Biology subjects

Baumdicker, F.

Publications and source records attributed to Baumdicker, F..

18 recordsLinked to original sources

bioRxiv2026

Hidden but Expanding Sampling Bias Distorts Prokaryotic Genome Collections

Public genome collections underpin our understanding of bacterial diversity and the distribution of traits such as antibiotic resistance. Their growing size promises an increasingly complete view of microbial genetic variation. However, these collections combine studies with different sampling priorities, often overrepresenting subgroups of closely related strains of particular interest. The resulting bias reduces effective information, and its consequences for diversity estimates and the value of further sequencing remain insufficiently quantified. Here we show that sampling bias in prokaryotic genome collections is widespread, escalating in recent years, and distorts gene frequency estimates and the representation of genomic diversity. We propose to detect and reduce sampling bias based on phylogenetic relationships compared to expectations under random sampling, which, in contrast to dereplication and data reduction methods, accounts for sample size. Applied to 402 prokaryotic species in the NCBI genome database, our method PhyloThin indicates that the effective number of samples is 41% lower than the actual number. Oversampling varies substantially among species, showing that genome counts alone provide an incomplete measure of how well a species has already been characterized. Accounting for oversampling reveals greater genomic diversity and more genes awaiting discovery than current data suggest and improves gene frequency estimates with implications for pathogen surveillance and the discovery of novel genetic functions.

genomics↗

bioRxiv2025

Interpreting Convolutional Neural Networks in Population Genetics

AO_SCPLOWBSTRACTC_SCPLOWMachine learning approaches have become a powerful alternative to traditional methods in population genetics. Convolutional neural networks (CNNs) in particular have been successful in inferring natural selection, recombination rate estimation, introgression, dispersal distances, and effective population size changes. One limitation of CNNs and other deep learning methods is that they can be difficult to interpret. When they have been shown to be as or more successful than summary-statistic-based methods, what are they learning? Here we investigate CNNs from two different methods: the pg-gan discriminator for identifying real vs. simulated data, and two networks trained to detect selective sweeps. We first compute correlations between learned network features and traditional summary statistics, then assess whether summary statistics can be predicted from the learned features. To understand the learned features, we compute feature importance through SHAP values and feature groupings through dimensionality reduction. Finally we use decision trees and random forests to build an interpretable "model-of-the-model". Our results reveal that some CNN architectures can implicitly compute summary statistics such as pairwise heterozygosity, while statistics such as the site frequency spectrum are less similar to the networks learned features. We find that long-range linkage disequilibrium is readily approximated by the networks and may be more efficiently computed by CNNs than traditional methods (which are quadratic in the number of sites). Overall, this work contributes to the interpretability of deep learning methods in population genetics by clarifying the relationships between model architecture, learned network features, established summary statistics, and predicted evolutionary parameters.

genomics↗

bioRxiv2025

Neural posterior estimation for population genetics

Simulation-based inference methods are increasingly being used in population genetics due to their flexibility and ability to be applied in settings where likelihood-based methods are intractable. Perhaps the best known such method is Approximate Bayesian Computation (ABC); however, its popularity is offset by its shortcomings which include computational expense and an unfortunate inability to efficiently fit models to high-dimensional summaries of the data. An alternative approach that solves these issues is supervised machine learning (ML); however, ML methods generally do not yield Bayesian uncertainty estimates of the quantities they predict. Here, we apply a recently introduced method, neural posterior estimation (NPE), that combines the best facets of ABC and supervised ML by training a neural network to estimate the posterior distribution of a population genetics model. We first compare neural posterior estimation with other inference methods for a variety of population genetic tasks, and show that neural posterior estimators yield posterior distributions with high accuracy and efficiency. We compare learned posterior distributions given raw genotypes and various summary statistics as input data. Additionally, we apply neural posterior estimation for demographic inference for simple and more complex models to highlight its application, including an analysis of demographic history in Drosophila melanogaster. Finally, we provide a user friendly workflow that enables others to perform neural posterior estimation on their own genetic data.

