Vancomycin-resistant Staphylococcus aureus (VRSA) can overcome the cost of antibiotic resistance and may threaten vancomycin's clinical durability.

Vancomycin has proven remarkably durable to resistance evolution by Staphylococcus aureus despite widespread treatment with vancomycin in the clinic. Only 16 cases of vancomycin-resistant S. aureus (VRSA) have been documented in the United States. It is thought that the failure of VRSA to spread is partly due to the fitness cost imposed by the vanA operon, which is the only known means of high-level resistance. Here, we show that the fitness cost of vanA-mediated resistance can be overcome through laboratory evolution of VRSA in the presence of vancomycin. Adaptation to vancomycin imposed a tradeoff such that fitness in the presence of vancomycin increased, while fitness in its absence decreased in evolved lineages. Comparing the genomes of vancomycin-exposed and vancomycin-unexposed lineages pinpointed the D-alanine:D-alanine ligase gene (ddl) as the target of loss-of-function mutations, which were associated with the observed fitness tradeoff. Vancomycin-exposed lineages exhibited vancomycin dependence and abnormal colony morphology in the absence of drug, which were associated with mutations in ddl. However, further evolution of vancomycin-exposed lineages in the absence of vancomycin enabled some evolved lineages to escape this fitness tradeoff. Many vancomycin-exposed lineages maintained resistance in the absence of vancomycin, unlike their ancestral VRSA strains. These results indicate that VRSA might be able to compensate for the fitness deficit associated with vanA-mediated resistance, which may pose a threat to the prolonged durability of vancomycin in the clinic. Our results also suggest vancomycin treatment should be immediately discontinued in patients after VRSA is identified to mitigate potential adaptations.

Applications of machine learning on electronic health record data to combat antibiotic resistance.

There is growing excitement about the clinical use of artificial intelligence and machine learning technologies. Advancements in computing and the accessibility of machine learning frameworks enable researchers to easily train predictive models using electronic health record data. However, there are several practical factors that must be considered when employing machine learning on electronic health record data. We provide a primer on machine learning and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct machine learning models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. Machine learning shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing potential dangers of, and barriers to, implementation of machine learning models in the clinic.

Many purported pseudogenes in bacterial genomes are bona fide genes.

BACKGROUND: Microbial genomes are largely comprised of protein coding sequences, yet some genomes contain many pseudogenes caused by frameshifts or internal stop codons. These pseudogenes are believed to result from gene degradation during evolution but could also be technical artifacts of genome sequencing or assembly. RESULTS: Using a combination of observational and experimental data, we show that many putative pseudogenes are attributable to errors that are incorporated into genomes during assembly. Within 126,564 publicly available genomes, we observed that nearly identical genomes often substantially differed in pseudogene counts. Causal inference implicated assembler, sequencing platform, and coverage as likely causative factors. Reassembly of genomes from raw reads confirmed that each variable affects the number of putative pseudogenes in an assembly. Furthermore, simulated sequencing reads corroborated our observations that the quality and quantity of raw data can significantly impact the number of pseudogenes in an assembler dependent fashion. The number of unexpected pseudogenes due to internal stops was highly correlated (R2 = 0.96) with average nucleotide identity to the ground truth genome, implying relative pseudogene counts can be used as a proxy for overall assembly correctness. Applying our method to assemblies in RefSeq resulted in rejection of 3.6% of assemblies due to significantly elevated pseudogene counts. Reassembly from real reads obtained from high coverage genomes showed considerable variability in spurious pseudogenes beyond that observed with simulated reads, reinforcing the finding that high coverage is necessary to mitigate assembly errors. CONCLUSIONS: Collectively, these results demonstrate that many pseudogenes in microbial genome assemblies are actually genes. Our results suggest that high read coverage is required for correct assembly and indicate an inflated number of pseudogenes due to internal stops is indicative of poor overall assembly quality.

Evaluating the long-term portrayal of antibiotic resistance in major U.S. newspapers.

