sppIDer: A Species Identification Tool to Investigate Hybrid Genomes with High-Throughput Sequencing

Abstract

The genomics era has expanded our knowledge about the diversity of the living world, yet harnessing high-throughput sequencing data to investigate alternative evolutionary trajectories, such as hybridization, is still challenging. Here we present sppIDer, a pipeline for the characterization of interspecies hybrids and pure species, that illuminates the complete composition of genomes. sppIDer maps short-read sequencing data to a combination genome built from reference genomes of several species of interest and assesses the genomic contribution and relative ploidy of each parental species, producing a series of colorful graphical outputs ready for publication. As a proof-of-concept, we use the genus Saccharomyces to detect and visualize both interspecies hybrids and pure strains, even with missing parental reference genomes. Through simulation, we show that sppIDer is robust to variable reference genome qualities and performs well with low-coverage data. We further demonstrate the power of this approach in plants, animals, and other fungi. sppIDer is robust to many different inputs and provides visually intuitive insight into genome composition that enables the rapid identification of species and their interspecies hybrids. sppIDer exists as a Docker image, which is a reusable, reproducible, transparent, and simple-to-run package that automates the pipeline and installation of the required dependencies (https://github.com/GLBRC/sppIDer; last accessed September 6, 2018).

Introduction

Interspecies hybrids play a large role in both natural and in industrial settings (Dunn and Sherlock 2008; Soltis et al. 2015; Payseur and Rieseberg 2016; Peris, Pérez-Torrado, et al. 2018). However, identification and characterization of the genomic contributions of hybrids can be difficult. In the modern genomic era, techniques using high-throughput sequencing can be used to address many of the barriers to identifying and characterizing hybrids. Short of complete de novo genome assembly, potential analytical methods to analyze these data fall into two major categories. Methods adapted from population genetics require alignment and variant-calling to a representative reference genome. Alignment-free phylogenetic methods have also been developed.

Some commonly used methods to detect interspecies hybrids have been adapted from methods developed for intraspecies diversity, such as F_ST, STRUCTURE analysis, phylogenetic discordance, linkage disequilibrium, and Principal Component Analysis approaches (Pritchard et al. 2000; Henn et al. 2012; Lawson et al. 2012; Payseur and Rieseberg 2016). There are numerous potential drawbacks to using these methods to detect interspecies hybrids. For example, most definitions of speciation require the cessation of gene flow and the accumulation of sequence divergence well beyond the levels observed between populations, which are therefore beyond the expectations of most of these approaches. Many of these methods were also developed for diploid obligately outcrossing species, which makes problematic their application to allopolyploids or species that primarily undergo selfing or other forms of inbreeding. Indeed, the basic assumptions of these methods, including gene flow, demographic history, and natural selection, are violated by most interspecies hybrids. For interspecies hybrids of highly divergent parents, one might expect additional biases would be introduced by the choice of reference genome, but to the best of our knowledge, their performance on this type of data has not been formally explored.

To avoid the drawbacks of limited reference genomes, several new phylogenetic approaches have been developed that do not require sequence alignments or whole-genome assemblies, such as phylogeny-building approaches using kmers (Fan et al. 2015), de novo identification of phylogenetically informative regions (Schwartz et al. 2015), and local assemblies of target genes (Allen et al. 2015; Johnson et al. 2016). These methods can accurately reconstruct known and simulated phylogenies of pure lineages. However, these methods have not been tested on hybrid or admixed lineages. As hybrids are the result of an outcrossing event between two independently evolving lineages, their origin is inherently not tree-like. Therefore, placing hybrids on a bifurcating tree will not reflect the topology observed with pure lineages. Placing hybrids on a phylogenetic network is more apt, but this approach has not been tested with alignment-free phylogenetic approaches. Other species identification methods based on local assembly of target genes could lead to erroneous identification, depending on which parent the gene of interest is retained from in the hybrid, or could lead to the assembly of a chimeric gene if the hybrid has retained copies from multiple parents. Therefore, applying alignment-free phylogenetic methods in organisms, such as hybrids with complex genomes, could potentially lead to misleading conclusions.

With the influx of sequencing data, the quality and number of reference genomes available is increasing at a rapid pace. Population genomic, ecological diversity, and gene expression projects are underway in many fields. These studies are yielding a high volume of short-read data, but determining the best way to leverage these data can be challenging. A key goal of the modern genomic era is to be able to integrate and synthesize these data to further our understanding of natural diversity (Richards 2015), including about the frequency and genomic identities of hybrid and admixed lineages.

Here, we present sppIDer as a novel, assumption-free method that rapidly provides visual and intuitive insight into ancestry genome-wide, which will aid in the discovery and characterization of interspecies hybrids. This method maps short-read data to a combination genome, built from available reference genomes chosen by the user. sppIDer allows for the analysis and visualization of the genomic makeup of a single organism of interest, facilitating the rapid discovery of hybrids and individuals with other genomic features of interest, such as aneuploidies and introgressions. Therefore, sppIDer is a method that provides unique and intuitive insights into complex genomic ancestry and regions of differing evolutionary history, which can complement existing methods in the characterization of hybrids.

New Approaches

Here, we describe and make available a user-friendly short-read data analysis pipeline that utilizes existing bioinformatic tools and custom scripts to determine species identity and hybrid status. Short reads are mapped to a combination reference genome of multiple species of interest, and the output is parsed for where, how well, and how deeply the reads map across this combination genome. A colorful automated output allows end users to rapidly and intuitively assess the genomic contribution, either from a single species or multiple species, and relative ploidy of an organism. Genomes with disproportionally high counts of reads mapping can be detected statistically, while smaller introgressions can be identified though coverage analysis. Figure 1 illustrates the basic workflow in a flow chart of each step. An upstream step creates a combination reference genome, which is a concatenation of reference genomes of interest, before the main pipeline is run. The main pipeline starts with mapping short-read data to this combination reference genome. Then, this output is parsed for percentage and quality of reads that map to each individual reference genome within the combination reference and percentage of unmapped reads; this summary is then plotted so these metrics can be visualized, and genomes with disproportionate mapping are statistically identified. In parallel, the mapping output is analyzed for depth of coverage. Reads with a mapping quality (MQ) >3 are retained and sorted into the combination reference genome order; then, coverage across the combination reference genome is computed. A custom script then calculates the mean coverage for each species, and the combination reference genome broken into windows. The output of these analyses is then used to analyze and identify the peaks in coverage distribution, and the coverage across the combination reference genome can be plotted and visualized.

Fig. 1.

