Evaluating the Genome-Based Average Nucleotide Identity Calculation for Identification of Twelve Yeast Species

Citation: Cortimiglia, C.; Alonso-DelReal, J.; Belloso Daza, M.V.; Querol, A.; Iacono, G.; Cocconcelli, P.S. Evaluating the Genome-Based Average Nucleotide Identity Calculation for Identification of Twelve Yeast Species. J. Fungi 2024,10, 646. https://doi.org/10.3390/ jof10090646 Academic Editor: Willem JG Melchers Received: 7 August 2024 Revised: 29 August 2024 Accepted: 9 September 2024 Published: 11 September 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Fungi Journal of Article Evaluating the Genome-Based Average Nucleotide Identity Calculation for Identification of Twelve Yeast Species Claudia Cortimiglia 1,† , Javier Alonso-Del-Real 2,† , Mireya Viviana Belloso Daza 1, Amparo Querol 3, Giovanni Iacono 4and Pier Sandro Cocconcelli 1,* 1Department for Sustainable Food Process (DISTAS), UniversitàCattolica del Sacro Cuore, 29122 Piacenza, Italy; [email protected] (C.C.); [email protected] (M.V.B.D.) 2Tuberculosis Genomics Unit, Instituto de Biomedicina de Valencia (IBV-CSIC), 46010 Valencia, Spain; [email protected] 3 Grupo de Biología de Sistemas en Levaduras de Interés Biotecnológico, Departamento de Biotecnología de los Alimentos, Instituto de Agroquímica y Tecnología de Los Alimentos (IATA-CSIC), 46980 Valencia, Spain; [email protected] 4European Food Safety Authority (EFSA), 43126 Parma, Italy; [email protected]opa.eu *Correspondence: pier[email protected] †These authors contributed equally to this work. Abstract: Classifying a yeast strain into a recognized species is not always straightforward. Currently, the taxonomic delineation of yeast strains involves multiple approaches covering phenotypic characteristics and molecular methodologies, including genome-based analysis. The aim of this study was to evaluate the suitability of the Average Nucleotide Identity (ANI) calculation through FastANI, a tool created for bacterial species identification, for the assignment of strains to some yeast species. FastANI, the alignment of in silico-extracted D1/D2 sequences of LSU rRNA, and multiple alignments of orthologous genes (MAOG) were employed to analyze 644 assemblies from 12 yeast genera, encompassing various species, and on a dataset of hybrid Saccharomyces species. Overall, the analysis showed high consistency between results obtained with FastANI and MAOG, although, FastANI proved to be more discriminating than the other two methods applied to genomic sequences. In particular, FastANI was effective in distinguishing between strains belonging to different species, defining clear boundaries between them (cutoff: 94–96%). Our results show that FastANI is a reliable method for attributing a known yeast species to a particular strain. Moreover, although hybridization events make species discrimination more complex, it was revealed to be useful in the identification of these cases. We suggest its inclusion as a key component in a comprehensive approach to species delineation. Using this approach with a larger number of yeasts would validate it as a rapid technique to identify yeasts based on whole genome sequences. Keywords: yeast species delineation; FastANI; whole genome sequencing; multi-loci characterization; taxogenomics 1. Introduction The species delineation of yeasts, intended as the process of assigning a yeast strain to a specific taxonomic group and of establishing the boundaries between different yeast species, is still a complex endeavor. Since the beginning of microbial taxonomy, for both prokaryotic and eukaryotic microorganisms, different meanings have been attributed to the ‘species’ concept and this evolution has been linked to the increase in knowledge about biological and genomic aspects of microbes. Alongside the evolution of the species concept, various methods have been developed to effectively define what is meant by a species. Although DNA-based molecular analysis improved the distinction between yeast species, by using several markers [ 1 ], such as the variable domains D1 and D2 of the nuclear large subunit ribosomal RNA (LSU rRNA) gene and internal transcribed spacer (ITS) sequences [ 2 ], in J. Fungi 2024,10, 646. https://doi.org/10.3390/jof10090646 https://www.mdpi.com/journal/jof J. Fungi 2024,10, 646 2 of 20 the case of some closely related species, their simultaneous use was demonstrated not to be sufficiently discriminating [ 3 ]. For this reason, these universal markers have been associated with other genetic markers, typical of each genus and species, constituting the so-called polyphasic approach [ 4 ]. Some examples of other markers are cytochrome oxidase II (COX II) [ 5 ], translation elongation factor 1 α (EF-1 α ) ( Kutzman and Robnett, 2003 ), RNA polymerase II gene [ 6 ], and the actin gene [ 7 ]. The alignment of concatenated genes is used to infer phylogenetic relationships and deduce the belonging to a determined species. Molecular analysis of a combined dataset of marker genes is currently integrated with the polyphasic approach for the determination of strain conspecificity, including not only molecular investigation but also a phenotypic evaluation. Indeed, nowadays, the species concept is built by looking at several aspects of a strain, starting from the so-called Phenotypic Species Concept (PhenSC), which includes phenotypic properties, like morphological characteristics of colonies and growth requirements in terms of substrates and temperature, and taking in consideration the Phylogenetic Species Concept (PSC), determined on the genetic distance calculated using different barcodes [8]. With the advent of genomic technologies, yeast taxonomy and characterization have undergone a transformative shift [ 9 ]. Genomic sequences have emerged as invaluable resources, providing a wealth of information for unraveling the intricate relationships among yeast taxa and species. In particular, whole-genome sequencing (WGS) and analysis allowed for a more comprehensive and accurate understanding of many of their biological features by studying intra-genus [ 10 , 11 ] and intraspecies variations [ 12 – 16 ], including