evolutionary biology↗

bioRxiv2025

An evolutionary approach to predict the orientation of CRISPR arrays

CRISPR-Cas is a defense system of bacteria and archaea against phages. Parts of the foreign DNA, called spacers, are incorporated into the CRISPR array which constitutes the immune memory. The orientation of CRISPR arrays is crucial for analyzing and understanding the functionality of CRISPR systems and their targets. Several methods have been developed to identify the orientation of a CRISPR array. To predict the orientation, different methods use different features such as the repeat sequences between the spacers, the location of the leader sequence, the Cas genes, or PAMs. However, those features are often not sufficient to predict the orientation with certainty, or different methods disagree. Remarkably, almost all CRISPR systems have been found to insert spacers in a polarized manner at the leader end of the array. We introduce CRISPR-evOr, a method that leverages the resulting patterns to predict the acquisition orientation for (a group of) CRISPR arrays by reconstructing and comparing the likelihood of their evolutionary history with respect to both possible acquisition orientations. The new method is independent of Cas type, leader existence and location, and transcription orientation. CRISPR-evOr is thus particularly useful for arrays that other CRISPR orientation tools cannot predict confidently and to verify or resolve conflicting predictions from existing tools. CRISPR-evOr currently confidently predicts the orientation of 28.3% of the arrays in the considered subset of CRISPRCasdb, which other tools like CRISPRDirection and CRISPRstrand cannot reliably orient. As our tool leverages evolutionary information we expect this percentage to grow in the future when more closely related arrays will be available. Additionally, CRISPR-evOr provides confident decisions for rare subtypes of CRISPR arrays, where knowledge about repeats and leaders and their orientation is limited. Author SummarySome bacteria and archaea possess a CRISPR-Cas defense system, which protects them against phages and mobile genetic elements. This system adapts to new threats by incorporating small fragments of their DNA, so called spacers, in a CRISPR array. Remarkably, the acquisition of new spacers is polarized at one end of this array. To understand how this immune system functions, it is essential to know the orientation of these arrays. Many existing tools try to determine orientation using genetic markers, but these methods are often unreliable or disagree with one another. In our work, we developed a new method that predicts the end at which new spacers are inserted, by considering the evolutionary history of a group of related CRISPR arrays. Unlike other tools, our approach is less reliant on specific genetic markers and can be applied broadly across many types of CRISPR systems. We show that it can confidently determine the orientation of a large number of arrays that other methods cannot resolve. This provides a new way to predict the array orientation, which is particularly useful for rare CRISPR types. Our evolutionary approach will become even more powerful as more genetic data becomes available.

bioinformatics↗

bioRxiv2025

The complexity of multiple CRISPR arrays in strains with (co-occurring) CRISPR systems

CRISPR and their associated Cas proteins provide adaptive immunity in prokaryotes, protecting against invading genetic elements. These systems are categorized into types and are highly diverse. Genomes often harbor multiple CRISPR arrays varying in length and distance from Cas loci. However, the ecological roles of multiple CRISPR arrays and their interactions with multiple Cas loci remain poorly understood. We present a comprehensive analysis of CRISPR systems that uncovers variation between diverse Cas types regarding the occurrence of multiple arrays, the distribution of their lengths and positions relative to Cas loci, and the diversity of their repeat sequences. Some types tend to occur as the sole Cas locus present in the genome, but typically have two or more associated arrays, especially for types I-E and I-F. Multiple Cas types are also common, with some systems showing a preference for specific co-occurrence. Distinct array distributions and orientations around Cas loci indicate substantial differences in functionality and transcriptional behavior among Cas types. Our analysis suggests that arrays with identical repeats in the same genome acquire new spacers at comparable rates, irrespective of their proximity to the Cas locus. Furthermore, repeat similarities indicate that arrays of systems that often co-occur with other systems tend to have more diverse repeats than those mostly appearing alongside solitary systems. Our results indicate that co-occurring Cas type pairs might not only collaborate in spacer acquisition but also maintain independent and complementary functions and that CRISPR systems distribute their defensive spacer repertoire equally across multiple CRISPR arrays.