BACKGROUND: Popular media play a critical role in informing the public about antibiotic resistance, which has remained a health concern for over seven decades. Media attention increases the notoriety of antibiotic resistance and shapes the public's perception of its severity, causes, and solutions. Therefore, it is critical the media accurately portray scientific knowledge that may shape personal and policy responses to antibiotic resistance. METHODS: We analyzed articles from two major U.S. newspapers, The New York Times and Los Angeles Times, from 1940 to 2019 to assess trends in sentiment and lexicon surrounding antibiotic and antimicrobial resistance. RESULTS: We observed a gradual increase in the number of relevant articles about resistance, although far fewer than other topics with comparable mortality rates. We found a consistently threatening portrayal of antibiotic resistance as a crisis, reflected in the usage of terms such as "superbug" to refer to some pathogens. Governmental agencies responsible for determining antibiotic usage policies were infrequently mentioned in articles. Blame for resistance was almost exclusively attributed to inappropriate antibiotic use, mainly in animals, rather than appropriate uses of antibiotics. CONCLUSIONS: Collectively, our results provide insights into how popular media can more accurately inform the public about antibiotic resistance. Potential changes include increasing news coverage, avoiding fear-mongering, and adequately conveying the multiple uses of antibiotics that can potentiate resistance.

Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis.

BACKGROUND: The development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect coverage, accuracy, and biases in the resulting compositional profiles. RESULTS: Of the workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. Use of a high-fidelity polymerase, or a lower-fidelity polymerase with an increased PCR elongation time, limited chimera formation. Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (coverage) and fraction of correct sequences (accuracy). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest accuracy (100%) but had a coverage of only 52%. Using mothur to assemble and denoise V4-V4 reads resulted in a coverage of 75%, albeit with marginally lower accuracy (99.5%). CONCLUSIONS: Optimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. These considerations will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health.

Balancing Trade-Offs Imposed by Growth Media and Mass Spectrometry for Bacterial Exometabolomics.

The bacterial exometabolome consists of a vast array of specialized metabolites, many of which are only produced in response to specific environmental stimuli. For this reason, it is desirable to control the extracellular environment with a defined growth medium composed of pure ingredients. However, complex (undefined) media are expected to support the robust growth of a greater variety of microorganisms than defined media. Here, we investigate the trade-offs inherent to a range of complex and defined solid media for the growth of soil microorganisms, production of specialized metabolites, and detection of these compounds using direct infusion mass spectrometry. We find that complex media support growth of more soil microorganisms, as well as allowing for the detection of more previously discovered natural products as a fraction of total m/z features detected in each sample. However, the use of complex media often caused mass spectrometer injection failures and poor-quality mass spectra, which in some cases resulted in over a quarter of samples being removed from analysis. Defined media, while more limiting in growth, generated higher quality spectra and yielded more m/z features after background subtraction. These results inform future exometabolomic experiments requiring a medium that supports the robust growth of many soil microorganisms. IMPORTANCE Bacteria are capable of producing and secreting a rich diversity of specialized metabolites. Yet, much of their exometabolome remains hidden due to challenges associated with eliciting specialized metabolite production, labor-intensive sample preparation, and time-consuming analysis techniques. Using our versatile three-dimensional (3D)-printed culturing platform, SubTap, we demonstrate that rapid exometabolomic data collection from a diverse set of environmental bacteria is feasible. We optimized our platform by surveying Streptomyces isolated from soil on a variety of media types to assess viability, degree of specialized metabolite production, and compatibility with downstream LESA-DIMS analysis. Ultimately, this will enable data-rich experimentation, allowing for a better understanding of bacterial exometabolomes.

FindNonCoding: rapid and simple detection of non-coding RNAs in genomes.

SUMMARY: Non-coding RNAs are often neglected during genome annotation due to their difficulty of detection relative to protein coding genes. FindNonCoding takes a pattern mining approach to capture the essential sequence motifs and hairpin loops representing a non-coding RNA family and quickly identify matches in genomes. FindNonCoding was designed for ease of use and accurately finds non-coding RNAs with a low false discovery rate. AVAILABILITY AND IMPLEMENTATION: FindNonCoding is implemented within the DECIPHER package (v2.19.3) for R (v4.1) available from Bioconductor. Pre-trained models of common non-coding RNA families are included for bacteria, archaea and eukarya. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of antibiotic pairs that evade concurrent resistance via a retrospective analysis of antimicrobial susceptibility test results.

