Bien, J. | bioRxiv Science

bioRxiv2026

Estimating Gene Prevalence with Incomplete Genomes: Application to the Prochlorococcus Pangenome

Gene prevalence is an essential statistic in population genomics, defined as the proportion of a population carrying at least one ortholog of a given gene family. Common approaches to estimating gene prevalence either assume complete genomes or exclude genomes below a completeness threshold, resulting in biased and high variance estimates that fail to take advantage of all available data. Here we developed a maximum-likelihood framework that explicitly models genome completeness in order to estimate gene prevalence and associated confidence intervals from collections of complete and incomplete genomes. We applied the methodology to marine Prochlorococcus, a globally abundant photosynthetic bacterium for which the majority of available genomes are incomplete. Our methodology produced gene prevalence estimates with associated confidence intervals for a total of 52,500 gene families, more than 4 times the number quantifiable using complete genomes alone. We find a core of 714 genes are likely universal to the Prochlorococcus genus and a substantial majority of 49,828 gene families occur at low prevalence. Our results reveal a Prochlorococcus pangenome dominated by rare flexible genes and provide a general framework for incorporating incomplete genomes into quantitative analyses of microbial genomic diversity.

microbiology↗

bioRxiv2024

Predictive modeling of microbial data with interaction effects

Microbial interactions are of fundamental importance for the functioning and the maintenance of microbial communities. Deciphering these interactions from (time-series) observational data or controlled lab experiments remains a formidable challenge due to their context-dependent nature, such as, e.g., (a)biotic factors, host characteristics, and overall community composition. Complementary to the classical ecological view, recent research advocates an empirical "community-function landscape" framework where an outcome of interest, e.g., a community function, is learned via statistical regression models that include pairwise or higher-order statistical species interaction effects. Here, we adopt the latter viewpoint and present penalized quadratic interaction models that can accommodate all common microbial data types, including microbial presence-absence data, relative (or compositional) abundance data from microbiome surveys, and quantitative (absolute abundance) microbiome data. We propose novel interaction models for compositional data and bring modern statistical techniques such as hierarchical interaction constraints and stability-based model selection to the microbial realm. To illustrate our frameworks versatility, we consider prediction tasks across a wide range of microbial datasets and ecosystems, including butyrate production in model communities in designed experiments and environmental covariate prediction from marine microbiome data. We show improved predictive performance of these interaction models and assess their limits in the presence of extreme data sparsity. On a large-scale gut microbiome cohort data, we identify interaction models that can accurately predict the abundance of antimicrobial resistance genes, enabling novel biological hypotheses about microbial community composition and antimicrobial resistance. Author SummaryMicrobes live in complex communities where interactions between species shape their function and stability. Understanding these interactions is crucial for predicting how microbial communities respond to environmental changes, medical treatments, or shifts in their host organisms. However, identifying these relationships is challenging because they depend on many factors, including the surrounding environment and community composition. In this study, we introduce a new statistical modeling approach to uncover microbial interactions from different types of data, including presence-absence patterns, relative abundance from microbiome surveys, and absolute abundance measurements. Our method builds on modern statistical techniques to improve accuracy and reliability, even when data are sparse or noisy. We demonstrate the power of our approach by applying it to diverse microbial datasets, from marine ecosystems to gut microbiomes. In one case, we successfully predicted antimicrobial resistance gene abundance based on microbial interactions, opening new avenues for understanding how resistance spreads in microbial communities. By advancing statistical tools for microbiome research, our work provides a new way to explore the hidden relationships between microbes, with potential applications in medicine, environmental science, and biotechnology.

genomics↗

bioRxiv2020

Tree-Aggregated Predictive Modeling of Microbiome Data

Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.

bioinformatics↗