Workflow of sppIDer. (a) An upstream step concatenates all the desired reference genomes (represented by colored bars). Generally, references should be distinct species (see Materials and Methods for advice about choosing references). This combination reference genome can be used for many analyses. (b) The main sppIDer pipeline. First, reads (short lines) are mapped. This output is used to parse for quality and percentage (left) or for coverage (right). On the left, quality (high MQ black lines versus low MQ light lines) is parsed, and the percentage of reads that map to each genome or do not map (gray bar) is calculated. To determine coverage, only MQ > 3 reads (black lines) are kept and sorted into the combination reference genome order. These reads are then counted, either for each base pair or, for large genomes (combination length >4 Gb), in groups. Then, the combination reference genome is broken into equally sized pieces, and the average coverage is calculated. (c) Several plots are produced. Shown here are examples of Percentage Mapped and Mapping Quality plots, a plot showing average coverage by species, plots of coverage distributions, and two ways to show coverage by windows with species side-by-side or stacked. Scer, Saccharomyces cerevisiae; Spar, S. paradoxus; Smik, S. mikatae; Skud, S. kudriavzevii; Sarb, S. arboricola; Suva, S. uvarum; Seub, S. eubayanus.

We have given this computational pipeline and wrapper a portmanteau of the pluralized abbreviation of species (spp.) and identifier (IDer), to reflect its ability to identify hybrids of multiple species. sppIDer also detects chromosomal and partial-chromosomal copy-number variants (C/CNVs), such as those caused by aneuploidy and other genomic changes that do not meet the textbook definition of aneuploidy, including interspecies loss-of-heterozygosity events, interspecies unbalanced translocations, and other differences in relative ploidy. sppIDer is provided as an open source Docker (http://www.docker.com; last accessed September 6, 2018), which organizes the pipeline and all the dependencies into a reusable, reproducible, transparent, and simple-to-run package (https://github.com/GLBRC/sppIDer; last accessed September 6, 2018).

Here, we present several applications of sppIDer in yeast, plant, and animal genomes. Through simulations, we show that sppIDer can detect hybrids of closely or distantly related species, and of recent or ancient origin. We use the genus Saccharomyces to 1) detect both interspecies hybrids and pure strains; 2) detect hybrids, even with missing reference genomes; and 3) determine how divergent lineages and poor-quality data and reference genomes affect sppIDer’s performance. Next, we test sppIDer’s utility in non-Saccharomyces systems: another yeast genus, Lachancea; an animal genus, Drosophila; and a plant genus, Arabidopsis. Finally, we test an extension for non-nuclear DNA using mitochondrial genome data. Overall, sppIDer is robust to many different inputs and can be used across organisms to provide rapid insight into the species identity, hybrid status, and C/CNVs of an organism.

Results and Discussion

Species and Interspecies Hybrid Identifications

To test sppIDer, we first used the well-studied genus Saccharomyces (Hittinger 2013). Seven of the eight species have reference genomes scaffolded at a near-chromosomal level, and there are many interspecies hybrids (Goffeau et al. 1996; Fischer et al. 2000; Dunn and Sherlock 2008; Liti et al. 2009; Scannell et al. 2011; Liti et al. 2013; Baker et al. 2015; Naseeb et al. 2017; Peris, Pérez-Torrado, et al. 2017). To test sppIDer’s species-level classification ability for a natural isolate, we used the short-read data available for a Saccharomyces eubayanus strain isolated in New Zealand (P1C1) (Gayevskiy and Goddard 2016). The reads from this wild S. eubayanus strain mapped preferentially to the S. eubayanus reference genome (fig. 2a) and were statistically confirmed (supplementary table S1, Supplementary Material online). This preferential mapping can be visualized as normalized coverage only being above zero (nonnormalized mean coverage 28.5×) for the S. eubayanus genome. This strain belongs to the same diverse lineage as the reference strain (Peris et al. 2016), but as the first isolate from Oceania, these results show that sppIDer can easily classify, to the species level, a divergent wild strain isolated from a novel environment. To test sppIDer’s utility for industrial strains, we used short reads from an ale strain, Fosters O (Gonçalves et al. 2016). This test shows that this brewing strain is a pure species; the S. cerevisiae genome is the only genome that had normalized coverage above zero (non-normalized mean coverage 52.9×) (fig. 2b and supplementary fig. S1b, Supplementary Material online) and was statistically confirmed (supplementary table S1, Supplementary Material online).

Fig. 2.

Normalized coverage plots of Saccharomyces test cases. (a) Reads from a New Zealand isolate of S. eubayanus, P1C1, mapped to the S. eubayanus reference genome (magenta). (b) Reads from an ale strain, FostersO, mapped to the S. cerevisiae reference genome (red), with visually detectable aneuploidies. (c) Reads from a hybrid Frohberg lager strain, W34/70, mapped to both the S. cerevisiae and S. eubayanus reference genomes in an average approximately 1:1 ratio with visually detectable translocations and aneuploidies. (d) Reads from a hybrid Saaz lager strain, CBS1503, mapped to both S. cerevisiae and S. eubayanus reference genomes in an average approximately 1:2 (respectively) ratio with visually detectable translocations and aneuploidies. (e) Reads from a wine hybrid strain, Vin7, mapped to S. cerevisiae and S. kudriavzevii (green) reference genomes in an average approximately 2:1 (respectively) ratio. (f) Reads from a hybrid cider-producing strain, CBS2834, mapped to four reference genomes: S. cerevisiae, S. kudriavzevii, S. uvarum (purple), and S. eubayanus.

To test sppIDer’s ability to delineate hybrids, we used short-read data from two S. cerevisiae × S. eubayanus lager yeast lineages, Saaz (strain CBS1503) and Frohberg (strain W34/70). The known parentage of S. cerevisiae and S. eubayanus was statistically confirmed, and only these genomes produced positive residuals from χ² tests of the mapped data (supplementary table S1, Supplementary Material online). By analyzing the distribution of coverage, we binned regions of the genomes with similar coverages, drawing boundaries based on local minima and maxima. These coverage bins roughly corresponded to the known relative ploidies and rearrangements (supplementary fig. S1c and d, Supplementary Material online) (Dunn and Sherlock 2008). Specifically, the Frohberg lineage contains approximately two copies of each chromosome from both S. cerevisiae and S. eubayanus. As expected, the average normalized coverage across both the S. cerevisiae and S. eubayanus genomes was approximately at the same level (non-normalized mean coverage 16.6×) (fig. 2c and supplementary fig. S1c, Supplementary Material online). In our test with a representative of the Saaz lineage, sppIDer detected three coverage bins where a majority of the data fell: 52.45% of the S. cerevisiae and 4.51% of the S. eubayanus windows fell below the first local minimum (no coverage); 33% of the S. cerevisiae and 10.67% of the S. eubayanus windows fell into the first bin (likely haploidy); 9.99% of the S. cerevisiae and 37.81% of the S. eubayanus windows fell into the second bin (likely diploidy); and 4.03% of the S. cerevisiae and 42.56% of the S. eubayanus windows fell into the third bin (likely triploidy) (fig. 2d, supplementary fig. S1d and table S1, Supplementary Material online). Overall, these estimates match with previous observations that the Saaz lineage is approximately haploid for the S. cerevisiae genome and diploid for the S. eubayanus genome. We also estimated four distinct ploidy states that correspond to the previously described aneuploidies and translocations (supplementary fig. S1d, Supplementary Material online) (Dunn and Sherlock 2008; Okuno et al. 2016).