the delimitation of species complexes [ 17 ], the complexity of population structure [ 18 ], evolutionary scenarios [19,20], domestication events [21], and geographic adaptation [18]. The latest discoveries in yeast genetics and genomics have also helped to deepen our knowledge of the evolutionary capabilities revealed through hybridization, particularly within the Saccharomyces genus [ 22 , 23 ]. The occurrence of past hybrid matings, accompanied by subsequent backcrossing and other postzygotic processes, has resulted in an imbalance in the relative contributions of parental genomes. This event makes species delineation and identification more complicated. For example, although LSU rRNA is used extensively for species identification, it is known that intragenomic variations in this region can arise, presenting challenges in achieving precise and accurate species identification [24,25]. In bacteria, the application of WGS has led to the development of a number of metrics to assess species delineation. The systematic process to identify species is performed by the overall genome relatedness index (OGRI), which estimates the relatedness between the genome of a strain to be identified and the genome of a type strain. Among the algorithms for OGRI calculation, the most widely used is Average Nucleotide Identity (ANI), which measures the genetic identity through the pairwise comparison of coding regions within genomes, and digital DNA-DNA hybridization (dDDH), namely the calculation of overall in silico similarity between the genomes of two strains, integrated with the 16S rRNA sequence similarity analysis [ 26 ]. For bacteria, boundaries of ≈ 94–96% and 70% for ANI and dDDH, respectively, are commonly accepted for species delimitation. The application of this metric to yeast is not particularly widespread and still needs to be explored in depth. Lachance and colleagues [ 27 ] were the first to use an ANI approach to delineate Metschnikowia species, comparing the results they obtained with this approach with a phylogenetic tree built on the 100 largest orthologs and suggesting 95 ± 0.5% as the threshold for species circumscription [28]. The aim of this work was to define ANI values to be used to set species boundaries for the taxonomic identification of yeast strains and for the delineation of some yeast species. For this purpose, we used FastANI, a bioinformatics tool that calculates ANI of orthologous genes using alignment-free workflow with a significantly improved analysis speed, originally designed for bacterial species identification [ 29 ]. To assess the performance of this method, we tested a dataset of 644 assemblies of yeast genera of biotechnological interest; the results were then compared to two other methods, one based on the alignment J. Fungi 2024,10, 646 3 of 20 of D1/D2 region of LSU rRNA extracted in silico and the other based on the multiple alignment of orthologous genes (MAOG) among the species in question. Moreover, to assess the use of FastANI in the hybrid strains, we compared the three methodologies using 77 previously published hybrid strain sequences of Saccharomyces [ 30 – 33 ] and 150 genomes artificially simulating introgression created for the present work. 2. Materials and Methods 2.1. Yeast Assemblies Used in This Study Twelve yeast genera belonging to Saccharomyces,Debaryomyces,Rhodotorula,Hanseniaspora,Kazachstania,Kluyveromyces,Pichia,Starmerella,Torulaspora,Yarrowia,Candida, and Schizosaccharomyces were selected. All except Candida and Schizosaccharomyces were chosen because of their numerous applications in biotechnology. We considered Candida as a genus of clinical importance as well as having biotechnological potential [ 34 ]; we included Schizosaccharomyces as an outlier in the taxonomic rank because it belongs to a different Order. Within each genus, all available assemblies from all species were downloaded from the NCBI Genome repository except for known hybrid species. Since the number of available draft genomes for Saccharomyces cerevisiae was more than 1000, we decided to consider a restricted number of them; we selected the 79 draft genomes analyzed by Legras and coworkers, which reflect the genetic variability of the species [35]. Overall, a total of 644 assemblies deposited in the NCBI Genome database and classified as the above-mentioned genera were downloaded to be part of the dataset. These assemblies showed different completeness statuses, as reported in Figure 1, and quality. The scaffold assembly level was the most represented (64.3%), while complete and chromosome status were less common. J. Fungi 2024, 10, 646 3 of 21 speed, originally designed for bacterial species identification [29]. To assess the performance of this method, we tested a dataset of 644 assemblies of yeast genera of biotechnological interest; the results were then compared to two other methods, one based on the alignment of D1/D2 region of LSU rRNA extracted in silico and the other based on the multiple alignment of orthologous genes (MAOG) among the species in question. Moreover, to assess the use of FastANI in the hybrid strains, we compared the three methodologies using 77 previously published hybrid strain sequences of Saccharomyces [30–33] and 150 genomes artificially simulating introgression created for the present work. 2. Materials and Methods 2.1. Yeast Assemblies Used in This Study Twelve yeast genera belonging to Saccharomyces, Debaryomyces, Rhodotorula, Hanseniaspora, Kazachstania, Kluyveromyces, Pichia, Starmerella, Torulaspora, Yarrowia, Candida, and Schizosaccharomyces were selected. All except Candida and Schizosaccharomyces were chosen because of their numerous applications in biotechnology. We considered Candida as a genus of clinical importance as well as having biotechnological potential [34]; we included Schizosaccharomyces as an outlier in the taxonomic rank because it belongs to a different Order. Within each genus, all available assemblies from all species were downloaded from the NCBI Genome repository except for known hybrid species. Since the number of available draft genomes for Saccharomyces cerevisiae was more than 