Biology subjects

Soeding, J.

Publications and source records attributed to Soeding, J..

At least 19 recordsLinked to original sources

bioRxiv2026

PhyloScore: Improved covariation score for RNA alignments using phylogeny

Motivation: Structured RNAs can carry out complex biological functions similar to proteins, but our understanding of RNA structure and function lags far behind that of proteins. Covariation signals arising from RNA evolution can be recovered from sequence alignments and provide valuable insights into structure and function. However, such signals can also arise purely as a result of phylogenetic relatedness. We aimed to develop a method which can make use of the phylogenetic tree to distinguish better between real and false signal. Results: PhyloScore predicts the manually curated secondary structure annotations in Rfam more accurately than the best currently used scores.On 48 Rfam families with experimentally determined structures, PhyloScore increases the mean average precision for recovering the annotated base pairs from 0.679 (R-scape's APC-corrected G-test) to 0.714 under cross-validation, and improves 41 of the 48 families. Its p-values are discriminative across families, making them valuable for testing whether a non-coding RNA has a conserved structure. Availability: PhyloScore is available as a pip package "phylo score" under MIT Licence. The code repository is at https://github.com/ soedinglab/phylo_score. The version described in this paper, including test data, is archived at https://doi.org/10.5281/zenodo.23401723

bioinformatics↗

bioRxiv2026

MMAD-Risk: Multivariate Mixed Survival Analysis for the Prediction of Age-Dependent Disease Risks from Plasma Proteomes

Motivation: Multivariate survival analysis with hundreds of correlated outcomes is computationally challenging. Established approaches either ignore correlations between response variables, rely on black-box deep learning or are limited to small-scale outcomes. Results: We introduce MMAD-Risk, a novel multivariate mixed accelerated failure time (AFT) model that enables scalable analysis of high-dimensional survival analysis data. We train MMAD-Risk using amortized variational inference where we design the variational distribution such that it factorizes across diseases, allowing us to decompose multivariate disease risk prediction into a series of tractable, one-dimensional problems. This allows us to calculate the ELBO analytically, enabling fast computation. The model employs a low-rank decomposition of the effect size matrix B = VW to capture shared disease mechanisms and latent random effects Vz to model comorbidity. MMAD-Risk is trained on the UK Biobank Pharma Proteomics cohort (N {approx} 55000, P {approx} 3000 proteins, D = 271 diseases). Using the full 3,000-protein dataset, MMAD-Risk achieved a mean concordance index (c-index) of 0.744 for diagnoses occurring [>=] 10 years after blood sample collection, outperforming a Cox proportional hazards model (mean c-index = 0.709). Greedy backward selection identified a 10-protein panel that preserved > 99% of the full-model performance. On this reduced panel MMAD-Risk still outperformed Cox regression (0.738 vs. 0.679).

bioinformatics↗

bioRxiv2026

Analysis of multi-way chromatin interactions at sub-nucleosome resolution identifies synergistic nano-scale interactions within super-enhancers

In-depth understanding of the 3D organization of the genome and its role in the regulation of gene expression requires techniques to measure complex chromosome conformations at single alleles at high resolution. Here, we present multi-way Micro-Capture-C (mwMCC), a method to detect concurrent interactions between multiple chromatin regions at single alleles at sub-nucleosome resolution. mwMCC resolves the internal 3D structure of super-enhancers and identifies synergistic multi-way interactions between their constituent elements and gene promoters, providing a structural basis for cooperation within super-enhancers in nano-scale chromatin hubs. The dynamic formation and dissolution of these hubs during cellular differentiation correlates with up- and down-regulation of gene expression. Although large-scale chromatin hubs are dependent on the architectural protein CTCF, nano-scale hubs are robust to CTCF depletion. Instead, integration of footprinting analysis and protein occupancy data indicates a role for transcription factors and the co-activator Mediator in the formation of synergistic enhancer-promoter interactions within nano-scale chromatin hubs.

genomics↗

bioRxiv2025

PMGen: From Peptide-MHC Prediction to Neoantigen Generation

Accurate structural modeling of peptide-MHC (pMHC) complexes is a prerequisite for understanding adaptive immunity and developing data-driven immunotherapies. However, current tools are often limited by narrow class coverage, restricted peptide lengths, or insufficient accuracy for downstream design tasks. Here, we introduce PMGen (Peptide MHC Generator), an integrated framework for structure prediction and structure-guided design of variable-length peptides across MHC class I and II. By introducing Initial Guess and Template Engineering as strategies to enforce anchor constraints in AlphaFold2, PMGen achieves state-of-the-art structural fidelity with median peptide core RMSDs of 0.54 [A] for MHC-I and 0.33 [A] for MHC-II, outperforming five state-of-the-art methods. We further demonstrate that PMGen captures the subtle structural impact of single-point neoantigen mutations and that model confidence (pLDDT) reliably correlates with structural accuracy. We investigated two potential applications of our framework: structure-aware peptide design and generating data for machine learning (ML) models. To this end, we introduced a framework to sample peptides with preserved structures and improved binding affinity. As an example for ML application, we fine-tuned ProteinMPNN on PMGen-modeled structures. This improved sequence recovery from 0.19 to 0.40 compared to the baseline. Ultimately, PMGen bridges the gap between high-fidelity structural prediction and downstream sequence design, offering a scalable solution to generate the large-scale, high-quality structural datasets required to train advanced predictive models in immunology. Available at https://github.com/soedinglab/PMGen.