As an additional hybrid test, we used short-read data from the wine strain Vin7, a S. cerevisiae × S. kudriavzevii hybrid. In this test, only S. cerevisiae and S. kudriavzevii produced positive residuals (supplementary table S1, Supplementary Material online). From the normalized coverage plot (fig. 2e), we could determine that Vin7 has retained complete copies of both parental genomes, but at different ploidy levels; 98.71% of the S. kudriavzevii genomes fell into the first coverage bin, and 91.07% of the S. cerevisiae genome fell into the second bin (supplementary fig. S1e, Supplementary Material online). Here, we could infer that this strain has double the number of copies of S. cerevisiae chromosomes as it does of S. kudriavzevii chromosomes. Although exact ploidy cannot be measured without direct measures of DNA content, the inferred ploidy is consistent with previous studies (Borneman et al. 2012, 2016; Peris et al. 2012).

As a final test of interspecies hybrids, we used data from the cider strain CBS2834 (Almeida et al. 2014). Here, sppIDer detected large genetic contributions from S. cerevisiae, S. kudriavzevii, and S. uvarum, all of which were statistically supported (supplementary table S1, Supplementary Material online). CBS2834 also contains introgressed contributions from S. eubayanus that were not detected as positive residuals (fig. 2f, supplementary fig. S1d and table S1, Supplementary Material online). Although the S. eubayanus genetic contribution is quite small, seen on chromosomes XII and XIV, it was still easily detected in the coverage analysis by sppIDer, where 7.02% of the S. eubayanus genome had coverage greater than the first local minimum (supplementary fig. S1f and table S1, Supplementary Material online). Although marginal cases should always be investigated by formal phylogenetic analyses, these examples show that sppIDer can easily detect higher-order interspecies hybrids, even those with minor contributions from several species.

Testing the Limits of sppIDer with a Simulated Phylogeny

To test sppIDer’s performance with hybrids of varying levels of parental divergence, we used a simulated phylogeny. To build this phylogeny we started with the S. cerevisiae reference genome and produced a phylogeny of ten species through several rounds of simulating short-read sequencing data, applying a set mutation rate, and assembling those reads. For these simulated genomes, sister species were ∼4% diverged, and the most distantly related species were ∼20% diverged (fig. 3a). We also created a phylogeny with six species where sister species pairs were each ∼1% diverged, and the most divergent species were ∼3% diverged (supplementary fig. S2a, Supplementary Material online). These simulated phylogenies allowed us to test pseudo-hybrids from closely and distantly related lineages. Further, the iterative process of phylogeny building allowed us to create ancient pseudo-hybrids that simulated the result from hybridization of a common ancestor predating a lineage split. sppIDer accurately mapped pure lineages to their corresponding reference genome (fig. 3b). For all ten species in the more diverse phylogeny and all six species in the closely related phylogeny, >90% of the reads mapped to their corresponding reference genome. The read simulation and assembly process resulted in varying quality final references, but despite differences in genome quality, all reads still mapped accurately and were not biased to the best reference genome.

Fig. 3.

Simulated phylogeny of ten species and sppIDer’s detection of hybrids from this phylogeny. (a) Phylogeny built with AAF. (b) Reads from G mapped to the G reference genome. (c) Reads from a pseudo-hybrid of the closely related species G and H mapped to the G and H references. (d) Reads from a more distant pseudo-hybrid of E and G mapped to references E and G. (e) Reads of an ancient pseudo-hybrid of A and a common ancestor of G and H mapped to the references of A, G, and H, which are the lineages that descended from the hybrid’s parents. (f) Without the G reference genome, reads from a pseudo-hybrid of the closely related species G and H instead mapped to the H reference genome, with some mapped promiscuously to references I and J.

To determine sppIDer’s applicability to hybrids of both closely and distantly related parents and of recent and ancient origin, we tested sppIDer with pseudo-hybrids of different combinations of simulated species. sppIDer accurately detected all true hybrid parents. When pseudo-hybrids were between sister species, <0.01% of the reads mapped promiscuously to other species (fig. 3c). When we used more divergent pseudo-hybrids, sppIDer still detected the true parents, with <5% of the reads mapped promiscuously to the sister species (fig. 3d). In the more closely related phylogeny, ∼3% of the reads mapped promiscuously to the sister species (supplementary fig. S2, Supplementary Material online), but the true parents were still statistically identified. Additionally, in the more distantly related phylogeny, we simulated ancient pseudo-hybrids, between common ancestors before lineage splits, and found that sppIDer mapped the reads of these hybrids to the references of the lineages that descended from the ancestors that hybridized (fig. 3e). With complete knowledge of this simulated phylogeny, we were able to test many different potential hybrid arrangements and found that sppIDer detected the true parents of all hybrids.

Finally, we tested a scenario, which is common in biology, of incomplete knowledge of the clade of interest. This dearth could due to many variables, such as a described species lacking a reference genome or a species being unknown to science altogether. To test the effect of missing a species, we removed one species’ reference genome from the combination reference genome, then mapped pure lineage and pseudo-hybrid reads to this permuted genome. With reads of a simulated pseudo-hybrid of sister species, G and H, we observed that, when one parent genome was missing, the reads mapped primarily to the reference genome of the remaining parent, reference H, with slightly increased promiscuous mapping of reads to the next-closest clade, references I and J (fig. 3f). Therefore, with incomplete reference genome knowledge, detecting hybrids of closely related species is limited. However, we could still detect hybrids of more distantly related species, such as a pseudo-hybrid of E and G and a pseudo-hybrid of A and the common ancestor of G and H (supplementary fig. S3, Supplementary Material online), though our inference of parentage was biased by the availability of reference genomes. Therefore, with incomplete knowledge of reference genomes, hybrid detection is limited, and the inference of true parentage can suffer in specific cases, but generally, distant and ancient hybrids can be detected.

Hybrid Detection with Missing Reference Genomes

To empirically address how sppIDer would be affected by missing reference genomes, such as for hybrids whose parents are themselves unknown (Hoot et al. 2004; Pryszcz et al. 2014), we focused again on the genus Saccharomyces. Specifically, we used the S. cerevisiae × S. kudriavzevii hybrid (Vin7) and the S. cerevisiae × S. eubayanus Frohberg lager yeast (W34/70) as examples. We tested the performance of sppIDer on short-read data from both hybrids by removing the S. cerevisiae reference genome and, in a separate test, removing the reference genome of the other parent. Our expectation was that reads would map to the genome of the sister species, if it were available, or that they would fail to map or be distributed across other genomes, if there were no close relatives.