BACKGROUND: Some antibiotic pairs display a property known as collateral sensitivity in which the evolution of resistance to one antibiotic increases sensitivity to the other. Alternating between collaterally sensitive antibiotics has been proposed as a sustainable solution to the problem of antibiotic resistance. We aimed to identify antibiotic pairs that could be considered for treatment strategies based on alternating antibiotics. METHODS: We did a retrospective analysis of 448 563 antimicrobial susceptibility test results acquired over a 4-year period (Jan 1, 2015, to Dec 31, 2018) from 23 hospitals in the University of Pittsburgh Medical Center (Pittsburgh, PA, USA) hospital system. We used a score based on mutual information to identify pairs of antibiotics displaying disjoint resistance, wherein resistance to one antibiotic is commonly associated with susceptibility to the other and vice versa. We applied this approach to the six most frequently isolated bacterial pathogens (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Pseudomonas aeruginosa, and Proteus mirabilis) and subpopulations of each created by conditioning on resistance to individual antibiotics. To identify higher-order antibiotic interactions, we predicted rates of multidrug resistance for triplets of antibiotics using Markov random fields and compared these to the observed rates. FINDINGS: We identified 69 antibiotic pairs displaying varying degrees of disjoint resistance for subpopulations of the six bacterial species. However, disjoint resistance was rarely conserved at the species level, with only 6 (0·7%) of 875 antibiotic pairs showing evidence of disjoint resistance. Instead, more than half of antibiotic pairs (465 [53·1%] of 875) exhibited signatures of concurrent resistance, whereby resistance to one antibiotic is associated with resistance to another. We found concurrent resistance to extend to more than two antibiotics, with observed rates of resistance to three antibiotics being higher than predicted from pairwise information alone. INTERPRETATION: The high frequency of concurrent resistance shows that bacteria have means of counteracting multiple antibiotics at a time. The almost complete absence of disjoint resistance at the species level implies that treatment strategies based on alternating between antibiotics might require subspecies level pathogen identification and be limited to a few antibiotic pairings. FUNDING: US National Institutes of Health.

Accurate annotation of protein coding sequences with IDTAXA.

The observed diversity of protein coding sequences continues to increase far more rapidly than knowledge of their functions, making classification algorithms essential for assigning a function to proteins using only their sequence. Most pipelines for annotating proteins rely on searches for homologous sequences in databases of previously annotated proteins using BLAST or HMMER. Here, we develop a new approach for classifying proteins into a taxonomy of functions and demonstrate its utility for genome annotation. Our algorithm, IDTAXA, was more accurate than BLAST or HMMER at assigning sequences to KEGG ortholog groups. Moreover, IDTAXA correctly avoided classifying sequences with novel functions to existing groups, which is a common error mode for classification approaches that rely on E-values as a proxy for confidence. We demonstrate IDTAXA's utility for annotating eukaryotic and prokaryotic genomes by assigning functions to proteins within a multi-level ontology and applied IDTAXA to detect genome contamination in eukaryotic genomes. Finally, we re-annotated 8604 microbial genomes with known antibiotic resistance phenotypes to discover two novel associations between proteins and antibiotic resistance. IDTAXA is available as a web tool (http://DECIPHER.codes/Classification.html) or as part of the open source DECIPHER R package from Bioconductor.

SubTap, a Versatile 3D Printed Platform for Eavesdropping on Extracellular Interactions.