microbiology↗

bioRxiv2025

Comprehensive Analysis of CRISPR Array Repeat Mutations Reveals Subtype-Specific Patterns and Links to Spacer Dynamics

CRISPR-Cas systems are adaptive immune mechanisms in bacteria and archaea that protect against invading genetic elements by integrating short fragments of foreign DNA into CRISPR arrays. These arrays consist of repetitive sequences interspersed with unique spacers, guiding Cas proteins to recognize and degrade matching nucleic acids. The integrity of these repeat sequences is crucial for the proper function of CRISPR-Cas systems, yet their mutational dynamics remain poorly understood. In this study, we analyzed 56,343 CRISPR arrays across 25,628 diverse prokaryotic genomes to assess the mutation patterns in CRISPR array repeat sequences within and across different CRISPR subtypes. Our findings reveal, as expected to some extent, that mutation frequency is substantially higher in terminal repeat sequences compared to internal repeats consistently across system types. However, the mutation patterns exhibit an unexpected amount of variation among different CRISPR subtypes, suggesting that selective pressures and functional constraints shape repeat sequence evolution in distinct ways. Understanding these mutation dynamics provides insights into the stability and adaptability of CRISPR arrays across diverse bacterial and archaeal lineages. Additionally, we elucidate a novel relationship between repeat mutations and spacer dynamics, demonstrating that hotspots for terminal repeat mutations coincide with regions exhibiting spacer conservation. This observation corroborates recent findings by Fehrenbach et al. (2024) indicating that spacer deletions occur at a frequency 374 times greater than that of mutations and are significantly influenced by repeat misalignment. Our findings suggest that repeat mutations play a pivotal role in spacer retention or loss, or vice versa, thereby highlighting an evolutionary trade-off between the stability and adaptability of CRISPR arrays.

bioinformatics↗

bioRxiv2024

Revealing the range of maximum likelihood estimates in the admixture model.

Many ancestry inference tools, including STRUCTURE and ADMIXTURE, rely on the admixture model to infer both, allele frequencies p and individual admixture proportions q for a collection of individuals relative to a set of hypothetical ancestral populations. We show that under realistic conditions the likelihood in the admixture model is typically flat in some direction around a maximum likelihood estimate [Formula]. In particular, the maximum likelihood estimator is non-unique and there is a complete spectrum of possible estimates. Common inference tools typically identify only a few points within this spectrum. We provide an algorithm which computes the set of equally likely [Formula], when starting from [Formula]. It is analytic for K = 2 ancestral populations and numeric for K > 2. We apply our algorithm to data from the 1000 genomes project, and show that inter-European estimators of q can come with a large set of equally likely possibilities. In general, markers with large allele frequency differences between populations in combination with individuals with concentrated admixture proportions lead to small areas with a flat likelihood. Our findings imply that care must be taken when interpreting results from STRUCTURE and ADMIXTURE if populations are not separated well enough.

genetics↗

bioRxiv2024

Effectiveness of CRISPR-Cas in Sensitizing Bacterial Populations with Plasmid-Encoded Antimicrobial Resistance

The spread of bacteria resistant to antibiotics poses a serious threat to human health. Genes that encode antibiotic resistance are often harbored on plasmids, extra-chromosomal DNA molecules found in bacteria. The emergence of multiresistance plasmids is particularly problematic and demands the development of new antibiotics and alternative strategies. CRISPR-Cas derived tools with their sequence specificity offer a promising new approach to combating antibiotic resistance. By introducing CRISPR-Cas encoding plasmids that target antibiotic resistance genes on plasmids, the susceptibility of bacteria to conventional antibiotics can be restored. However, genetic variation within bacterial populations can hinder the effectiveness of such CRISPR-Cas tools by allowing some mutant plasmids to evade CRISPR-mediated cleaving or gene silencing. In this study, we develop a model to test the effectiveness of CRISPR-Cas in sensitizing bacterial populations carrying resistance on non-transmissible plasmids and assess the success probability of a subsequent treatment with conventional antibiotics. abstract hgt We evaluate this probability according to the target interference mechanism, the copy number of the resistance-encoding plasmid, and its compatibility with the CRISPR-Cas encoding plasmid. Our results identify promising approaches to revert antibiotic resistance with CRISPR-Cas encoding plasmids: A DNA-cleaving CRISPR-Cas system on a plasmid incompatible with the targeted plasmid is most effective for low copy numbers, while for resistance plasmids with higher copy numbers gene silencing by CRISPR-Cas systems encoded on compatible plasmids is the superior solution.