When we removed the S. eubayanus reference genome for the lager example, the proportion of reads that failed to map increased, as did those reads that mapped to S. uvarum, its sister species (∼93% identical in DNA sequence, Libkind et al. 2011), albeit with a decreased MQ (fig. 4c). We then tested sppIDer on Vin7 and W34/70 when the S. cerevisiae reference genome was removed (fig. 4a and d). In both examples, the proportion of reads that mapped to S. paradoxus, S. cerevisiae’s sister species (∼87% identical in DNA sequence), increased (fig. 4a and d). Thus, the absence of a reference genome for one of the parents of a hybrid led to increased mapping to its sister species. We also tested removing the S. kudriavzevii reference genome for Vin7. As there is not a sister species closely related to S. kudriavzevii, the number of unmapped reads increased, and the remaining reads mapped to the reference genomes of other species of the genus in approximately equal proportions (fig. 4f).

Fig. 4.

Comparison of the percentage of reads that mapped when different reference genomes were excluded, compared with when all possible reference genomes for Saccharomyces were available (middle panels). (a) When the S. cerevisiae reference genome was not provided and reads from a Frohberg lager strain, W34/70, were mapped, more reads failed to map (gray) or mapped to the S. paradoxus reference genome (yellow). (b) When the full array of Saccharomyces genomes was provided, reads for the lager strain mapped to both S. cerevisiae and S. eubayanus. (c) When the S. eubayanus reference genome was removed, more reads from the lager strain failed to map or mapped to the S. uvarum reference genome (purple). (d) With the removal of the S. cerevisiae reference genome, reads from the S. cerevisiae × S. kudriavzevii hybrid strain Vin7, which would normally map to S. cerevisiae, instead failed to map or mapped to S. paradoxus. (e) When all genomes were used, reads mapped to both S. cerevisiae and S. kudriavzevii. (f) With the removal of the S. kudriavzevii reference genome, reads that would normally map to S. kudriavzevii instead failed to map or were distributed across all other genomes.

From these tests, we would have easily inferred that W34/70 was a hybrid, regardless of whether either parent genome was withheld (the actual state of affairs for S. eubayanus before Libkind et al. 2011). Using the coverage plots, we were still even able to infer the same C/CNVs for W34/70 that we observed with the full suite of reference genomes. With Vin7, we still easily inferred its hybrid status without including the S. cerevisiae genome. Without the S. kudriavzevii reference genome, Vin7 produced an unusually high number of unmapped reads without a decrease in MQ to S. cerevisiae, a result that should spur the investigator to perform more detailed analyses to search for evidence of contributions by an unknown species, such as de novo genome assembly and phylogenetics. Therefore, even without a full complement of reference genomes, sppIDer can still be useful for rapid inference of interspecies hybrids.

Hybrid Detection with Simulated Low-Quality Reference Genomes

To test a scenario where not all of the reference genomes are ideal, we used iWGS (Zhou et al. 2016) to independently simulate reads and then assemble de novo genomes for S. cerevisiae, S. kudriavzevii, S. uvarum, and S. eubayanus. These simulations resulted in reference genomes with many more scaffolds and with a lower N50 than the published genomes (supplementary table S1, Supplementary Material online). These low-quality genomes were independently swapped for the high-quality references in the combination reference genome and tested with short-read data. We started by testing simulated pseudo-lager short reads where we expected reads to map to both the S. cerevisiae and S. eubayanus reference genomes. Whether we swapped in the low-quality S. cerevisiae reference (supplementary fig. S4a, Supplementary Material online) or the low-quality S. eubayanus reference (supplementary fig. S4b, Supplementary Material online), the reads still mapped equally to the references that were used to simulate the reads with few promiscuously mapped reads to their sister species reference genomes.

We next tested the limits of sppIDer with the empirical data for CBS2834 because it has the most complex arrangement of contributions from four species. Tests with each simulated low-quality reference genome independently showed that we could indeed recapitulate the same inference of ancestry and that roughly the same proportion of reads mapped to each reference genome (supplementary fig. S5, Supplementary Material online) as with high-quality reference genomes (supplementary fig. S1f, Supplementary Material online). Here, the inference of approximate ploidy became more difficult, and visually interpreting translocations between species was impossible. When both high-quality S. cerevisiae and S. kudriavzevii reference genomes were used, we could infer translocations between these two genomes on chromosomes IV, X, and XV due to mid-chromosome ploidy changes that are compensated for in the other genome. There were more promiscuously mapped reads to the high-quality reference genomes of the sister species, but not at the same level as mapped to the true parent reference genomes. These tests with simulated low-quality de novo genomes showed that, both with simulated and empirical data, proper hybrid genome contributions can still be identified, and ploidy shifts still detected, despite the poor-quality reference genomes, but the inference of translocations and ploidies of specific chromosomes becomes difficult.

Hybrid Detection with Low-Coverage and Long-Read Data

To further explore the power of sppIDer, we wanted to test how little coverage was needed to still detect the proper ancestry (supplementary fig. S6, Supplementary Material online). Using data simulated at varying coverages, we found that only 0.5× coverage was needed to recover the true ancestry for a single species (supplementary fig. S6a and b, Supplementary Material online), single species with aneuploidies (supplementary fig. S6c and d, Supplementary Material online), and interspecies hybrids (supplementary fig. S6e and f, Supplementary Material online). We also tested empirical data by down-sampling the FASTQ files of CBS2834 and found that we could still detect contributions from the four species at as low as ∼0.05× coverage (supplementary fig. S6g, Supplementary Material online), but we lost the ability to infer ploidy at around ∼0.5× coverage (supplementary fig. S6h, Supplementary Material online). These low coverage tests show how powerful sppIDer is, even with scant data, which could be a boon in many systems with large genomes or when sequencing resources are limited.

We also tested sppIDer with simulated PacBio long-read data from the S. cerevisiae genome and a hybrid pseudo-lager genome with equal contributions from the S. cerevisiae and S. eubayanus reference genomes. We found that we could still easily determine the species contribution for each (supplementary fig. S7, Supplementary Material online), suggesting sppIDer’s utility will continue if long-read technologies eventually supplant short-read sequencing technologies.

Divergent Lineages and Poor-Quality Data

As sppIDer relies on reference genomes, we recognized that it might be biased in its ability to work with lineages that were highly divergent from the reference genome, as might be the case in many systems. We tested this scenario with an example from S. paradoxus, one of the most diverse Saccharomyces species (Liti et al. 2009; Leducq et al. 2016). Compared with a representative of the reference genome’s lineage, fewer reads from the divergent lineage (∼96% identical) mapped and with poorer quality (supplementary fig. S8a and b, Supplementary Material online). We also tested this effect in S. kudriavzevii using poor-quality data (36-bp reads from a first-generation Illumina Genome Analyzer run by Hittinger et al. 2010) and found qualitatively similar results, but many more unmapped reads. Thus, while divergence from the reference genome affected map-ability, sppIDer still worked generally as expected. However, when mapping percentage and quality decline substantially, such as seen in these test cases, sppIDer can provide an early indication that the organism may be highly divergent from the reference genome, which may merit further investigation.