Communication within the microbiome occurs through an immense diversity of small molecules. Capturing these microbial interactions is a significant challenge due to the complexity of the exometabolome and its sensitivity to environmental stimuli. Traditional methods for acquiring exometabolomic data from interacting microorganisms are limited by their low throughput or lack of sampling depth. To address this challenge, we introduce subtapping (short for substrate tapping), a technique for tapping into extracellular metabolites that are being transferred through the growth substrate during coculture. High-throughput subtapping is made possible by a new coculturing platform, named SubTap, that we engineered to resemble a 96-well plate. The three-dimensional (3D) printed SubTap platform captures the exometabolome in an agar compartment that connects physically separated growth chambers, which permits cell growth without competition for space. We show how SubTap facilitates replicable and quick detection of exometabolites via direct infusion mass spectrometry analysis. Using bacterial isolates from the soil, we apply SubTap to characterize the effects of growth medium, growth duration, and mixed versus unmixed coculturing on the exometabolome. Finally, we demonstrate SubTap's versatility by interrogating microbial interactions in multicultures with up to four strains. IMPORTANCE Improvements in experimental techniques and instrumentation have led to the discovery that the microbiome plays an essential role in human and environmental health. Nevertheless, there remain major impediments to conducting large-scale interrogations of the microbiome in a high-throughput manner, particularly in the field of exometabolomics. Existing methods to coculture microorganisms and interrogate their interactions are labor-intensive and low throughput. This inspired us to develop a solution for coculturing that was (i) open source, (ii) inexpensive, (iii) scalable, (iv) customizable, and (v) compatible with existing mass spectrometry instrumentation. Here, we present SubTap-a 3D printed coculturing platform that permits tapping directly into the growth substrate between physically separated, but interconnected, growth compartments. SubTap allows multiculture (with up to four distinct growth compartments) in spatially mixed or unmixed configurations and enables repeatable results with mass spectrometry, as shown by our validation with known compounds and cultures of one to four organisms.

Parallel evolution between genomic segments of seasonal human influenza viruses reveals RNA-RNA relationships.

The influenza A virus (IAV) genome consists of eight negative-sense viral RNA (vRNA) segments that are selectively assembled into progeny virus particles through RNA-RNA interactions. To explore putative intersegmental RNA-RNA relationships, we quantified similarity between phylogenetic trees comprising each vRNA segment from seasonal human IAV. Intersegmental tree similarity differed between subtype and lineage. While intersegmental relationships were largely conserved over time in H3N2 viruses, they diverged in H1N1 strains isolated before and after the 2009 pandemic. Surprisingly, intersegmental relationships were not driven solely by protein sequence, suggesting that IAV evolution could also be driven by RNA-RNA interactions. Finally, we used confocal microscopy to determine that colocalization of highly coevolved vRNA segments is enriched over other assembly intermediates at the nuclear periphery during productive viral infection. This study illustrates how putative RNA interactions underlying selective assembly of IAV can be interrogated with phylogenetics.

The viruses responsible for influenza evolve rapidly during infection. Changes typically emerge in two key ways: through random mutations in the genetic sequence of the virus, or by reassortment. Reassortment can occur when two or more strains infect the same cell. Once in a cell, viral particles 'open up' to release their genetic material so it can make copies of itself using the cell's machinery. The new copies of the genetic material of the virus are used to make new viral particles, which then envelop the genetic material and are released from the cell to infect other cells. If several strains of a virus infect the same cell, a new viral particle may pick up genetic segments from each of the infecting strains, creating a new strain via reassortment. Several factors are known to affect the success of the reassortment process. For example, if the new strain acquires a genetic defect that hinders its replication cycle, it is likely to die out quickly. Other times, this trading of genetic information can create a strain that is more resistant to the human immune system, allowing it to sweep across the globe and cause a deadly pandemic. However, a key part of the reassortment process that still remains unclear is how genome segments from two different influenza strains recognize each other before merging together to create hybrid daughter viruses. To explore this further, Jones et al. used a technique called fluorescence microscopy. They found that genome segments that evolved along similar paths were more likely to cluster in the same area inside infected cells, and therefore, more likely to be reassorted together into a new strain during assembly of daughter viruses. This suggests that assembly may guide the evolutionary path taken by individual genomic segments. Jones et al. also looked at the evolution of different genome segments collected from patients suffering from seasonal influenza, and found that these segments had a distinct evolutionary path to those in pandemic-causing strains. This research provides new insights into the role of reassortment in the evolution of influenza viruses during infection. In particular, it suggests that how the genome segments interact with one another may have a previously unknown and important role in guiding this evolution. These insights could be used to predict future reassortment events based on evolutionary relationships between influenza virus genomic segments, and may in the future be used as part of risk assessment tools to predict the emergence of new pandemic strains.