1000, we decided to consider a restricted number of them; we selected the 79 draft genomes analyzed by Legras and coworkers, which reflect the genetic variability of the species [35]. Overall, a total of 644 assemblies deposited in the NCBI Genome database and classified as the above-mentioned genera were downloaded to be part of the dataset. These assemblies showed different completeness statuses, as reported in Figure 1, and quality. The scaffold assembly level was the most represented (64.3%), while complete and chromosome status were less common. Figure 1. The completeness status of assemblies belonging to the analyzed dataset. Contigs are sequence fragments; scaffolds are contigs connected by spanned gaps; chromosome status indicates that one or more chromosomes could be complete, and other sequences could be made by scaffold or contigs; and complete status refers to the entire chromosome sequences without gaps, including plasmid(s), if present. Figure 1. The completeness status of assemblies belonging to the analyzed dataset. Contigs are sequence fragments; scaffolds are contigs connected by spanned gaps; chromosome status indicates that one or more chromosomes could be complete, and other sequences could be made by scaffold or contigs; and complete status refers to the entire chromosome sequences without gaps, including plasmid(s), if present. Table S1 provides a list of the considered genomes, with details on the genus, species, accession number, indication of reference strains, identification of CBS strains, sequencing technology, coverage, and BUSCO-based completeness. J. Fungi 2024,10, 646 4 of 20 We created a dataset with 77 Saccharomyces interspecific hybrid strains selected from previous publications [ 30 – 33 ] (Table S2), downloading SRA files from the NCBI Sequence Read Archive. Reads were trimmed for adapter removal and quality filtered with Trimmomatic [ 36 ] and de novo assembled with SPAdes [ 37 ]. Furthermore, the proportion of reads that mapped against the different parental species of each hybrid was calculated with sppIDer [38]. 2.2. Species Delineation Using ANI Calculation, In Silico Alignment of D1/D2 Region of LSU Ribosomal DNA, and the Selection of Orthologous Genes (MAOG) Three methods were used to obtain identity values between assemblies within each genus. One is based on the alignment-free ANI calculation; the second is based on the in silico extraction of ribosomal barcode sequence D1/D2 from assemblies; and the third employs a multiple sequence alignment (MAOG). The scheme of the applied methodology is described in Figure 2. J. Fungi 2024, 10, 646 4 of 21 Table S1 provides a list of the considered genomes, with details on the genus, species, accession number, indication of reference strains, identification of CBS strains, sequencing technology, coverage, and BUSCO-based completeness. We created a dataset with 77 Saccharomyces interspecific hybrid strains selected from previous publications [30–33] (Table S2), downloading SRA files from the NCBI Sequence Read Archive. Reads were trimmed for adapter removal and quality filtered with Trimmomatic [36] and de novo assembled with SPAdes [37]. Furthermore, the proportion of reads that mapped against the different parental species of each hybrid was calculated with sppIDer [38]. 2.2. Species Delineation Using ANI Calculation, In Silico Alignment of D1/D2 Region of LSU Ribosomal DNA, and the Selection of Orthologous Genes (MAOG) Three methods were used to obtain identity values between assemblies within each genus. One is based on the alignment-free ANI calculation; the second is based on the in silico extraction of ribosomal barcode sequence D1/D2 from assemblies; and the third employs a multiple sequence alignment (MAOG). The scheme of the applied methodology is described in Figure 2. J. Fungi 2024, 10, 646 5 of 21 Figure 2. The workflow and tools used to analyze the dataset of assemblies using the three methods, D1/D2 region alignment, multiple alignment of orthologous genes (MAOG), and FastANI. The assemblies downloaded from the NCBI Genome database were analyzed with FastANI v1.32 [29]. FastANI is specifically designed to estimate ANI within the 70–100% identity range. This configuration enables FastANI to generate mappings with alignment identities that are close to 70% or higher. The analysis was run using the following parameters: fragment length of 3000, kmer size of 16, and minimum fraction of shorter genome coverage of 50%. It was applied to assemblies belonging to different species within each genus separately, with an “all against all” approach, using the same genomes as queries and references and using the—matrix parameter. The outputs consisted of matrices containing the identity percentage between genomes of strains in the same species. Barrnap v0.9 (https://github.com/tseemann/barrnap) (accessed on 7 September 2023) was used to predict and extract 28S sequences from the considered assemblies. From these, D1/D2 regions of LSU ribosomal DNA were extracted using in silico PCR (https://github.com/simonrharris/in_silico_pcr) (accessed on 20 September 2023). Primers were NL1-forward (5′-GCATATCAATAAGCGGAGGAAAAG-3′) and NL4-reverse (5′- GGTCCGTGTTTCAAGACGG-3′) or LR6-reverse (5′-CGCCAGTTCTGCTTACC-3′). The extracted D1/D2 sequences were then classified according to the genus they belonged to according to the NCBI Genome database and aligned with MAFFT v7.390; identity values were calculated from these alignments. For the MAOG method, assemblies were analyzed with BUSCO v5.2.2 [39]. The Fungi Odb10 database was used to extract orthologous that are conserved in the kingdom Fungi, with the AUGUSTUS gene predictor parameter on. Assemblies resulting in fewer than 200 orthologous genes annotated as “single complete copy” by BUSCO were filtered out. The remaining assemblies were classified according to their genus in the NCBI Genome database. The intersection of orthologous sequences identified in all the assemblies of each genus was then listed. Within each species, the sequences of all these filtered orthologs were put together into a multi-FASTA file for every ortholog. For each of these