Comparison to Alignment-Free Phylogenetic and Population Genetic Methods

Alignment and assembly (AA)-free phylogeny-building methods are gaining popularity, but they have not previously been applied to hybrid data. Therefore, we also tested how AA-free phylogenetic methods, such as AAF (Fan et al. 2015) or SISRS (Schwartz et al. 2015), performed in detecting and visualizing hybrids compared with sppIDer. We found that these methods performed well when given only pure lineages, but when hybrids were included, they either failed completely or produced incorrect phylogenies. We tested both our simulated phylogeny and empirical Saccharomyces data. For the simulated data, both AAF and SISRS produced the correct phylogeny when given the ten simulated species. However, when given any hybrid data, AAF failed to produce the correct phylogeny and instead clustered the hybrid with its parents, while SISRS failed to complete at all. With the empirical data, we saw similar results; with AAF, we could recapitulate the phylogeny of the genus Saccharomyces when using only pure samples, but when we included any hybrid, an incorrect phylogeny was produced (supplementary fig. S9a and c, Supplementary Material online). SISRS had similar issues with producing the correct phylogeny with hybrids, but its output allowed for more nuanced network visualizations. For CBS2834, the SISRS output allowed us to infer the shared background with S. cerevisiae, S. kudriavzevii, S. uvarum, and S. eubayanus (supplementary fig. S9d, Supplementary Material online), but the proportion of contribution from each species was difficult to estimate compared with the sppIDer output. Overall, we found that these methods have serious limitations when used with hybrids, but they could be used as a complement to sppIDer to make inferences about pure parental lineages.

Methods that assemble targeted genes from short-read data, such as aTRAM and HybPiper, can be used with poor-quality references and/or references that may be missing genes of interest. We tested these tools with a panel of loci that can be used to delineate the S. eubayanus populations (Peris et al. 2016). HybPiper and aTRAM were able to match short reads to a locus of interest 59% or 34% of the time, respectively, but they could only assemble these reads 23% or 28% of the time, respectively. Neither method could assemble one locus for all 15 strains tested, including both hybrids and non-hybrids (supplementary table S1, Supplementary Material online). While these methods can be powerful when applied in a targeted manner to pure strains, they fail when applied to hybrid data.

We also tested how three population genetic methods, each based on variant-calling of a reference-based alignment, would perform with the two hybrid lager lineages, a S. eubayanus representative, and a S. cerevisiae representative. With STRUCUTRE (Pritchard et al. 2000), the population to which the lager strains were assigned depended on which reference genome was used to map and call variants (supplementary table S1, Supplementary Material online). When mapped to the S. eubayanus reference genome, they clustered with the S. eubayanus representative at K = 2 and K = 3. When mapped to the S. cerevisiae reference genome, the Frohberg strain always clustered with the S. cerevisiae representative. The Saaz strain clustered independently at K = 3, while at K = 2, it was inferred to be 0.856 S. cerevisiae and 0.144 S. eubayanus. FineStructure (Lawson et al. 2012) and PCAdmix (Henn et al. 2012) both failed to complete when given variants called on either the S. eubayanus or S. cerevisiae reference genome. These analyses confirm that population genetic methods cannot be reliably applied to detect or interpret allopolyploid hybrids at this level of sequence divergence.

Non-Saccharomyces Examples

Lachancea: Refining the Interpretation of Voucher Specimens.

With the publication of ten high-quality Lachancea genome sequences (Vakirlis et al. 2016) and another two recently described and fully sequenced species (González et al. 2013; Freel et al. 2015, 2016; Sarilar et al. 2015), this genus is becoming a powerful yeast model. As molecular techniques improve, initial identifications in culture and museum collections can yield new interpretations. For example, the strain CBS6924 was initially identified as Lachancea thermotolerans, but recent evidence suggested it as a candidate for a novel species (L. fantastica nom. nud.) (Vakirlis et al. 2016). Its closest relative, L. lanzarotensis, was also recently described (González et al. 2013). To test sppIDer’s utility for determining whether a strain or voucher specimen is or is not properly classified, we tested mapping reads from CBS6924 to a combination genome with all Lachancea reference genomes (supplementary fig. S10a, Supplementary Material online), then removing the “L. fantastica” reference genome (supplementary fig. S10b, Supplementary Material online), and then removing both the “L. fantastica” and L. lanzarotensis reference genomes (supplementary fig. S10c, Supplementary Material online). When both reference genomes were removed, the reads were spread across many genomes, and the initial classification of the strain as L. thermotolerans would have been easily falsified. By including the L. lanzarotensis reference genome, most reads mapped to that reference, but still poorly enough to warrant additional investigation. When the “L. fantastica” reference genome was included, CBS6924 reads mapped unambiguously to this reference. These results demonstrate sppIDer’s utility outside of the genus Saccharomyces to aid in reclassifying provisional species identifications of voucher specimens from culture and museum collections.

Drosophila

To test sppIDer with larger genomes, we examined the animal genus Drosophila, which is a large genus with many available reference genomes, including many less diverged than in Saccharomyces (Adams et al. 2000; Drosophila 12 Genomes Consortium 2007; Alekseyenko et al. 2013; Sanchez-Flores et al. 2016), as well as several species still lacking reference genomes. The difference in reference genome qualities led us to remove contigs <10 kb from our combination reference genome; this paring down sped up computation time, reduced memory usage, and improved the visualization, but otherwise did not affect the results (supplementary fig. S11a, Supplementary Material online). To test the ability of sppIDer to distinguish closely related species, we started with the Drosophila yakuba species complex (Turissini et al. 2015), where D. yakuba has a sequenced reference genome available, but its close relative D. santomae and more distant relative D. teissieri do not. Here, we observed that short reads from a D. yakuba (Comeault et al. 2016) representative mapped well to the D. yakuba reference genome. As we moved from the close relative D. santomae (fig. 5b) to a more distant one, D. teissieri (fig. 5c), the mapping percentage and quality decreased with increased promiscuous mapping to other relatives (fig. 5a–c). Thus, as in yeasts, sppIDer can classify pure species and their close relatives well and provide insight to guide downstream analyses.

Fig. 5.

Examples using animal and plant genomes. (a) Reads from a Drosophila yakuba individual mapped primarily (>99%) to the D. yakuba reference genome. (b) Reads from the sister species D. santomae mapped best to the D. yakuba reference genome with some mapped promiscuously to other reference genomes. (c) Reads from the more distantly related species D. teissieri mapped mostly to the D. yakuba reference genome, but with more reads not mapped and mapped promiscuously to other related reference genomes. (d) Reads from an Arabidopsis thaliana accession from Tanzania mapped back to the European reference genome for A. thaliana. The repetitive nature of centromeres causes the coverage to fluctuate around those regions. (e) Reads from the hybrid species A. kamchatica mapped to the two parental reference genomes: A. halleri and A. lyrata.