Multi-stable bacterial communities exhibit extreme sensitivity to initial conditions.

Microbial communities can have dramatically different compositions even among similar environments. This might be due to the existence of multiple alternative stable states, yet there exists little experimental evidence supporting this possibility. Here, we gathered a large collection of absolute population abundances capturing population dynamics in one- to four-strain communities of soil bacteria with a complex life cycle in a feast-or-famine environment. This dataset led to several observations: (i) some pairwise competitions resulted in bistability with a separatrix near a 1:1 initial ratio across a range of population densities; (ii) bistability propagated to multi-stability in multispecies communities; and (iii) replicate microbial communities reached different stable states when starting close to initial conditions separating basins of attraction, indicating finite-sized regions where the dynamics are unpredictable. The generalized Lotka-Volterra equations qualitatively captured most competition outcomes but were unable to quantitatively recapitulate the observed dynamics. This was partly due to complex and diverse growth dynamics in monocultures that ranged from Allee effects to nonmonotonic behaviors. Overall, our results highlight that multi-stability might be generic in multispecies communities and, combined with ecological noise, can lead to unpredictable community assembly, even in simple environments.

Discordance Among Antibiotic Prescription Guidelines Reflects a Lack of Clear Best Practices.

BACKGROUND: Antibiotics are among the most frequently administered drugs globally, yet they are often prescribed inappropriately. Guidelines for prescribing are developed by expert committees at international and national levels to form regional standards and by local experts to form hospital guidance documents. Our aim was to assess variability in antibiotic prescription guidelines for both regional standards and individual hospitals. METHODS: A search through 3 publicly accessible databases from February to June 2018 led to a corpus of English language guidance documents from 70 hospitals in 12 countries and regional standards from 7 academic societies. RESULTS: Guidelines varied markedly in content and structure, reflecting a paucity of rules governing their format. We compared recommendations for 3 common bacterial infections: community-acquired pneumonia, urinary tract infection, and cellulitis. Hospital guidance documents and regional standards frequently disagreed on preferable antibiotic classes for common infections. Where agreement was observed, guidance documents appeared to inherit recommendations from their respective regional standards. Several regional prescribing patterns were identified, including a greater reliance on penicillins over cephalosporins in the United Kingdom and fluoroquinolones in the United States. Regional prescribing patterns could not be explained by antibiotic resistance or costs. Additionally, literature that cited underlying recommendations did not support the magnitude of recommendation differences observed. CONCLUSIONS: The observed discordance among prescription recommendations highlights a lack of evidence for superior treatments, likely resulting from a preponderance of noninferiority trials comparing antibiotics. In response, we make several suggestions for developing guidelines that support best practices of antibiotic stewardship.

RNAconTest: comparing tools for noncoding RNA multiple sequence alignment based on structural consistency.

The importance of noncoding RNA sequences has become increasingly clear over the past decade. New RNA families are often detected and analyzed using comparative methods based on multiple sequence alignments. Accordingly, a number of programs have been developed for aligning and deriving secondary structures from sets of RNA sequences. Yet, the best tools for these tasks remain unclear because existing benchmarks contain too few sequences belonging to only a small number of RNA families. RNAconTest (RNA consistency test) is a new benchmarking approach relying on the observation that secondary structure is often conserved across highly divergent RNA sequences from the same family. RNAconTest scores multiple sequence alignments based on the level of consistency among known secondary structures belonging to reference sequences in their output alignment. Similarly, consensus secondary structure predictions are scored according to their agreement with one or more known structures in a family. Comparing the performance of 10 popular alignment programs using RNAconTest revealed that DAFS, DECIPHER, LocARNA, and MAFFT created the most structurally consistent alignments. The best consensus secondary structure predictions were generated by DAFS and LocARNA (via RNAalifold). Many of the methods specific to noncoding RNAs exhibited poor scalability as the number or length of input sequences increased, and several programs displayed substantial declines in score as more sequences were aligned. Overall, RNAconTest provides a means of testing and improving tools for comparative RNA analysis, as well as highlighting the best available approaches. RNAconTest is available from the DECIPHER website (http://DECIPHER.codes/Downloads.html).

AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions.

MOTIVATION: A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy. RESULTS: Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88-95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites. AVAILABILITY AND IMPLEMENTATION: AssessORF is available as an R package via the Bioconductor package repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations.

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for five strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy-tailed, with a few cells effectively 'winning the jackpot' to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early-germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy-tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations and explaining variability in infection outcomes across patients.

Exclusivity offers a sound yet practical species criterion for bacteria despite abundant gene flow.

BACKGROUND: The question of whether bacterial species objectively exist has long divided microbiologists. A major source of contention stems from the fact that bacteria regularly engage in horizontal gene transfer (HGT), making it difficult to ascertain relatedness and draw boundaries between taxa. A natural way to define taxa is based on exclusivity of relatedness, which applies when members of a taxon are more closely related to each other than they are to any outsider. It is largely unknown whether exclusive bacterial taxa exist when averaging over the genome or are rare due to rampant hybridization. RESULTS: Here, we analyze a collection of 701 genomes representing a wide variety of environmental isolates from the family Streptomycetaceae, whose members are competent at HGT. We find that the presence/absence of auxiliary genes in the pan-genome displays a hierarchical (tree-like) structure that correlates significantly with the genealogy of the core-genome. Moreover, we identified the existence of many exclusive taxa, although individual genes often contradict these taxa. These conclusions were supported by repeating the analysis on 1,586 genomes belonging to the genus Bacillus. However, despite confirming the existence of exclusive groups (taxa), we were unable to identify an objective threshold at which to assign the rank of species. CONCLUSIONS: The existence of bacterial taxa is justified by considering average relatedness across the entire genome, as captured by exclusivity, but is rejected if one requires unanimous agreement of all parts of the genome. We propose using exclusivity to delimit taxa and conventional genome similarity thresholds to assign bacterial taxa to the species rank. This approach recognizes species that are phylogenetically meaningful, while also establishing some degree of comparability across species-ranked taxa in different bacterial clades.

IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences.

BACKGROUND: Microbiome studies often involve sequencing a marker gene to identify the microorganisms in samples of interest. Sequence classification is a critical component of this process, whereby sequences are assigned to a reference taxonomy containing known sequence representatives of many microbial groups. Previous studies have shown that existing classification programs often assign sequences to reference groups even if they belong to novel taxonomic groups that are absent from the reference taxonomy. This high rate of "over classification" is particularly detrimental in microbiome studies because reference taxonomies are far from comprehensive. RESULTS: Here, we introduce IDTAXA, a novel approach to taxonomic classification that employs principles from machine learning to reduce over classification errors. Using multiple reference taxonomies, we demonstrate that IDTAXA has higher accuracy than popular classifiers such as BLAST, MAPSeq, QIIME, SINTAX, SPINGO, and the RDP Classifier. Similarly, IDTAXA yields far fewer over classifications on Illumina mock microbial community data when the expected taxa are absent from the training set. Furthermore, IDTAXA offers many practical advantages over other classifiers, such as maintaining low error rates across varying input sequence lengths and withholding classifications from input sequences composed of random nucleotides or repeats. CONCLUSIONS: IDTAXA's classifications may lead to different conclusions in microbiome studies because of the substantially reduced number of taxa that are incorrectly identified through over classification. Although misclassification error is relatively minor, we believe that many remaining misclassifications are likely caused by errors in the reference taxonomy. We describe how IDTAXA is able to identify many putative mislabeling errors in reference taxonomies, enabling training sets to be automatically corrected by eliminating spurious sequences. IDTAXA is part of the DECIPHER package for the R programming language, available through the Bioconductor repository or accessible online ( http://DECIPHER.codes ).