files, nucleotide sequences were translated and aligned with MAFFT multiple alignment software v7.390 [40]. The alignments were then back-translated and trimmed with TrimAl v1.4.1 (http://trimal.cgenomics.org/) (accessed on 12 September 2023) . Subsequently, all orthologous gene alignments of each genus were concatenated into a single file, with as many records as there were available assemblies for that genus. Each record contained the aligned sequence of each of the orthologs in the list for that genus, one after the other. The square root of the pairwise distances (D) between these alignments Figure 2. The workflow and tools used to analyze the dataset of assemblies using the three methods, D1/D2 region alignment, multiple alignment of orthologous genes (MAOG), and FastANI. J. Fungi 2024,10, 646 5 of 20 The assemblies downloaded from the NCBI Genome database were analyzed with FastANI v1.32 [ 29 ]. FastANI is specifically designed to estimate ANI within the 70–100% identity range. This configuration enables FastANI to generate mappings with alignment identities that are close to 70% or higher. The analysis was run using the following parameters: fragment length of 3000, kmer size of 16, and minimum fraction of shorter genome coverage of 50%. It was applied to assemblies belonging to different species within each genus separately, with an “all against all” approach, using the same genomes as queries and references and using the—matrix parameter. The outputs consisted of matrices containing the identity percentage between genomes of strains in the same species. Barrnap v0.9 (https://github.com/tseemann/barrnap) (accessed on 7 September 2023) was used to predict and extract 28S sequences from the considered assemblies. From these, D1/D2 regions of LSU ribosomal DNA were extracted using in silico PCR (https: //github.com/simonrharris/in_silico_pcr) (accessed on 20 September 2023). Primers were NL1-forward (5 ′ -GCATATCAATAAGCGGAGGAAAAG-3 ′ ) and NL4-reverse (5 ′ - GGTCCGTGTTTCAAGACGG-3 ′ ) or LR6-reverse (5 ′ -CGCCAGTTCTGCTTACC-3 ′ ). The extracted D1/D2 sequences were then classified according to the genus they belonged to according to the NCBI Genome database and aligned with MAFFT v7.390; identity values were calculated from these alignments. For the MAOG method, assemblies were analyzed with BUSCO v5.2.2 [ 39 ]. The Fungi Odb10 database was used to extract orthologous that are conserved in the kingdom Fungi, with the AUGUSTUS gene predictor parameter on. Assemblies resulting in fewer than 200 orthologous genes annotated as “single complete copy” by BUSCO were filtered out. The remaining assemblies were classified according to their genus in the NCBI Genome database. The intersection of orthologous sequences identified in all the assemblies of each genus was then listed. Within each species, the sequences of all these filtered orthologs were put together into a multi-FASTA file for every ortholog. For each of these files, nucleotide sequences were translated and aligned with MAFFT multiple alignment software v7.390 [ 40 ]. The alignments were then back-translated and trimmed with TrimAl v1.4.1 (http://trimal.cgenomics.org/) (accessed on 12 September 2023). Subsequently, all orthologous gene alignments of each genus were concatenated into a single file, with as many records as there were available assemblies for that genus. Each record contained the aligned sequence of each of the orthologs in the list for that genus, one after the other. The square root of the pairwise distances (D) between these alignments was calculated using the dist.alignment function (parameter matrix set to identity) of the adegenet R package v1.7. Then, pairwise distances were transformed into identity values by calculating the difference in 1 minus the squared value of D. 2.3. Statistical Analysis for the Comparison between D1/D2 Sequence, MAOG, and ANI Calculation Outliers were assumed to be wrongly identified species and were corrected. Thus, assemblies with a FastANI identity value above 99%, D1/D2 identity value above 99%, and alignment-based identity value above 95% were considered to belong to the same species, and assemblies with a FastANI identity value below 90% and an alignment-based identity value below 90% were designated as different species. Boxplots were obtained with the ggplot2 R package v2 3.4.2 function geom_boxplot, taking into account the Boolean variable of whether two assemblies were from the same species or not. Density plots of the identity values obtained with each of the three methods were obtained with the R base function density. In addition, we obtained the local minima for each method, both for the full dataset and the S. cerevisiae dataset, from density distributions of identity values computed by the function density from the R base. A paired t-test was carried out by applying the Welch correction due to differences in variance distribution between groups of samples on the identity values from either MAOG or D1/D2 sequence analysis versus the identity values from FastANI for the same species assembly comparisons. Moreover, a Cohen’s D test was performed to evaluate the effect size. PCA was performed with the R function prcomp v4.2.1. J. Fungi 2024,10, 646 6 of 20 3. Results 3.1. Species Delineation Based on ANI Calculation The first objective was to assess how effective FastANI analysis was in identifying strains belonging to the same species and discriminating genomes belonging to different species, considering those reported in NCBI to be valid. When FastANI was run on the dataset composed of 644 assemblies, the tool provided pairwise comparison values for all genome pairs tested (complete data are shown in Tables S3 and S4 ). Regardless of genus and species, the similarity identities obtained through FastANI proved effective in distinguishing between different species. Different cutoffs were defined depending on genus, but overall, if two genomes showed identity values below 90%, they belonged to separate species, as described in detail below. Differently, genome pairs showing values above 94–95% were classified among the same species. Table 1gives a detailed