We also used Drosophila short-read data to test sppIDer’s ability to detect hybrids in non-fungal systems. In this case, we used genomic data from a pure parent and RNA-seq data from a F₁ interspecies hybrid (Coolon et al. 2014). We found that sppIDer could easily detect hybrids in an animal model, but as expected, detection of C/CNVs using RNA-seq was not possible (supplementary fig. S12, Supplementary Material online).

Arabidopsis

The study of hybrid speciation and allopolyploidy in plants has a long history (Rieseberg 1997; Soltis et al. 2015), and we choose Arabidopsis as our plant test case because it has reference genomes available for Arabidopsis halleri, A. thaliana, and A. lyrata (Swarbreck et al. 2007). There are drastic differences in the quality of reference genomes available: The A. thaliana reference has seven scaffolds with an N50 of 23,459,830, whereas the A. halleri reference has 282,453 scaffolds with an N50 of 17,686. To control for this limitation, we again removed contigs <10 kb from our combination reference genome, which helped with run time and memory usage but did not affect the conclusions (supplementary fig. S11b, Supplementary Material online). These tests in Arabidopsis provide an empirical illustration of sppIDer’s performance with differing quality reference genomes. Arabidopsis also provides a useful test of detecting hybrids in a plant system, as there are two well-supported allotetraploid species in the genus, A. suecica and A. kamchatica (Shimizu-Inatsugi et al. 2009; Schmickl et al. 2010). First, we tested short-read data from a divergent lineage of A. thaliana (Durvasula et al. 2017) and found that the reads mapped well to the A. thaliana reference genome (fig. 5d). As expected, reads from the interspecies hybrid A. kamchatica (Novikova et al. 2016) mapped both to A. lyrata and to A. halleri (fig. 5e), and the χ² test of mapped reads showed that only these two species had positive residuals (supplementary table S1, Supplementary Material online). The coverage analysis also showed that the contributions from A. lyrata and A. halleri were approximately equal. These analyses confirm that A. kamchatica indeed has genomic contributions from these two species and that sppIDer can detect hybrids, even when the combination reference genome contains reference genomes of substantially varying quality. Thus, sppIDer can accurately detect interspecies hybrid in a plant model and will likely become more generally useful in other plant systems, where allopolyploidy is frequent (Soltis et al. 2015), as more reference genomes become available.

mitoSppIDer

Applications of sppIDer with non-nuclear sequencing data are also of considerable interest. Organelle genomes (e.g., mitochondria, chloroplast) have a different mode of inheritance, and increasing data suggest widespread reticulation and cases where their ancestries differ from the nuclear genomes (Peris et al. 2014; Wu et al. 2015; Leducq et al. 2017; Peris, Arias, et al. 2017; Peris, Pérez-Torrado, et al. 2017; Sulo et al. 2017). We developed mitoSppIDer as an extension to explore these nonnuclear inherited elements. As mitochondrial genomes are generally small, the coding regions can be easily visualized, which allows precise mapping of introgressions in both coding and non-coding regions. However, more cautious interpretation is warranted, because mitochondrial reads are often at low and variable abundance, and quality can differ between DNA isolations and sequencing runs. Again, we tested using the genus Saccharomyces because of the availability of mitochondrial reference genomes (Foury et al. 1998; Procházka et al. 2012; Baker et al. 2015). We first tested mitoSppIDer with a strain of S. uvarum (ZP1021) (Almeida et al. 2014) and found that, of the reads that mapped to any mitochondrial genome, >99% mapped to the S. uvarum mitochondrial genome (supplementary fig. S13a, Supplementary Material online). Next, we examined Vin7, a hybrid strain of S. cerevisiae × S. kudriavzevii, and mitoSppIDer revealed that this strain inherited the mitochondrial genome of S. kudriavzevii with intergenic introgressions from multiple non-S. kudriavzevii mitochondrial genomes (supplementary fig. S13b, Supplementary Material online) (Peris, Pérez-Torrado, et al. 2017). As with conventional sppIDer, mitoSppIDer rapidly highlights interesting regions for further analysis, such as detailed phylogenetic analyses of introgression candidates.

Summary

Altogether, these tests show the versatility of sppIDer across clades: in fungi, plants, and animals. sppIDer allows for the rapid exploration and visualization of short-read sequencing data to answer a variety of questions, including species identification; determination of the genome composition of natural, synthetic, and experimentally evolved interspecies hybrids; and inference of C/CNVs (Brickwedde et al. 2017; Gorter de Vries et al. 2017; Peris et al. 2017). With examples from the genus Saccharomyces, sppIDer could detect contributions from up to four species and recapitulated the known relative ploidies and aneuploidies of brewing strains. From a simulated phylogeny, we found that sppIDer accurately detected hybrids from a range of divergences in the parents and even detected ancient hybrids. In systems with low-quality or varied-quality references genomes, sppIDer performs well without much promiscuous mapping between varying reference qualities, but its ability to infer translocations and C/CNVs is limited. Even in systems missing reference genomes, sppIDer still enables rapid inferences by using the reference genomes of closely related species, with the caveat that MQ declines with sequence divergence. Additionally, sppIDer works on long-read data and with coverage as low as 0.5×. Finally, sppIDer can be extended to non-nuclear data, allowing for the exploration of alternative evolutionary trajectories of mitochondria or chloroplasts. As more high-quality reference genomes become available across the tree of life, we expect sppIDer will become an increasingly useful and versatile tool to quickly provide a first-pass summary and intuitive visualization of the genomic makeup in diverse organisms and interspecies hybrids.

Materials and Methods

The sppIDer workflow to identify pure species, interspecies hybrids, and C/CNVs consists of one main pipeline that utilizes common bioinformatics programs, as well as several custom summary and visualization scripts (fig. 1). An upstream step is required to prepare the combination reference genome to test the desired comparison species. The inputs for the main sppIDer pipeline are this combination reference genome and short-read FASTQ file(s) from the organism to test. The output consists of several plots showing to which reference genomes the short reads mapped, how this mapping varies across the combination reference genome, and several text files of summary information. Additionally, the pipeline retains all the intermediate files used to make the plots and summary files; these contain much more detailed information and may be useful as inputs to various other potential downstream analyses. We are releasing sppIDer as a Docker, which runs as an isolated, self-contained package, without the need to download dependencies and change environmental settings. Packaging complex bioinformatic pipelines as Docker containers increases their reusability and reproducibility, while simplifying their ease of use (Boettiger, Unpublished; Di Tommaso et al. 2015). sppIDer can be found here (https://github.com/GLBRC/sppIDer), where a transparent Dockerfile lays out the technical prerequisites, platform, how they work in combination, and is a repository for all the custom scripts. A manual for sppIDer can be found both at the GitHub page and at http://sppider.readthedocs.io; last accessed September 6, 2018.

