Biology subjects

Honkela, A.

Publications and source records attributed to Honkela, A..

6 recordsLinked to original sources

bioRxiv2026

Bayesian modeling of longitudinal metatranscriptomes of broiler meat spoilage microbiomes shows shared predictive signature associated with spoilage at refrigerated temperatures

Microbial spoilage of packaged meat is driven by complex microbial succession and related metabolic activity, yet conventional shelf-life assessment is mainly based on shelf-life studies relying on culturing and sensory analysis. In routine quality assurance, results are obtained retrospectively, and they are only indirectly linked to the metabolic activity related to sensory deterioration. Functional, time informative approaches that capture the active metabolic state of the spoilage microbiome and predict the rate of spoilage are lacking. We developed a censoring-aware Gaussian process (CAGP) framework to model longitudinal pathway expression profiles from broiler meat metatranscriptomes collected over consecutive storage days at 4 or 6{degrees}C. Samples were annotated using odor-based sensory scores defining fresh, early-spoilage, and late-spoilage phases. Because observed zeros in pathway-level data may reflect non-detection rather than true absence, the model treats low values as left-censored observations below a soft detection threshold while estimating smooth temporal trajectories with uncertainty. In leave-one-out prediction within the 4{degrees}C time-series, predicted sampling days differed from the true days by an average of 0.43 days, and predicted spoilage phases agreed with the sensory classification. Trajectories learned at 4{degrees}C also transferred to an independent 6{degrees}C time-series at the spoilage-phase level, suggesting that shared functional spoilage programs are preserved despite temperature-dependent changes in spoilage rate. Cross-entropy ranking further identified pathway modules carrying time- and phase-informative signals across temperatures. Overall, this framework provides a probabilistic approach for linking metatranscriptomic functional dynamics to sensory spoilage progression, supporting shelf-life assessment beyond retrospective microbial enumeration. IMPORTANCEShelf-life evaluation of meat products still relies heavily on microbial counts, targeted detection of spoilage organisms, and sensory panels. However, microbial abundance and species-level composition do not always predict when a product becomes unacceptable, because spoilage depends on the active metabolic state of the microbiome and can vary between strains, production lots, and storage conditions. This study shows that longitudinal metatranscriptomics, combined with censoring-aware Bayesian time-series modeling, can recover functional pathway trajectories aligned with sensory spoilage progression. By identifying pathway-level signatures that transfer across refrigeration temperatures, the approach moves shelf-life assessment from retrospective enumeration toward predictive, function-based monitoring. In this study, a spoilage signature refers to a set of microbial pathway trajectories whose expression patterns are informative of storage time and sensory spoilage phase. These signatures could support future tools for earlier spoilage detection, better shelf-life estimation, and improved control of product quality in meat production.

bioinformatics↗

bioRxiv2026

Towards Useful and Private Synthetic Omics: Community Benchmarking of Generative Models for Transcriptomics Data

BackgroundThe synthesis of anonymized data derived from real-world cohorts offers a promising strategy for regulatory-compliant and privacy-preserving biological data sharing, potentially facilitating model development that can improve predictive performance. However, the extent to which generative models can preserve biological signals while remaining resilient to adversarial privacy attacks in high-dimensional omics contexts remains underexplored. To address this gap, the CAMDA 2025 Health Privacy Challenge launched a community-driven effort to systematically benchmark synthetic and privacy-preserving data generation for bulk RNA-seq cohorts. ResultsBuilding on this initiative, we systematically benchmarked 11 generative methods across two cancer cohorts ([~]1,000 and [~]5,000 patients) over 978 landmark genes. Methods were evaluated across complementary axes of distributional fidelity, downstream utility, biological plausibility and empirical privacy risk, with emphasis on trade-offs between vulnerability to membership inference attacks (MIA) and other evaluation dimensions. Expressive deep generative models achieved strong predictive utility and differential expression recovery, but were often more vulnerable to membership inference risk. Differentially private methods improved resistance to attacks at the cost of reduced utility, while simpler statistical approaches offered competitive utility with moderate privacy risk and fast training. ConclusionsSynthetic bulk RNA-seq quality is inherently multi-dimensional and shaped by trade-offs between utility, biological preservation and privacy. Our results indicate that differences in model architecture drive distinct trade-offs across these axes, suggesting that model choice should align with dataset characteristics, intended downstream use and privacy requirements. Privacy risk should also be assessed using multiple complementary attack methods and, where possible, formal differential privacy protection.

bioinformatics↗

bioRxiv2024

Bayesian model-based method for clustering gene expression time series with multiple replicates