breakdown of the cutoffs defined for each species analyzed within each individual genus. Table 1. The identity values cutoffs were calculated by FastANI (Average Nucleotide Identity calculation) among the species belonging to the twelve considered genera. The within-species cutoff is the ANI value that identifies strains within the same species; conversely, the different-species cutoff is the ANI value specifying assemblies belonging to different species. Genus Within-Species Cutoff Between-Species Cutoff Candida >96% <88% Debaryomyces >92% <90% Hanseniaspora >97% <90% Kazachstania >98% <89% Kluyveromyces >95% <79% Pichia >98% <82% Rhodotorula >95% <80% Saccharomyces >92% <81% Schizosaccharomyces >98% <80% Starmerella >97% <85% Torulaspora >95% <90% Yarrowia >98% <90% Candida. Within the genus Candida, we analyzed a total of 114 genomes, including 12 species. Where genomes belonged to the same species, the similarity identity exceeded 96%, while ANI values between strains of different species were below 88%. The most represented species, C. albicans (comprising 70 assemblies), showed intraspecies ANI values above 98%. Since the identity exceeds 96% between strains of the same species within the Candida genus, this value is proposed as the cutoff for the species delineation. An ANI value of 92% between C. sanyaensis and C. sojae was observed. This intermediate value, falling between the threshold for the delineation of the same or different species, suggests a greater similarity between these two species that may be the result of a hybridization event. Debaryomyces. Within the Debaryomyces genus, 19 assemblies encompassing seven species were analyzed (Figure 3A, Table S3). FastANI demonstrated effective discrimination between species, with pairwise ANI values consistently falling below 90%. Discrepancies were identified in certain assemblies belonging to the D. hansenii species: assemblies GCA_006942225.1 and GCA003349505.1 displayed low identity values (below 90%) with other genomes belonging to D. hansenii species, including the reference genome of CBS767T, but exhibited high ANI values (99.41% and 99.45%, respectively) with D. fabryi. Similarly, strains MTCC 234 (GCA_000239015.2) and J6 (GCA_001682995.1) originally identified as J. Fungi 2024,10, 646 7 of 20 D. hansenii showed very high ANI values (99.92% and 99.95%, respectively) with D. subglobosus and between each other (100% and 99.92%, respectively). These comparisons suggest a potential misidentification of the isolates. J. Fungi 2024, 10, 646 9 of 21 of them displayed identity values below 95%. Further studies should be performed to ascertain whether the typical ANI value for intraspecies S. kudriavzevii can be established at 92%, or if ANI values are influenced by hybridization events, horizontal gene transfer, or purity of the sequenced DNA sample. Nonetheless, FastANI effectively discriminates this species from the others, as the identity values between them were below 81%. As observed for other genera, certain ambiguous scenarios were identified. For example, S. eubayanus GCA_013181345.1 exhibited ANI values of around 90–92% with both S. cerevisiae and S. uvarum, suggesting the possibility of its being a hybrid. In contrast, the discriminating value was 98% among other S. eubayanus genomes. Figure 3. Heatmaps of the FastANI ID values obtained from the pairwise comparison of the Hanseniaspora (A), Debaryomyces (B), and Saccharomyces (C) assemblies. Schizosaccharomyces. Despite the limited number of genomes analyzed, with S. pombe being the predominant species, FastANI demonstrated a reliable ability to distinguish between different species. Specifically, among the S. pombe species, ANI values were notably high, around 99%. The pairwise identity values between S. pombe assemblies and assemblies belonging to the other species were considerably low, below 80%. FastANI identified an inconsistency between two strains belonging to the S. japonicus species, exhibiting a reciprocal identity below 90%. Starmerella. Despite the low number of Starmerella genomes (20 genomes and 12 species), the analysis confirmed that the same species ANI value in this dataset was greater than 97%. Clear boundaries between species were evident, as the ANI between different species remained under 85%. Only the strain S. bombicola PYCC 5882 (GCA_003033785.1) had a lower ANI value compared with other S. bombicola genomes. This discrepancy may be attributed to experimental artifacts stemming from the lack of purity in the original sample, influencing accurate species delineation, or to the above-mentioned genomic element interchanges. Torulaspora. Despite the limited number of available assemblies, this genus showed a complex structure. Clear boundaries between species were evident, as the ANI values between different species consistently remained below 90%. For some species, such as T. microellipsoides, T. franciscae, and T. delbrueckii, the same-species identity value exceeded 95%. However, inconsistencies were observed among the T. pretoriensis species. Notably, five assemblies belonging to T. pretoriensis displayed lower identity values with other assemblies from the same species. Given that these genomes were part of the same project (Bioproject PRJNA623867) and that no public peer-reviewed articles were Figure 3. Heatmaps of the FastANI ID values obtained from the pairwise comparison of the Hanseniaspora (A), Debaryomyces (B), and Saccharomyces (C) assemblies. The D. hansenii assemblies GCA_006942205.1, GCA_006942215.1, GCA_006942225.1, GCA_006942235.1, and GCA_006942305.1 showed reciprocal ANI values exceeding 95%, indicating that they belong to the same species. However, they displayed lower ANI values (approximately 92%) than the other three assemblies of the same species (GCA_006942325.1, GCA_016097515.1, and GCA_016097625.1). A pairwise identity of 92% may be characteristic of the D. hansenii species or could be attributed to complex genomic alterations such as hybridization with closely related species. Higher identity percentages with other stains among the same genus Debaryomyces were not observed. Hanseniaspora. Within