evolutionary biology↗

bioRxiv2024

Goldfinder: Unraveling Networks of Gene Co-occurrence and Avoidance in Bacterial Pangenomes

The pangenome is the set of all genes present in a prokaryotic species. Most pangenomes contain many accessory genes that are present in only some of the species members. Genes need to function together, and it has been suggested that selection for certain gene combinations affects the structure of prokaryotic pangenomes. Nevertheless, genes might also co-occur simply due to being linked on the genome, and efficient tools are needed to distinguish linkage from co-selection. Here we present Goldfinder, an approach to infer co-occurrence and co-avoidance between gene pairs by taking the phylogenetic relationships of the species into account. The approach is implemented in an efficient Python script available at https://github.com/fbaumdicker/goldfinder. We also provide scripts for clustering co-occurring genes and for visualizing the resulting co-occurrence and co-avoidance networks in Cytoscape. In comparison to the co-occurrence inference tool Coinfinder, Goldfinder finds fewer co-occurring pairs in a real species pangenome, suggesting that fewer spurious associations due to phylogenetic dependencies are detected. We conclude that Goldfinder is a fast and accurate tool to infer gene co-occurrence and co-avoidance, which will enable large-scale analyses to infer co-selected genes across bacterial pangenomes.

bioinformatics↗

bioRxiv2024

Inferring Ancestry with the Hierarchical Soft Clustering Approach tangleGen

Understanding the genetic ancestry of populations is central to numerous scientific and societal fields. It contributes to a better understanding of human evolutionary history, advances personalized medicine, aids in forensic identification, and allows individuals to connect to their genealogical roots. Existing methods, such as ADMIXTURE, have significantly improved our ability to infer ancestries. However, these methods typically work with a fixed number of independent ancestral populations. As a result, they provide insight into genetic admixture, but do not include a hierarchical interpretation. In particular, the intricate ancestral population structures remain difficult to unravel. Alternative methods with a consistent inheritance structure, such as hierarchical clustering, may offer benefits in terms of interpreting the inferred ancestries. Here, we present tangleGen, a soft clustering tool that transfers the hierarchical machine learning framework Tangles, which leverages graph theoretical concepts, to the field of population genetics. The hierarchical perspective of tangleGen on the composition and structure of populations improves the interpretability of the inferred ancestral relationships. Moreover, tangleGen adds a new layer of explainability, as it allows identifying the SNPs that are responsible for the clustering structure. We demonstrate the capabilities and benefits of tangleGen for the inference of ancestral relationships, using both simulated data and data from the 1000 Genomes Project.

bioinformatics↗

bioRxiv2024

SpacerPlacer: Ancestral reconstruction of CRISPR arrays reveals the evolutionary dynamics of spacer deletions

Bacteria employ CRISPR-Cas systems for defense by integrating invader-derived sequences, termed spacers, into the CRISPR array, which constitutes an immunity memory. While spacer deletions occur randomly across the array, newly acquired spacers are predominantly integrated at the leader end. Consequently, spacer arrays can be used to derive the chronology of spacer insertions. Reconstruction of ancestral spacer acquisitions and deletions could help unravel the coevolution of phages and bacteria, the evolutionary dynamics in microbiomes, or track pathogens. However, standard reconstruction methods produce misleading results by overlooking insertion order and joint deletions of spacers. Here, we present SpacerPlacer, a maximum likelihood-based ancestral reconstruction approach for CRISPR array evolution. We used SpacerPlacer to reconstruct and investigate ancestral deletion events of 4565 CRISPR arrays, revealing that spacer deletions occur 374 times more frequently than mutations and are regularly deleted jointly, with an average of 2.7 spacers. Surprisingly, we observed a decrease in the spacer deletion frequency towards both ends of the reconstructed arrays. While the resulting trailer-end conservation is commonly observed, a reduced deletion frequency is now also detectable towards the variable leader end. Finally, our results point to the hypothesis that frequent loss of recently acquired spacers may provide a selective advantage.