bioinformatics↗

bioRxiv2025

Enhancing genome recovery across metagenomic samples using MAGmax

SummaryThe number of metagenome-assembled genomes (MAGs) is rapidly increasing with the growing scale of metagenomic studies, driving fast progress in microbiome research. Sample-wise assembly has become the standard due to its computational efficiency and strain-level resolution. It requires dereplication, the removal of near-identical genomes assembled in different metagenomic samples. We present MAGmax, an efficient dereplication tool that enhances both the quantity and quality of MAGs through a strategy of bin merging and re-assembly. Unlike dRep, which selects a single representative bin per genome cluster, MAGmax merges multiple bins within a cluster and reassembles them to increase coverage. MAGmax produces more dereplicated, higher-quality MAGs than dRep at 1.6x its speed and using three times less memory. Availability and implementationThe MAGmax open source software, implemented in Rust, is available under the GPLv3 license at https://github.com/soedinglab/MAGmax.

bioinformatics↗

bioRxiv2025

Evaluation of Metagenome Binning: Advances and Challenges

BackgroundSeveral recent deep learning methods for metagenome binning claim improvements in the recovery of high quality metagenome-assembled genomes. These methods differ in their approaches to learn the contig embeddings and to cluster them. Rapid advances in binning require rigorous benchmarking to evaluate the effectiveness of new methods. We have benchmarked newly developed state-of-the-art deep learning binners on CAMI2 datasets, including our own, McDevol. ResultsThe results show that COMEBin and GenomeFace give the best binning accuracy, although not always the best embedding accuracy. Interestingly, post-binning reassembly consistently improves the quality of low coverage bins. We find that binning coassembled contigs with multi-sample coverage is effective for low coverage dataset while binning multi-sample contigs with multi-sample coverage ( multi-sample) is effective for high-coverage samples. In multi-sample binning, splitting the embedding space by sample before clustering showed enhanced performance compared to the standard approach of splitting final clusters by sample. ConclusionsCOMEBin and GenomeFace emerged as the top-performing tools overall, with MetaBAT2 and GenomeFace demonstrating superior speed. To facilitate future development, we provide workflows for standardized benchmarking of metagenome binners.

bioinformatics↗

bioRxiv2024

Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids

Amyloids are insoluble protein aggregates with a cross-beta structure, which are traditionally associated with neurodegeneration. Similar structures, named functional amyloids, expressed mostly by microorganisms, play important physiological roles, e.g. bacterial biofilm stabilization. Using a bioinformatics approach, we identify gut microbiome functional amyloids and analyze their potential impact on human health via the gut-brain axis. The results point to taxonomically diverse sources of functional amyloids and their frequent presence in the extracellular space. The retrieved interactions between gut microbiome functional amyloids and human proteins indicate their potential to trigger inflammation, affect transport and signaling processes. We also find a greater relative abundance of bacterial functional amyloids in patients diagnosed with Parkinsons disease and specifically a higher content of the curli amyloid protein, CsgA, in Alzheimers disease patients than in healthy controls. Our results provide a rationale for the tentative link between neurodegeneration and gut bacterial functional amyloids.

bioinformatics↗

bioRxiv2024

UniOP: a universal operon prediction for high-throughput prokaryotic (meta-)genomic data using intergenic distance

The study of the deluge of metagenomic and genomic sequences is challenging due to the severe lack of function information. Predicting operons, groups of functionally related genes in prokaryotic genomes, is critical for bridging this gap. However, existing methods for operon prediction heavily rely on experimental data, functional annotations, or extensive characterization of homologous genes, making it difficult to accurately predict operons in newly sequenced or poorly characterized genomes. Here, we introduce UniOP, an unsupervised approach that uses a statistical model to predict operons from intergenic distances directly derived from the target genomic sequence. UniOP not only outperforms alternative approaches on ten complete genomes but also shows superior results on 3269 metagenome-assembled genomes across 13 bacterial and 2 archaeal phyla. Furthermore, we explored enhancing UniOP by incorporating the conservation of gene neighborhood and strandedness in respective genomes and examined the influence of Pfam annotations and motif searching on its performance.