Hanseniaspora genus, 17 species were deposited in the NCBI Genome database, with a total number of 31 genomes. H. uvarum was the species with the highest number of deposited assemblies. The application of FastANI to the Hanseniaspora genus demonstrated the tool’s effectiveness in delineating species boundaries, evident in ANI values below 90% between different species (Figure 3B, Table S3). ANI values exceeding 97% were consistently observed among genomes belonging to the same species. However, a noteworthy exception involves two genomes of H. occidentalis exhibiting a reciprocal ANI value of 91.84%. This inconsistent result was due to a misidentification during the sequencing of the D1/D2 region [17,41]. Kazachstania. This genus consisted of 28 assemblies, grouped in 21 species. Although few genomes belonging to the same species were available for this genus, FastANI analysis evidenced highly discriminatory values among the different species of Kazachstania genus. ANI values below 89% effectively discriminated between various species, with the exception of K. exigua and K. turicensis, which displayed a remarkably high identity percentage of 99.86%. This suggests that these two species likely belong to the same taxonomic unit. Since more than one genome was available only for K. unispora,K. barnettii, and K. servazzii, the proposed cutoff that determines the same species can be applied only to them. In particular, ANI cutoff values exceeding 98% can be defined for the delineation of the species. Kluyveromyces. The 27 assemblies downloaded from the NCBI Genome dataset belonged to seven species. Of these, three were represented by a single assembly, while for the remaining four species, two or more assemblies were analyzed. The intraspecies ANI J. Fungi 2024,10, 646 8 of 20 value exceeding 95% was observed among the K. marxianus assemblies, which were the most abundant, and was also consistently noted for the other species, namely K. lactis, K. dobzhanskii, and K. aestuarii. Pichia. Thirty-two assemblies belonging to the Pichia genus and included in the analysis were grouped into 13 species. Similar to the Kazachstania genus, some species were represented by several assemblies, and others were represented by only one assembly. However, boundaries between Pichia species were defined by ANI values below 82%. Genomes of the P. kudriavzevii species, the most numerous, displayed pairwise identity values ranging from 98.90% to 100%. The case of P. membranifaciens is noteworthy, as only two assemblies belonging to the same species showed an identity value of 85.74%. Again, this observation suggests possible misidentification of isolates or complex genetic interchange events. Rhodotorula. The Rhodotorula dataset encompassed 132 genomes, with most of them belonging to R. mucilaginosa (101 assemblies) and R. toruloides (18 genomes). FastANI analysis defined clear boundaries between species, with ANI values below 80%. For species with multiple assemblies, such as R. mucilaginosa,R. toruloides,R. kratochvilovae, and R. paludigena, a same-species cutoff of >95% was observed. However, exceptions were noted, particularly in 7 out of 101 strains within R. mucilaginosa, displaying ANI values near 90%. This value suggests the potential existence of hybrids between R. mucilaginosa and other species, or the misidentification of a different as-yet-uncharacterized species, especially considering that the identity values for the other 94 strains were above 95%. R. toruloides presents a complex scenario, with higher variability. Indeed, NBRC10032 (GCA_007990605.1) exhibited very low ANI values (below 85%) compared with all the other strains. The remaining strains appeared to be divided into two clusters, with identity values over 96% in one cluster and around 90% in the other. The first cluster, which included the reference strain NP11 (GCA_000320785.2), was made up of GCA_000258745.1, GCA_000320785.2, GCA_000988805.1, GCA_001542305.1, GCA_001600115.1, GCA_001600155.1, GCA_003234015.1, and GCA_016808315.1 The second cluster consisted of GCA_000222205.2, GCA_000988875.1, GCA_000988875.2, GCA_001255795.1, GCA_001456015.1, GCA_001542265.1, and GCA_00160 0135.1. This divergence suggests potential misidentification based on unique genetic markers. Interestingly, GCA_000222205.2, belonging to the second cluster, is documented as a R. glutinis genome, despite its assignment to the R. toruloides taxon in the NCBI Genome database. However, it exhibited very low identity with assemblies assigned to the R. glutinis species in the NCBI Genome database (Table S3), underscoring inconsistencies in species delineation, which impact database information. Furthermore, four R. toruloides assemblies, GCA_001600215.1, GCA_005387725.1, GCA_007990605.1, and GCA_016808315.1, showed ANI values below 90% with all the others belonging to the same species, hinting at the possibility of belonging to a distinct yet unaccounted-for species, a hypothesis that warrants experimental confirmation. Saccharomyces. This largest dataset consisted of the assemblies from the Saccharomyces genus encompassing seven species, with S. cerevisiae being the most prevalent. FastANI successfully identified species boundaries, especially for S. cerevisiae,S. eubayanus,S. mikatae, and S. paradoxus (Figure 3C, Table S3). For these species, a cutoff value of 95% identified genomes belonging to the same species. The comparison between S. eubayanus and S. uvarum genomes showed ANI values of approximately 92%, indicating a close relationship between the two species, as corroborated by previous phylogenomic analyses [ 35 ]. Nevertheless, the cutoff of 95% remains effective for identifying genomes of S. eubayanus or S. uvarum. Greater variability was observed among the 23 S. kudriavzevii genomes. Indeed, FastANI analysis revealed pairwise identities between genomes of the same species ranging from 92% to 100%. When comparing ANI values between the assembly of the reference strain S. kudriavzevii CR85 (GCA_003327635.1) and the other 22 strains, only six of them displayed identity values below 95%. Further studies should be performed to ascertain whether the