species and 5000 individual sequences (Figure 5). We selected the sequences for the 16S dataset by grouping the sequences by the class level in a random order, rotating that order, and randomly selecting an individual sequence from each group. We then simulated sequencing reads from the dataset simulating 21,947,613 single-end reads (150 bp and 300 bp in length) as well as 21,478,738 paired-end 150 bp × 2 reads. In all simulations at both the genus and family levels, Tronko had a higher recall and a lower misclassification rate than all other methods. The simulations for 300 bp single-end reads are not directly comparable to the 150 bp single-end or paired-end reads because only 105 missing-out tests out of 2310 were able to be performed because most reference sequences were <300 bp in length. We only display the results for 300 bp single-end reads for APPLES-2 in the supplement as we believe the results are not a good representation of the method. See Figure 5—figure supplement 1 for results for APPLES-2 using 300 bp single-end reads, along with results using the wavefront alignment algorithm. Additionally, we tested the use of hmmer or MAFFT for alignments with APPLES-2 and pplacer (Figure 5—figure supplement 2), and we did not observe any substantial difference with the choice of alignment.

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We then compared Tronko’s performance to kraken2, MEGAN, and metaphlan2 using mock communities for both 16S (Schirmer et al., 2015; Gohl et al., 2016) and COI markers (Braukmann et al., 2019; Figure 6). We did not compare mock community data to pplacer and APPLES-2 because we were unsuccessful in building a full MSA for our 16S and COI reference databases. Tronko also relies on sequence alignments, but as described in ‘Methods’, they can be handled by dividing sequences into clusters in the Tronko pipeline. For 16S, we used three different mock community datasets. We used 1,054,868 2 × 300 bp Illumina MiSeq sequencing data from a mock community consisting of 49 bacteria and 10 archaea species from Schirmer et al., 2015, 54,930 2× 300 bp Illumina MiSeq sequencing data from a mock community consisting of 14 bacteria species from Lluch et al., 2015, and 206,696 2 × 300 bp Illumina MiSeq sequencing data from a mock community of 20 evenly distributed bacterial species from Gohl et al., 2016. For the data from Schirmer et al., 2015, at the species level, Tronko had a <0.6% misclassification rate at every cut-off with a recall rate of 11.0% at cut-off 0 (Figure 6A; see Figure 6—figure supplement 1 for plot without outliers). kraken2 had a misclassification rate of 1.2% with a recall rate of 10.6% when using its default database, and a misclassification rate of 3.5% and a recall rate of 35.1% when using the same reference sequences as Tronko. metaphlan2 did not have any assignments at the species, genus, or family level using the default database, and it had an 8.3% misclassification and 8.9% recall rate at the species level when using the same reference sequences as Tronko. MEGAN had a recall rate of 0.2% and a misclassification rate of 0% at the species level.

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For the data from Lluch et al., 2015, at the genus level, Tronko had a misclassification rate of 0.6% and a recall rate of 22.3% using a cut-off of 0, while all other methods had a misclassification rate of >8% (see Figure 6—figure supplement 2 for a close-up of the rates).

For the data from Gohl et al., 2016, at the species level, Tronko had a <2.6% misclassification rate at every cut-off with a recall rate of 12.8% at cut-off 0 (Figure 6C; see Figure 6—figure supplement 3 for plot without outliers). kraken2 had a misclassification rate of 26.8% and recall rate of 33.7% when using its default database, and a misclassification rate of 21.4% and recall rate of 25.4% when using the same reference sequences as Tronko. metaphlan2 did not have any assignments at the species, genus, or family level using the default database, and it had an 8.5% misclassification and 2.1% recall rate at the species level when using the same reference sequences as Tronko. MEGAN had a misclassification rate of 0% and a recall rate of 4.4% at the species level.

For COI, we used a dataset from Braukmann et al., 2019 which consists of 646,997 2 × 300 bp Illumina MiSeq sequencing data from 374 species of terrestrial arthropods, which is the most expansive mock community dataset that we used. At the genus level, Tronko had a misclassification rate of <0.5% with a recall rate of 78.3% at the cut-off of 0 (Figure 6D; see Figure 6—figure supplement 4 for plot without outliers). With the default database, kraken2 had a misclassification rate of 40.5% with a recall rate of 6.5%. With the same reference sequences as Tronko, kraken2 still had a misclassification of 14.0% with a recall rate of 83.1%. metaphlan2 had a misclassification rate of 3.5% with a recall of 86.4% with the same reference sequences as Tronko while the default database failed to assign any reads. MEGAN had a 15.0% recall and 0% misclassification rate at the species level and a 49.9% recall and 0.5% misclassfication rate at the genus level.