The Pipeline

Before running the main sppIDer script, a combination reference genome must first be created and properly formatted (top of fig. 1). This is done using a separate script, combineRefGenomes.py, that takes multiple FASTA-formatted reference genomes and a key listing the reference genomes to use and a unique ID for each. The script concatenates the reference genomes together in the order given in the input key, outputting a combination reference FASTA where the chromosomes/scaffolds are renamed to reflect their reference unique ID and their numerical position within the reference-specific portion of the combination output. For reference genomes that contain many short and uninformative scaffolds, there is an option to remove scaffolds below a desired base-pair length. This option improves speed, memory usage, and visual analysis for large genomes with many scaffolds and low N50 values. Setting a threshold usually does not affect the conclusions (supplementary fig. S11, Supplementary Material online), but we recommend trying different thresholds to determine how much information is lost. The choice of reference genomes to concatenate is completely at the discretion of the user and their knowledge of the system to which they are applying sppIDer. We recommend choosing multiple phylogenetically distinct lineages or species, where gene flow and incomplete lineage sorting are limited, from a single genus. We caution that, for ease of analysis and interpretation, <30 reference genomes should be used at once. To illustrate the power of sppIDer, for our examples, we used all available species-level reference genomes for the genera tested, but we excluded lineages and strains within species. However, sppIDer could be applied iteratively with different combinations of reference genomes that are more targeted for a particular lineage or question. For example, with an experimentally evolved hybrid, just the parental genomes could be included to detect C/CNVs that occurred during the evolution, but with a suspected hybrid isolated from the wild or industry, all potential parent species reference genomes should be included.

The main body of sppIDer (fig. 1b) uses a custom Python 2 (Python Software Foundation. Python Language Reference, version 2.7. Available at http://www.python.org) script to run published tools and custom scripts to map short reads to a combination reference genome and parse the output. The first step uses the mem algorithm in BWA (Li and Durbin 2009) to map the reads to the combined concatenated reference genome. Two custom scripts use this output to count and collect the distribution of MQs for the reads that map to each reference genome and produce plots of percentage and MQ of reads that map to each reference genome. The BWA output is also used by samtools view and sort (Li et al. 2009) to keep only reads that map with an MQ >3, a filter that removes reads that map ambiguously. From here, the number of reads that map to each base pair can be analyzed using bedtools genomeCoverageBed (Quinlan and Hall 2010), for smaller genomes using the per-basepair option (-d) and, for large genomes, the –bga option. The depth of coverage output is used by an R (Wickham 2009; R Core Team, Unpublished) script that determines the mean coverage of the combined reference genome that is subdivided into 10,000 windows of equal size. Finally, a plot for the average coverage for each component reference genome and a second plot of average coverage for the windows are produced.

The Metrics

Several different metrics are used to summarize the data. Depth of coverage is a count of how many reads cover each base pair or region of the genome. Coverage can vary greatly from sequencing run to sequencing run; hence, a log₂ conversion is used to normalize to the mean coverage. As discussed in the Results, depth of coverage plots can be used to infer the species, the parents of hybrids, and ploidy changes either between or within a genome. Ploidy can be estimated by using the coverage distribution to bin regions of the genomes with similar coverages. Local minima (antimodes) and maxima (peaks) for the coverage distribution across the combination genomes are identified using the R package “modes” (version 0.7.0) (Deevi S, 4D Strategies, Unpublished), and bins with >5% of the windows are retained. These ploidy bins can also be used to identify small regions of introgressions from other genomes, and the default threshold for detection is if >1% of a genome is above the local minimum with the lowest coverage value. sppIDer also reports the percentage of reads that map to each reference genome. Finally, sppIDer uses the established MQ score introduced by Li et al. (2008) to bin reads by their map-ability on a 0–60 scale. A score of zero is used for reads where it is unlikely that their placement is correct, so sppIDer reports these as “unmapped,” along with reads that cannot be mapped and therefore do not receive an MQ score. The MQ scale can therefore provide a rough assessment of data quality, as well as divergence to the provided reference genomes. Genomes contributing to the tested data can be identified as those producing a statistically significant positive residual in a χ² test of the counts of reads that map. The null hypothesis is that reads will be randomly distributed across all genomes. One test includes all reads MQ > 0 and the unmapped reads, which allows for inference of contributing species, as well as if unmapped reads are significantly enriched. A second χ² test is just performed on the MQ = 60 reads; by determining which genomes are enriched for high-quality reads, this test is more appropriate for statistically assessing which parents contributed to hybrids.

Tested Reference Genomes and Data

For the Saccharomyces tests, we used reference genomes that are scaffolded to a chromosomal level. In some cases, there is only one reference genome available per species, and for the others, we used the first available near-complete reference; see supplementary table S1, Supplementary Material online, for those used. For systems with multiple reference genomes available, the choice could be more targeted, such as utilizing lineage-specific references or references that contain unplaced scaffolds with genes of interest. Alternatively, for systems where few genomes are available, we have shown here that a close relative works as a proxy. For the Saccharomyces references, each ordered “ultra-scaffolds” genome was downloaded from http://www.saccharomycessensustricto.org/; last accessed September 6, 2018 or for S. arboricola and S. eubayanus from NCBI. The published S. uvarum genome (Scannell et al. 2011) had chromosome X swapped with chromosome XII, which was fixed manually. These genomes were concatenated together using the Python script combineRefGenomes.py, creating a combination reference FASTA with all Saccharomyces species. This combination reference genome can then be used repeatedly to test any data set of interest. For the Saccharomyces tests, we used publicly available FASTQ data from a number of publications, all available on NCBI (supplementary table S1, Supplementary Material online, contains all accession numbers). Using the data for each strain separately and the combination reference genome created above, we then called sppIDer.py with, –out uniqueID, –ref SaccharomcyesCombo.fasta, –r1 read1.fastq, and optionally –r2 read2.fastq. sppIDer is written to test one sample’s FASTQ file(s) against one combination reference genome at a time, but this could be easily parallelized.

For the tests to determine whether hybrids could be detected with missing reference genomes, new combination reference genomes without one species’ genomes were created by removing the desired species’ reference name from the reference genome key before running combineRefGenomes.py. As both Vin7 and W34/70 contain contributions from S. cerevisiae, the combination reference genome lacking the S. cerevisiae reference was tested for each set for FASTQ files for Vin7 and W34/70. The same process was followed to remove the S. kudriavzevii reference genome from the combination reference to test Vin7, as well as to remove the S. eubayanus reference genome from the combination reference to test W34/70.

For the Lachancea test, all of the genomes were available and downloaded from http://gryc.inra.fr/; last accessed September 6, 2018. The FASTQ data for CBS6924 were downloaded from NCBI. A combination reference genome with all available genomes was created and used. Then, sequentially, the “Lachancea fantastica” and Lachancea lanzarotensis genomes were removed by modifying the input key and rerunning combineRefGenomes.py. The FASTQ data for CBS6924 were tested against all three of these combination reference genomes. See supplementary table S1, Supplementary Material online, for the full accessions.

