Biology subjects

Huson, D. H.

Publications and source records attributed to Huson, D. H..

14 recordsLinked to original sources

bioRxiv2026

Phylogenetic parallelograms: visual comparison of discordant phylogenetic trees

Phylogenetic trees inferred from different genomic regions, or from organellar and nuclear genomes, frequently disagree owing to incomplete lineage sorting, hybridization, introgression or horizontal gene transfer. Such discordance is informative, yet the standard tool for comparing trees visually, the tanglegram, is restricted to two trees and is known to misrepresent their similarity. Here we introduce phylogenetic parallelograms, drawings in which any number of rooted trees are embedded into a common scaffold, a rooted phylogenetic network that displays all of them, and are drawn in parallel so that shared branches align while conflicting branches visibly diverge. On synthetic tree pairs, the complexity of a parallelogram tracks the topological distance between the trees, whereas the complexity of an optimized tanglegram does not. We apply the approach to genome-wide introgression in the Anopheles gambiae species complex and to organellar discordance in cats, grasses and Fagaceae. PhyloParallelograms is an open-source interactive application.

bioinformatics↗

bioRxiv2026

A host-directed virulence factor of Clostridium perfringens is modulated by gut commensal strains

In a healthy host, the residential microbes help regulate the growth of pathobionts, which are common members of the human gut microbiome, preventing them from causing diseases, including infections, under certain conditions. In cases of dysbiosis, this protection may be compromised. Targeted microbiome modulation offers a promising approach to restore healthy conditions in a disrupted community and consequently prevent infections using the natural colonization resistance of the microbiome. Elucidating the interaction mechanisms between microbial species within a microbiome is crucial for understanding how a microbiome can be modulated precisely and effectively to benefit the hosts well-being. Here, we investigated the interactions between the pathobiont C. perfringens and human gut commensals on physiological and molecular levels. We found that commensal strains affect C. perfringens growth by competing for substrates such as amino acids or a carbon source other than glucose. We further observed that Bacteroidaceae strains altered the levels of C. perfringens proteins, among others, the host-directed {theta}-toxin. Our findings reinforce the notion that modulating the composition of the gut microbiome is an effective strategy to prevent infections.

microbiology↗

bioRxiv2025

MMonitor: Software for Real-Time Monitoring of Microbial Communities Using Long Reads

Real-time monitoring of microbial communities offers valuable insights into microbial dynamics across diverse environments. However, many existing metagenome analysis tools require advanced computational expertise and are not designed for monitoring. We present MMonitor, an open source software platform for the real-time analysis and visualization of metagenomic Oxford Nanopore Technologies (ONT) sequencing data. MMonitor includes two components: a desktop application for running bioinformatics pipelines through a graphical user interface (GUI) or command-line interface (CLI), and a web-based dashboard for interactive result inspection. The dashboard provides taxonomic composition over time, quality scores, diversity indices, and taxonomy-metadata correlations. Integrated pipelines enable automated de-novo assembly and reconstruction of metagenome-assembled genomes (MAGs). To validate MMonitor, we tracked human gut microbial populations in three bioreactors using 16S rRNA gene sequencing, and applied it to whole-genome sequencing (WGS) data to generate high-quality annotated MAGs and reveal functional insights. We also compare MMonitor to other software for real-time metagenomic analysis, highlighting the strengths and limitations of each tool for this use case. By performing automated time-series analyzes, sample management, and updating of reference databases, MMonitor addresses current limitations and supports dynamic microbiome research in various fields. IMPORTANCEMetagenome monitoring is essential for understanding and managing microbial communities in dynamic environments. Rapid and accurate analysis of these communities allows for timely interventions in biologically active environments, for example, in industrial biotechnology, environmental surveillance, and medical diagnostics. Metagenome monitoring tools should have real-time capabilities, intuitive interfaces, convenient setup and distribution, and suitable visual representations for effective monitoring. MMonitor addresses these challenges by offering a platform that combines real-time analysis of nanopore sequencing data with customizable pipelines and advanced visualization tools. It is designed to be accessible to researchers of all levels of computational skill. It supports efficient taxonomic and functional analyses based on popular metagenome software and automates taxonomic profiling, genome assembly, binning, and annotation. The dashboard helps to interpret data and present results. MMonitor is designed to track metagenomes and we hope that it can impact microbiome research and applications in health, biotechnology, agricultural, and environmental sciences.

bioinformatics↗

bioRxiv2024

The gases H2 and O2 in open-culture reactors influence the performance and microbiota of chain elongation into n-caproate and n-caprylate.