typical ANI value for intraspecies S. kudriavzevii can be established at 92%, or J. Fungi 2024,10, 646 9 of 20 if ANI values are influenced by hybridization events, horizontal gene transfer, or purity of the sequenced DNA sample. Nonetheless, FastANI effectively discriminates this species from the others, as the identity values between them were below 81%. As observed for other genera, certain ambiguous scenarios were identified. For example, S. eubayanus GCA_013181345.1 exhibited ANI values of around 90–92% with both S. cerevisiae and S. uvarum, suggesting the possibility of its being a hybrid. In contrast, the discriminating value was 98% among other S. eubayanus genomes. Schizosaccharomyces. Despite the limited number of genomes analyzed, with S. pombe being the predominant species, FastANI demonstrated a reliable ability to distinguish between different species. Specifically, among the S. pombe species, ANI values were notably high, around 99%. The pairwise identity values between S. pombe assemblies and assemblies belonging to the other species were considerably low, below 80%. FastANI identified an inconsistency between two strains belonging to the S. japonicus species, exhibiting a reciprocal identity below 90%. Starmerella. Despite the low number of Starmerella genomes (20 genomes and 12 species), the analysis confirmed that the same species ANI value in this dataset was greater than 97%. Clear boundaries between species were evident, as the ANI between different species remained under 85%. Only the strain S. bombicola PYCC 5882 (GCA_003033785.1) had a lower ANI value compared with other S. bombicola genomes. This discrepancy may be attributed to experimental artifacts stemming from the lack of purity in the original sample, influencing accurate species delineation, or to the above-mentioned genomic element interchanges. Torulaspora. Despite the limited number of available assemblies, this genus showed a complex structure. Clear boundaries between species were evident, as the ANI values between different species consistently remained below 90%. For some species, such as T. microellipsoides,T. franciscae, and T. delbrueckii, the same-species identity value exceeded 95%. However, inconsistencies were observed among the T. pretoriensis species. Notably, five assemblies belonging to T. pretoriensis displayed lower identity values with other assemblies from the same species. Given that these genomes were part of the same project (Bioproject PRJNA623867) and that no public peer-reviewed articles were associated with it, a misidentification can be speculated. The relatively low sequencing coverage (approximately 40 × ) is unlikely to impact the results, as other assemblies with consistent ANI values had the same coverage. Yarrowia. The discriminating ANI value for Yarrowia species was extremely high, with ANI values between genomes belonging to different species consistently below 90%. Conversely, within the same species, especially in the case of Y. lipolytica, represented by 24 genomes, the identity values were very high, exceeding 99%. However, we observed that some strains were assigned to the wrong species. Indeed, the NCAIM 3590 strain (GCA_003571375.1) appeared as an outlier, displaying high ANI values (99.5%) with Y. bubula strains, suggesting a potential misidentification. Additionally, assembly GCA_001069125.1 represented a unique case: it was identified as Y. lipolytica but presented high FastANI identity values only with some of the other Y. lipolytica assemblies and one of the Y. bubula; this suggested a potential hybridization event. However, based on D1/D2 results, it is classified as Y. lipolytica, which may explain the classification of this strain in the database. 3.2. Species Delineation Based on In Silico Extraction of D1/D2 Region and MAOG The other objective was to evaluate the effectiveness of genome-based-D1/D2 region analysis and multigenic alignment comparison (MAOG) in the determination of species and to compare the results obtained from these analyses with those obtained by FastANI. Despite the analysis being applied to the entire dataset of 644 genomes, the D1/D2 analysis has been performed only on 404 assemblies (62.7%), because the D1/D2 region was not found in the remaining 240 assemblies. Similarly, the MAOG method was applied to 484 assemblies out of 644, constituting 75.2% of the total, of which at least 200 orthologous J. Fungi 2024,10, 646 16 of 20 To our knowledge, this is the first comprehensive study on the use of ANI as a method to assign a strain to a species in different yeast genera. By evaluating overall ANI values, a common threshold of 94–95% emerged across all the genera analyzed in this study, in agreement with the findings of others [ 6 , 47 ]. Regarding intra-genus data, we can observe that for some genera, such as Candida and Hanseniaspora, higher cutoffs (around 96%) may be appropriate. It is important to acknowledge exceptions to these thresholds, as noted for some of the genera studied in this work. It should be noted that we cannot exclude the impact of sequencing technology on the definition of the ANI cutoffs proposed in this study. As mentioned previously, the pros and cons concerning the choice of sequencing platform and pipeline to be applied for obtaining the assembly and its analysis must be considered. Although the work on the application of FastANI to determine cutoffs between bacterial species Jain et al. [ 29 ] showed that this tool is not affected by genomic completeness, it would be important to investigate the influence of the sequencing platform and the tools used for assembly on the results of ANI analysis. In addition, FastANI may be used to deduce complex genome modification events such as hybridization and to identify experimental artifacts such as species misidentification. In the present work, very high identity values between assemblies that were supposed to belong to different species were observed, suggesting the presence of hybrid organisms. Such intermediate values were lower than those obtained from the same species comparison and higher than those resulting from the comparison with different