For the non-Saccharomyces tests, we used the most complete reference genome available for each species in the genus (accession numbers are provided in the supplementary table S1, Supplementary Material online). Therefore, there is quite a bit of variation between different references. For the Drosophila and Arabidopsis genomes, we tested removing contigs, using the –trim option, with combineRefGenomes.py, as well as not removing contigs, and found the cleanest results when we removed contigs <10 kb. The combined reference genomes of both Drosophila and Arabidopsis were both larger than 4 Gb; therefore, the –byGroup option was used with sppIDer.py to speed up processing and reduce memory usage. The data we tested came from a variety of publications, but we targeted data of divergent or hybrid lineages. See supplementary table S1, Supplementary Material online, for complete information.

For the mitoSppIDer test, we used the complete species-level Saccharomyces mitochondrial reference genomes available on NCBI, which do not necessarily correspond to the same strain that was used to build nuclear genomic reference (supplementary table S1, Supplementary Material online). Again, combineRefGenomes.py was used to concatenate these references. An additional script, combineGFF.py, was used to create a combination GFF file that was used to denote the coding regions on the output plots. mitoSppIDer.py has an additional flag for the GFF file, but it otherwise runs in a similar manner to sppIDer.py; the same input FASTQ file(s) can even be used. Whole-genome sequencing data contain varying amounts of mitochondrial sequences; therefore, using the raw FASTQ data works sufficiently, even when many of the genomic reads will be classified as “unmapped.”

Simulations

To create the simulated low-quality de novo genomes, we used the software iWGS (Zhou et al. 2016) to simulate 100-bp paired-end reads with an average inter-read insert size of 350 bp (SD 10) at 2× coverage from the reference genomes of S. cerevisiae, S. kudriavzevii, S. uvarum, and S. eubayanus. For the simulated de novo Saccharomyces genomes, the N50 scores ranged from 1,254 to 1,274 and the number of scaffolds ranged from 10,023 to 10,426 (supplementary table S1, Supplementary Material online).

To simulate short-read data, we used DWGSIM (https://github.com/nh13/DWGSIM; last accessed September 6, 2018), which allowed us to vary the coverage, error rate, and mutation rate as needed. The S. cerevisiae reference genome was used to simulate single species reads, and a concatenation of the S. cerevisiae and S. eubayanus reference genomes was used for hybrid pseudo-lager reads. As a test of an aneuploid genome, we also manually manipulated the S. cerevisiae reference genome so that it contained zero copies of chromosomes I and III and duplicate copies of chromosome XII. All simulated reads were 100-bp paired-end reads with an average insert size of 500 bp. For the coverage tests, we varied the coverage from 0.01× to 10×. For the short reads used against the low-quality de novo genomes, we used 10× coverage and a 3% mutation rate. To simulate PacBio-style long reads, we used iWGS on the hybrid pseudo-lager concatenated genome with the default settings of 30× coverage, average read accuracy of 0.9, and SD of read accuracy 0.1.

To make our simulated phylogeny, we used the S. cerevisiae reference genome as a base and simulated reads with DWGSIM at a 2% (or 0.5% for the closely related phylogeny) mutation rate as 100-bp paired-end reads with an average insert size of 500 bp at 10× coverage. iWGS was used to assemble these reads. The resulting assembly was again simulated with a 2% mutation rate, and those reads were assembled. This procedure was followed for six rounds with one lineage being independently simulated twice each round to produce a speciation event. This simulation resulted in ten species in the phylogenetic arrangement shown in figure 3a. Summaries of the final assemblies can be found in supplementary table S1, Supplementary Material online, but the median of the final assemblies was 5,100 scaffolds, N50 of 1,335, and total length of 6.4 Mb. Each simulated species was ∼12% diverged from S. cerevisiae, the most closely related species were ∼4% diverged, and the most distantly related species were ∼20% diverged. The reads used to produce the final assemblies were used to test whether sppIDer mapped each set of reads to their corresponding reference genomes. The reads of different references were concatenated to simulate pseudo-hybrids of different divergences. To simulate ancient hybrids, the reads from earlier rounds of simulation, before speciation events, were concatenated and tested against the final assemblies with sppIDer. As with the empirical data, to simulate a missing reference genome, that reference was removed from the input key prior to running combineRefGenomes.py.

Alignment-Free Phylogenetic and Population Genetic Methods

We tested four alignment-free phylogenetic methods: Two that build phylogenies using short-read data, SISRS (Schwartz et al. 2015) and AAF (Fan et al. 2015), and two that assemble targeted loci from short-read data, aTRAM (Allen et al. 2015) and HybPiper (Johnson et al. 2016). We simulated 10× coverage paired-end, 100-bp data for each Saccharomyces reference genome at a mutation rate of 0 with DWGSIM to use as input for these methods. For SISRS, we used the default settings with a genome size of 12 Mb, first using only the reference Saccharomyces data, then including empirical data for hybrids. SISRS failed at the missing data filtering step when data from the lager strain W34/70 were used, even when we allowed for all but one sample to have missing data. SISRS nexus outputs were visualized with SplitsTree (Huson and Bryant 2006). For AAF, we found that a k of 17 accurately recapitulated the Saccharomyces phylogeny, even with the inclusion of empirical data from other pure lineages. Once we determined the optimal k, we tested including empirical hybrid data. We also used AAF with our simulated phylogeny, which constructed the tree that matched the simulations with the default k of 25. The output of AAF was visualized with iTol (Letunic and Bork 2016).

For the targeted loci methods, we used 13 loci that can delineate S. eubayanus populations (Peris et al. 2016), as well as the ITS sequences for S. cerevisiae (AY046146.1) (Kurtzman and Robnett 2003) and S. eubayanus (JF786673.1) (Libkind et al. 2011) as bait, all obtained from NCBI. We tested the simulated Saccharomyces reads, as well as the empirical data for P1C1, Fosters O, CBS1503, CBS2834, Vin7, and W34/70. For aTRAM, we used the default settings and the option for the Velvet assembler. For HybPiper, we used the default settings and the SPADES assembler.

To test STRUCTURE, FineStructure, and PCAdmix variants for P1C1 (S. eubayanus), FostersO (S. cerevisiae), CBS1503 (a Saaz lager), and W34/70 (a Frohberg lager) were called as done by Peris et al. (2016), with the S. cerevisiae and S. eubayanus reference genomes considered independently in separate analyses. For STRUCTURE, ∼10,000 single nucleotide polymorphisms (SNPs) were sampled form the total data set. STRUCURE was run with BURNIN 5000 and NUMREPS 10000 at K = 2, 3, and 4. For PCAdmix, all the SNPs (∼220,000) were used, and P1C1 and FostersO were set as the ancestors, and the lagers were set as the admixed lineages. For FineStructure, all the SNPs were used, and it was run with the default settings.