Medium-chain carboxylates are used in various industrial applications. These chemicals are typically extracted from palm oil, which is deemed not sustainable. Recent research has focused on microbial chain elongation using reactors to produce medium-chain carboxylates, such as n-caproate (C6) and n-caprylate (C8), from organic substrates such as wastes. Even though the production of n-caproate is relatively well-characterized, bacteria and metabolic pathways that are responsible for n-caprylate production are not. Here, three 5-L reactors with continuous membrane-based liquid-liquid extraction (i.e., pertraction) were fed ethanol and acetate and operated for an operating period of 234 days with different operating conditions. Metagenomic and metaproteomic analyses were employed. n-Caprylate production rates and reactor microbiomes differed between reactors even when operated similarly due to differences in H2 and O2 between the reactors. The complete reverse {beta}-oxidation pathway was present and expressed by several bacterial species in the Clostridia class. Several Oscillibacter spp., including Oscillibacter valericigenes, were positively correlated with n-caprylate production rates, while Clostridium kluyveri was positively correlated with n-caproate production. Pseudoclavibacter caeni, which is a strictly aerobic bacterium, was abundant across all the operating periods, regardless of n-caprylate production rates. This study provides insight into microbiota that are associated with n-caprylate production in open-culture reactors and provides ideas for further work. ImportanceMicrobial chain elongation pathways in open-culture biotechnology systems can be utilized to convert organic waste and industrial side streams into valuable industrial chemicals. Here, we investigated the microbiota and metabolic pathways that produce medium-chain carboxylates, including n-caproate (C6) and n-caprylate (C8), in reactors with in-line product extraction. Although the reactors in this study were operated similarly, different microbial communities dominated and were responsible for chain elongation. We found that different microbiota were responsible for n-caproate or n-caprylate production, and this can inform engineers on how to operate the systems better. We also observed which changes in operating conditions steered the production toward and away from n-caprylate, but more work is necessary to ascertain a mechanistic understanding that could be predictive. This study provides pertinent research questions for future work.

microbiology↗

bioRxiv2024

mgPGPT: Metagenomic analysis of plant growth-promoting traits

In a recent publication, we introduce the PGPT ontology and PGPT-db of bacterial plant growth-promotion traits and associated database of protein sequences, and provide several tools for bacterial genome analysis on the PLaBAse server. Here, we extend the scope of the PGPT ontology to perform PGPT analysis of metagenomic datasets. First, we introduce mgPGPT-db, an extended database of 39, 582, 183 protein sequences obtained computationally by including proteins from AnnoTree. With this, we have integrated the PGPT ontology into our metagenome analysis tool MEGAN and provide mapping files to identify PGPT-related genes using the results of a DIAMOND alignment of reads against either the new mgPGPT-db database, the NCBI-nr protein database, or the AnnoTree protein database. We demonstrate and compare these different approaches in detail on an example data set and evince the improvement compared to the PGPT-db. We also compare the inferred PGPT content of several samples taken from different environments and reveal plant specific PGPT clustering. IMPORTANCEA deeper understanding of plant growth-promoting traits of bacteria is important to enlight and enhance the native plant-beneficial bacterial functional diversity regarding the environmental stress adaption or the ability to suppress even food-borne pathogens by strain inoculation dedicated to agriculture and other plant production systems. The work presented here extends recent work beyond the analysis of individual to allow the assessment of the PGPT potential of metagenomes obtained from environmental samples.

bioinformatics↗

bioRxiv2023

MuLan-Methyl - Multiple Transformer-based Language Models for Accurate DNA Methylation Prediction

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep-learning framework for predicting DNA methylation sites, which is based on five popular transformer-based language models. The framework identifies methylation sites for three different types of DNA methylation, namely N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pre-train and fine-tune" paradigm. Pre-training is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA-methylation status of each type. The five models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source and we provide a web server that implements the approach. Key pointsO_LIMuLan-Methyl aims at identifying three types of DNA-methylation sites. C_LIO_LIIt uses an ensemble of five transformer-based language models, which were pre-trained and fine-tuned on a custom corpus. C_LIO_LIThe self-attention mechanism of transformers give rise to importance scores, which can be used to extract motifs. C_LIO_LIThe method performs favorably in comparison to existing methods. C_LIO_LIThe implementation can be applied to chromosomal sequences to predict methylation sites. C_LI

bioinformatics↗

bioRxiv2021

PLaBAse: A comprehensive web resource for analyzing the plant growth-promoting potential of plant-associated bacteria