We compared Tronko with kraken2, metaphlan2, and MEGAN (using BLAST as the aligner) for running time (Figure 7A) and peak memory (Figure 7B) using 100, 1000, 10,000, 100,000, and 1,000,000 sequences using the COI reference database. Unsurprisingly, kraken2 had the fastest running time followed by metaphlan2, but MEGAN had a substantially slower running time than all methods. Tronko was able to assign 1,000,000 queries in ∼8 hr with the choice of aligner being negligible. Tronko had the highest peak memory (∼50 GBs) as it stores all reference sequences, their trees, and their posterior probabilities in memory. We note that for very large databases, the memory requirements can, in theory, be reduced by processing different alignment subsets sequentially.

Figure 7

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Both leave-one-species-out and leave-one-individual-out simulations show that Tronko recovers the correct taxonomy with higher probability than competing methods and represents a substantial improvement over current assignment methods. The advantage of Tronko comes from the use of limited full-sequence alignments and the use of phylogenetic assignment based on a fast approximation to the likelihood.

We evaluate Tronko using different cut-offs representing different trade-offs between recall and misclassification rate, thereby providing some guidance to users for choice of cut-off. We note that in most cases the other methods evaluated here fall within the convex hull of Tronko, showing that Tronko dominates those methods, and in no cases do other methods fall above the convex hull of Tronko. However, in some cases, other methods are so conservative, or anti-conservative, that a direct comparison is difficult. For example, when using single-end 300 bp reads (Figure 4G–I), MEGAN has assignment rates that are so low that a direct comparison is difficult.

Among the methods compared here, kraken2 is clearly the fastest (Figure 7A). However, it generally also has the worst performance with a higher misclassification rate than other methods, especially in the leave-one-species-out simulations (Figure 2).

Both metaphlan2 and MEGAN tend to fall within the convex hull of Tronko. Typically, metaphlan2 assigns much more aggressively, and therefore, has both a recall and misclassification rate that is much higher than MEGAN, which assigns very conservatively. We also note that the computational speed of MEGAN is so low that it, in some applications, may be prohibitive (Figure 7A).

We evaluated Tronko using two different alignment methods: Needleman–Wunsch and wavefront alignment. In many cases, the two alignment algorithms perform similarly. However, in the case, where short, single-end reads are used (i.e., 150 bp single-end reads), the wavefront alignment performs worse than the Needleman–Wunsch alignment (see Figure 2—figure supplement 2D–F and Figure 4—figure supplement 2D–F). The wavefront alignment algorithm implements heuristic modes to accelerate the alignment, which performs similarly to Needleman–Wunsch when the two sequences being aligned are similar in length. However, when there is a large difference between the two sequences being aligned, we notice that the wavefront alignment forces an end-to-end alignment which contains large gaps at the beginning and end of the alignment. Hence, based on current implementations, we cannot recommend the use of the wavefront alignment for assignment purposes of short reads, although this conclusion could change with future improvements in the implementation of the wavefront alignment algorithm.

Tronko is currently not applicable to eukaryotic genomic data as it requires well-curated alignments of markers and associated phylogenetic trees, although we note that whole-genome phylogenetic reference databases for such data could potentially be constructed. Such extensions of the use of Tronko would require heuristics for addressing the memory requirements. Tronko currently has larger memory requirements than methods that are not phylogeny-based. Nonetheless, for assignment to viruses, amplicon sequencing, and other forms of non-genomic barcoding, Tronko provides a substantial improvement over existing assignment methods and is the first full phylogenetic assignment method applicable to modern large datasets generated using NGS.

The methods presented in this article are implemented in the Tronko software package that includes Tronko-build and Tronko-assign for reference database building and species assignment, respectfully. Tronko can be downloaded at https://github.com/lpipes/tronko and is available under an open-software license (Mapper and Pipes, 2024).

The identified reference databases, MSAs, phylogenetic trees, and posterior probabilities of nucleotides in nodes for COI and 16S are available for download at https://doi.org/10.5281/zenodo.13182507.

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Author details

1. Lenore Pipes

   Department of Integrative Biology, University of California, Berkeley, Berkeley, United States

   Contribution

   Conceptualization, Software, Formal analysis, Funding acquisition, Validation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   lpipes@berkeley.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0056-8045

2. Rasmus Nielsen

   1. Department of Integrative Biology, University of California, Berkeley, Berkeley, United States
   2. GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Conceptualization, Software, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

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