bioinformatics↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2021

Neural Networks for self-adjusting Mutation Rate Estimation when the Recombination Rate is unknown

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics. Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean square error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown.

evolutionary biology↗

bioRxiv2021

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprimes many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

genetics↗

bioRxiv2020

Horizontal gene transfer-mediated bacterial strain variation affects host fitness

How microbes affect host fitness and environmental adaptation has become a fundamental research question in evolutionary biology. We tested for associations of bacterial genomic variation and Drosophila melanogaster offspring number in a microbial Genome Wide Association Study (GWAS). Leveraging strain variation in the genus Gluconobacter, a genus of bacteria that are commonly associated with Drosophila under natural conditions, we pinpoint the thiamine biosynthesis pathway (TBP) as contributing to differences in fitness conferred to the fly host. By tracing the evolutionary history of TBP genes in Gluconobacter, we find that TBP genes were most likely lost and reacquired by horizontal gene transfer (HGT). We suggest that HGT might contribute to microbiome flexibility and speculate that it can also more generally contribute to host adaptation.

evolutionary biology↗

bioRxiv2020

Inference of recent admixture using genotype data

The inference of biogeographic ancestry (BGA) has become a focus of forensic genetics. Misinference of BGA can have profound unwanted consequences for investigations and society. We show that recent admixture can lead to misclassification and erroneous inference of ancestry proportions, using state of the art analysis tools with (i) simulations, (ii) 1000 genomes project data, and (iii) two individuals analyzed using the ForenSeq DNA Signature Prep Kit. Subsequently, we extend existing tools for estimation of individual ancestry (IA) by allowing for different IA in both parents, leading to estimates of parental individual ancestry (PIA), and a statistical test for recent admixture. Estimation of PIA outperforms IA in most scenarios of recent admixture. Furthermore, additional information about parental ancestry can be acquired with PIA that may guide casework. HighlightsO_LIWe improve statistical methods as used in STRUCTURE and ADMIXTURE for Biogeographical Ancestry (BGA) inference to account for recent admixture, i.e. different admixture of both parents. C_LIO_LIThe resulting recent admixture model has a higher accuracy in estimating individual admixture in most cases. C_LIO_LIWe give a likelihood ratio test for recent admixture, which is both highly specific and sensitive for recent admixture. C_LIO_LIWe find evidence of recent admixture in the 1000 genomes dataset. C_LIO_LIThe self-report on recent admixture of two self-sequenced samples was only confirmed in one case. C_LI

genetics↗

bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗

bioRxiv2019

How to choose sets of ancestry informative markers: A supervised feature selection approach

Inference of the Biogeographical Ancestry (BGA) of a person or trace relies on three ingredients: (1) A reference database of DNA samples including BGA information; (2) a statistical clustering method; (3) a set of loci which segregate dependent on geographical location, i.e. a set of so-called Ancestry Informative Markers (AIMs). We used the theory of feature selection from statistical learning in order to obtain AIM-sets for BGA inference. Using simulations, we show that this learning procedure works in various cases, and outperforms ad hoc methods, based on statistics like FST or informativeness for the choice of AIMs. Applying our method to data from the 1000 genomes project (excluding Admixed Americans) we identified an AIMset of 17 SNPs, which partly overlaps with existing ones. For continental BGA, the AIMset outperforms existing AIMsets on the 1000 genomes dataset, and gives a vanishing misclassification error.

bioinformatics↗