Plant-beneficial microorganisms are gaining importance for sustainable plant production and phytosanitary practices. Yet there is a lack of computational approaches targeting bacterial traits associated with plant growth-promotion (PGP), which hinders the in-silico identification, comparison, and selection of phytostimulatory bacterial strains. To address this problem, we have developed the new web resource PLaBAse (v1.01, http://plabase.informatik.uni-tuebingen.de/pb/plabase.php), which provides a number of services, including (i) a database for screening 5,565 plant-associated bacteria (PLaBA-db), (ii) a tool for predicting plant growth-promoting traits (PGPTs) of single bacterial genomes (PGPT-Pred), and (iii) a tool for the prediction of bacterial plant-association by marker gene identification (PIFAR-Pred). The latter was developed by Martinez-Garcia et al. and is now hosted at University of Tuebingen. The PGPT-Pred tool is based on our new PGPT ontology, a literature- and OMICs-curated, comprehensive, and hierarchical collection of [~]6,900 PGPTs that are associated with 6,965,955 protein sequences. To study the distribution of the PGPTs across different environments, we applied it to 70,540 bacterial strains associated with (i) seven different environments (including plants), (iii) five different plant spheres (organs), and (iii) two bacteria-induced plant phenotypes. This analysis revealed that plant-symbiotic bacteria generally have a larger genome size and a higher count of PGPT-annotated protein encoding genes. Obviously, not all reported PGPTs are restricted to -or only enriched in-plant-associated and plant symbiotic bacteria. Some also occur in human- and animal-associated bacteria, perhaps due to the transmission of PGP bacteria (PGPBs) between environments, or because some functions are involved in adaption processes to various environments. Here we provide an easy-to-use approach for screening of PGPTs in bacterial genomes across various phyla and isolation sites, using PLaBA-db, and for standardized annotation, using PGPT-Pred. We believe that this resource will improve our understanding about the entire PGP processes and facilitate the prediction of PGPB as bio-inoculants and for biosafety strategies, so as to help to establish sustainable and targeted bacteria-incorporated plant production systems in the future.

bioinformatics↗

bioRxiv2021

Using AnnoTree to get more assignments, faster, in DIAMOND+MEGAN microbiome analysis

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein-reference database such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr, and increased assignment rates, in particular, assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCEThe NCBI-nr database is not explicitly designed for the purpose of microbiome analysis and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, and so should be supported by alignment-based pipelines.

bioinformatics↗

bioRxiv2021

Phylogenetic context using phylogenetic outlines

AO_SCPLOWBSTRACTC_SCPLOWMicrobial studies typically involve the sequencing and assembly of draft genomes for individual microbes or whole microbiomes. Given a draft genome, one first task is to determine its phylogenetic context, that is, to place it relative to the set of related reference genomes. We provide a new interactive graphical tool that addresses this task using Mash sketches to compare against all bacterial and archaeal representative genomes in the GTDB taxonomy, all within the framework of SplitsTree5. The phylogenetic context of the query sequences is then displayed as a phylogenetic outline, a new type of phylogenetic network that is more general that a phylogenetic tree, but significantly less complex than other types of phylogenetic networks. We propose to use such networks, rather than trees, to represent phylogenetic context, because they can express uncertainty in the placement of taxa, whereas a tree must always commit to a specific branching pattern. We illustrate the new method using a number of draft genomes of different assembly quality.

bioinformatics↗

bioRxiv2020

The structure of autocatalytic networks, with application to early biochemistry

Metabolism across all known living systems combines two key features. First, all of the molecules that are required are either available in the environment or can be built up from available resources via other reactions within the system. Second, the reactions proceed in a fast and synchronised fashion via catalysts that are also produced within the system. Building on early work by Stuart Kauffman, a precise mathematical model for describing such self-sustaining autocatalytic systems (RAF theory) has been developed to explore the origins and organisation of living systems within a general formal framework. In this paper, we develop this theory further by establishing new relationships between classes of RAFs and related classes of networks, and developing new algorithms to investigate and visualise RAF structures in detail. We illustrate our results by showing how it reveals further details into the structure of archaeal and bacterial metabolism near the origin of life, and provide techniques to study and visualise the core aspects of primitive biochemistry.