In this study, we introduce a Bayesian model-based method for clustering transcriptomics time series data with multiple replicates. This technique is based on sampling Gaussian processes (GPs) within an infinite mixture model from a Dirichlet process (DP). Our method uses multiple GP models to accommodate for multiple differently behaving experimental replicates within each cluster. We call it multiple models Dirichlet process Gaussian process (MMDPGP). We compare our method with state-of-the-art model-based clustering approaches for handling gene expression time series with multiple replicates. We present a case study where all methods are applied for clustering RNA-Seq time series of Clostridium botulinum with three different experimental replicates. The results obtained from the gene enrichment analysis showed that the number of significantly enriched sets of genes is larger in the clusters produced by MMDPGP. To demonstrate the accuracy of our method we use it to cluster synthetically generated data sets. The clusters produced by our method on the synthetic data had a significantly higher purity score compared to the state-of-the-art approaches. By modelling each replicate with a separate GP, our method can use the natural variability between experimental replicates to learn more about the underlying biology. Author summaryIn our manuscript we introduce a method called multiple models Dirichlet process Gaussian process (MMDPGP), a novel Bayesian approach for clustering gene expression time series data. Our method stands out by accounting for the variability among multiple experimental replicates within each cluster, a feature that is often overlooked in existing model-based clustering approaches. This allows us to capture the natural variability between replicates as opposed to the crude method of simply averaging the replicates which discards interesting information in the data. By integrating multiple Gaussian process models within an infinite mixture model derived from a Dirichlet process, MMDPGP offers a more nuanced and accurate representation of the biological data. We benchmarked MMDPGP against state-of-the-art methods, by applying them for the purpose of clustering recently collected RNA-Seq time series of the bacterium Clostridium botulinum and performing a gene enrichment analysis on the generated clusters. Additionally, we test the accuracy of our method in comparison with other methods using synthetic data sets. The superior performance of our method in terms of finding significantly enriched gene sets and the clustering accuracy on synthetic data underscore its robustness and potential for broad applicability in computational biology. Our study addresses a critical gap in the analysis of transcriptomics time series data by explicitly modeling the natural variability across experimental replicates. This advancement not only enhances the accuracy of clustering results but also provides deeper insights into the underlying biological processes. By leveraging Bayesian methods and Gaussian processes, our approach offers a powerful tool that can be adapted and extended for various types of omics data, inspiring further methodological developments in the field. Competing interestsWe declare no competing interests related to this work. Code availability and implementationThe Python code for implementing our method is publicly available in Zenodo through the following DOI link: https://doi.org/10.5281/zenodo.11202145. DataThe RNA-Seq data used to validate our method in the paper are deposited in GEO at the following link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE248529.

bioinformatics↗

bioRxiv2022

Strong pathogen competition in neonatal gut colonisation

Bacterial pathogen species and their strains that colonise the human gut are generally understood to compete against both each other and the commensal species colonising this ecosystem. However, currently we are lacking a population-wide quantification of strain-level colonisation dynamics for many common bacterial pathogens and the relationship of colonisation potential to prevalence in disease is unknown. In addition, it is unclear how ecological factors might be modulating the dynamics. Here, using a combination of latest high-resolution metagenomics and strain-level genomic epidemiology methods leveraging large genomic reference libraries of key pathogens, we performed a characterisation of the competition (co-exclusion and co-presence) and colonisation dynamics for a longitudinal cohort of neonatal gut microbiomes. We found a strong inter- and intra-species competition dynamic in the gut colonisation process, but also a number of synergistic relationships among several species belonging to genus Klebsiella, which includes the prominent human pathogen Klebsiella pneumoniae. Additionally, we find no evidence of preferential colonisation by hospital-adapted pathogen lineages in either vaginal or caesarean section birth groups. Our analysis also enables the first unbiased assessment of the strain-level colonisation potential of extra-intestinal pathogenic Escherichia coli (ExPEC) in comparison with their potential to cause bloodstream infections. We determined that the established common ExPEC clones ST73 and ST95 are overall significantly more pathogenic than the more recent, globally circulating multi-drug resistant clone ST131, where only a single subclone (ST131-C2) exhibited excess pathogenic potential. Our study highlights the importance of systematic surveillance of bacterial gut pathogens, not only from disease but also from carriage state, to better inform therapies and preventive medicine in the future.

microbiology↗

bioRxiv2020

Genomic Epidemiology with Mixed Samples

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, skipping the colony pick step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

bioinformatics↗

bioRxiv2018

High-resolution sweep metagenomics using ultrafast read mapping and inference

Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP method for identifying and estimating the relative abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our method facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

bioinformatics↗