species. This is quite common in the Saccharomyces genus, from which a variety of hybrid strains have been isolated, especially in industrial environments, including S. pastorianus in beer [ 60 ] and S. cerevisiae × S. uvarum and S. cerevisiae × S. kudriavzevii in wine fermentations [ 61 , 62 ]. When we tested the three methods on a dataset composed of previously characterized Saccharomyces hybrids, the analyses indicated that a considerably larger portion of a species genome would be required to achieve noticeably higher identity values with both the MAOG and the FastANI methods (Figure 8). The MAOG method appears to be more resilient to very high identity values between hybrids and pure species, which would thus avoid erroneous delineation of these assemblies. Nevertheless, some instances, especially in the case of S. uvarum, exhibited identity close to the conspecificity threshold defined above. The reason why the rest of the species in the study behaved differently is difficult to deduce from our dataset. Interestingly, the FastANI method tended to produce identity values surpassing the threshold in hybrids when the contribution of a parent species to the genome was 25% or more (Figure 8). This feature may be strategically employed in a pipeline for species delineation, particularly when FastANI identity values surpass the threshold for more than one species, markedly increasing the probability of classifying the assembly as a hybrid. 5. Conclusions As genomic data continue to proliferate, tools like FastANI, not used as a standalone but as part of a multifaceted taxonomic strategy, will contribute to refining our understanding of yeast diversity and taxonomy, thus paving the way for a more accurate and nuanced species classification. In this work, we have analyzed an extensive dataset and proposed cutoffs that can be a guide for assigning a yeast strain to a known species. Moreover, we suggest using FastANI on data sets that are as inclusive as possible, to explore comparisons with different species within the same genus. This analysis should be part of a holistic approach that includes phenotypic and morphological analyses. Our findings also emphasize the need for such adaptable approaches in the face of complexities and potential hybridization scenarios. J. Fungi 2024,10, 646 17 of 20 Supplementary Materials: The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/jof10090646/s1; Figure S1. Density curves of the D1/D2 region are limited to identity values between 95% and 100%. Y-axis is in logarithmic scale. Green curve depicts values from conspecific assemblies and the grey curve corresponds to ID values from different species assemblies. Figure S2. (A) Density estimation of the pairwise identity values obtained for Saccharomyces assemblies in the original dataset, calculated with D1/D2, MAOG, and FastANI methods. (B) Histograms of pairwise identity values among Saccharomyces assemblies according to the species they belong to. Color indicates the peak at which an identity value was found in the density plot in panel A. This classification was made taking into account the minimum nodes calculated for each method (D1/D2: 0.987 and 0.995; MAOG: 0.858 and 0.927; and FastANI: 0.845 and 0.938). Figure S3. Principal component analysis (PCA) was performed to evaluate the capability of D1/D2 alignment, MAOG, and FastANI to delineate species. Table S1. Accession numbers of assemblies used in this study, divided by genus. Reference genomes, intended as “high quality genome datasets that NCBI and the community have identified as being important” (https://www.ncbi.nlm.nih.gov/) (accessed on 4 July 2023), are highlighted in yellow. For each assembly, the coverage and BUSCO score (%) are reported. Table S2. Raw data of hybrid Saccharomyces strains sequenced, with related publications, are used in this work. The table provides the species involved in each hybrid strain, with the related proportion expressed in percentage. The number of contigs, coverage, and N50 are reported as quality parameters of assembly that are also reported. Table S3. Identity matrices showing similarity values obtained with FastANI, divided by genus. Table S4. Average nucleotide identities between species, as determined using fastANI (pairwise view), see Methods (kmer size of 16, fragment length of 3000, minimum fraction of shorter genome coverage of 50%). Table S5. Identity matrices showing similarity values obtained with D1/D2 region analysis, divided by genus. Table S6. Identity matrices showing similarity values obtained with MAOG, divided by genus. Table S7. Results of the Wilcoxon rank-sum test performed between ID values obtained by each of the three methods from each of the genera. W is the Wilcoxon statistic for the first group. Normal approximation for p-value calculation was used in the case of comparisons made between groups with more than 50 ID values. A p-value lower than 0.05 (highlighted in bold) indicates that the hypothesis that both sets of data were equal can be rejected. Author Contributions: Conceptualization, C.C., J.A.-D.-R., M.V.B.D., G.I., A.Q. and P.S.C.; methodology, C.C., J.A.-D.-R., M.V.B.D. and G.I.; Investigation and software, C.C., J.A.-D.-R., M.V.B.D. and G.I.; data curation and writing—original draft preparation, C.C. and J.A.-D.-R.; writing—review and editing, C.C., J.A.-D.-R., M.V.B.D., G.I., A.Q. and P.S.C.; project administration: A.Q. and P.S.C.; funding acquisition: P.S.C. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3—Call for tender No. 341 of 15 March 2022 of the Italian Ministry of University and Research funded by the European Union—NextGenerationEU. Award Number: Project code PE00000003, Concession Decree No. 1550 of 11 October 2022 adopted by the Italian Ministry of University and Research, CUP D93C22000890001, Project title “ON Foods—Research and innovation network on food and nutrition Sustainability, Safety and Security—Working ON Foods”. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. Conflicts of Interest: Author Giovanni Iacono was employed by the European Food Safety Authority. 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