evolutionary biology↗

bioRxiv2019

Anti-consensus: detecting trees that have an evolutionary signal that is lost in consensus

AO_SCPLOWBSTRACTC_SCPLOWIn phylogenetics, a set of gene trees is often summarized by a consensus tree, such as the majority consensus, which is based on the set of all splits that are present in more than 50% of the input trees. A \"consensus network\" is obtained by lowering the threshold and considering all splits that are contained in 10% of the trees, say, and then computing the corresponding splits network. By construction and in practice, a consensus network usually shows the majority tree, extended by a number of rectangles that represent local rearrangements around internal nodes of the consensus tree. This may lead to the false conclusion that the input trees do not differ in a significant way because \"even a phylogenetic network\" does not display any large discrepancies. To harness the full potential of a phylogenetic network, we introduce the new concept of an anti-consensus network that aims at representing the largest interesting discrepancies found in a set of gene trees. We provide an efficient algorithm for computing an anti-consensus and illustrate its application using a set of gene trees from species and strains in the genus Kosakonia.

bioinformatics↗

bioRxiv2019

Annotated bacterial chromosomes from frame-shift-corrected long read metagenomic data

BackgroundShort-read sequencing technologies have long been the work-horse of microbiome analysis. Continuing technological advances are making the application of long-read sequencing to metagenomic samples increasingly feasible.\n\nResultsWe demonstrate that whole bacterial chromosomes can be obtained from a complex community, by application of MinION sequencing to a sample from an EBPR bio-reactor, producing 6Gb of sequence that assembles in to multiple closed bacterial chromosomes. We provide a simple pipeline for processing such data, which includes a new approach to correcting erroneous frame-shifts.\n\nConclusionsAdvances in long read sequencing technology and corresponding algorithms will allow the routine extraction of whole chromosomes from environmental samples, providing a more detailed picture of individual members of a microbiome.

bioinformatics↗

bioRxiv2018

Improving recovery of member genomes from enrichment reactor microbial communities using MinION--based long read metagenomics

New long read sequencing technologies offer huge potential for effective recovery of complete, closed genomes. While much progress has been made on cultured isolates, the ability of these methods to recover genomes of member taxa in complex microbial communities is less clear. Here we examine the ability of long read data to recover genomes from enrichment reactor metagenomes. Such modified communities offer a moderate level of complexity compared to the source communities and so are realistic, yet tractable, systems to use for this problem. We sampled an enrichment bioreactor designed to target anaerobic ammonium-oxidising bacteria (AnAOB) and sequenced genomic DNA using both short read (Illumina 301bp PE) and long read data (MinION Mk1B) from the same extraction aliquot. The community contained 23 members, of which 16 had genome bins defined from an assembly of the short read data. Two distinct AnAOB species from genus Candidatus Brocadia were present and had complete genomes, of which one was the most abundant member species in the community. We can recover a 4Mb genome, in 2 contigs, of long read assembled sequence that is unambiguously associated with the most abundant AnAOB member genome. We conclude that obtaining near closed, complete genomes of members of low-medium microbial communities using MinION long read sequence is feasible.

genomics↗

bioRxiv2018

Human salivary amylase gene copy number impacts oral and gut microbiomes

Host genetic variation influences the composition of the human microbiome. While studies have focused on associations between the microbiome and single nucleotide polymorphisms in genes, their copy number (CN) can also vary. Here, in a study of human subjects including a 2-week standard diet, we relate oral and gut microbiome to CN at the AMY1 locus, which encodes the gene for salivary amylase, active in starch degradation. We show that although diet standardization drove gut microbiome convergence, AMY1-CN influenced oral and gut microbiome composition and function. The gut microbiomes of low-AMY1-CN subjects had an enhanced capacity for breakdown of complex carbohydrates. Those of high-AMY1 subjects were enriched in microbiota linked to resistant starch fermentation, had higher fecal SCFAs, and drove higher adiposity when transferred to germfree mice. Gut microbiota results were validated in a larger separate population. This study establishes AMY1-CN as a genetic factor patterning microbiome composition and function.

genetics↗