bioinformatics↗

bioRxiv2024

De novo discovery of conserved gene clusters in microbial genomes with Spacedust

Metagenomics has revolutionized environmental and human-associated microbiome studies. However, the limited fraction of proteins with known biological process and molecular functions presents a major bottleneck. In prokaryotes and viruses, evolution favors keeping genes participating in the same biological processes co-localized as conserved gene clusters. Conversely, conservation of gene neighborhood indicates functional association. Spacedust is a tool for systematic, de novo discovery of conserved gene clusters. To find homologous protein matches it uses fast and sensitive structure comparison with Foldseek. Partially conserved clusters are detected using novel clustering and order conservation P-values. We demonstrate Spacedusts sensitivity with an all-vs-all analysis of 1 308 bacterial genomes, identifying 72 843 conserved gene clusters containing 58% of the 4.2 million genes. It recovered 95% of antiviral defense system clusters annotated by a specialized tool. Spacedusts high sensitivity and speed will facilitate the annotation of the huge numbers of sequenced bacterial, archaeal and viral genomes.

bioinformatics↗

bioRxiv2024

CarpeDeam: A De Novo Metagenome Assemblerfor Heavily Damaged Ancient Datasets

De novo assembly of ancient metagenomic datasets is a challenging task. Ultra-short fragment size and characteristic postmortem damage patterns of sequenced ancient DNA molecules leave current tools ill-equipped for ideal assembly. We present CarpeDeam, a novel damage-aware de novo assembler designed specifically for ancient metagenomic samples. Utilizing maximum-likelihood frameworks that integrate sample-specific damage patterns, CarpeDeam demonstrates improved recovery of longer continuous sequences and protein sequences in many simulated and empirical datasets compared to existing assemblers. As a pioneering ancient metagenome assembler, CarpeDeam opens the door for new opportunities in functional and taxonomic analyses of ancient microbial communities.

genomics↗

bioRxiv2024

Rapid and Sensitive Protein Complex Alignment with Foldseek-Multimer

Advances in computational structure prediction will vastly augment the hundreds of thousands of currently-available protein complex structures. Translating these into discoveries requires aligning them, which is computationally prohibitive. Foldseek-Multimer computes complex alignments from compatible chain-to-chain alignments, identified by efficiently clustering their superposition vectors. Foldseek-Multimer is 3-4 orders of magnitudes faster than the gold standard, while producing comparable alignments; allowing it to compare billions of complex-pairs in 11 hours. Foldseek-Multimer is open-source software: github.com/steineggerlab/foldseek, webserver: search.foldseek.com and the BFMD database.

bioinformatics↗

bioRxiv2024

Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs

BackgroundMetagenomics is a powerful approach to study environmental and human-associated microbial communities and, in particular, the role of viruses in shaping them. Viral genomes are challenging to assemble from metagenomic samples due to their genomic diversity caused by high mutation rates. In the standard de Bruijn graph assemblers, this genomic diversity leads to complex k-mer assembly graphs with a plethora of loops and bulges that are challenging to resolve into strains or haplotypes because variants more than the k-mer size apart cannot be phased. In contrast, overlap assemblers can phase variants as long as they are covered by a single read. ResultsHere, we present PenguiN, a software for strain resolved assembly of viral DNA and RNA genomes and bacterial 16S rRNA from shotgun metagenomics. Its exhaustive detection of all read overlaps in linear time combined with a Bayesian model to select strain-resolved extensions allow it to assemble longer viral contigs, less fragmented genomes, and more strains than existing assembly tools, on both real and simulated datasets. We show a 3-40-fold increase in complete viral genomes and a 6-fold increase in bacterial 16S rRNA genes. ConclusionPenguiN is the first overlap-based assembler for viral genome and 16S rRNA assembly from large and complex metagenomic datasets, which we hope will facilitate studying the key roles of viruses in microbial communities.

bioinformatics↗

bioRxiv2022

Foldseek: fast and accurate protein structure search

As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing the amino acid backbone of proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of DALI, TM-align and CE, respectively.

bioinformatics↗

bioRxiv2021

Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

MotivationUnderstanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de-novo discovery of RNA binding motifs do not take multivalent binding into account. ResultsWe present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two cooperatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions. AvailabilityBMF source code is available at https://github.com/soedinglab/bipartite_motif_finder under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org.

bioinformatics↗

bioRxiv2020

Fast and sensitive taxonomic assignment to metagenomic contigs

MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contigs taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2-18x faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments. AvailabilityMMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com. Contacteli.levy.karin@gmail.com

bioinformatics↗

bioRxiv2020

Large-scale analysis of Drosophila core promoter function using synthetic promoters

The core promoter, the region immediately surrounding the transcription start site, plays a central role in setting metazoan gene expression levels, but how exactly it computes expression remains poorly understood. To dissect core promoter function, we carried out a comprehensive structure-function analysis to measure synthetic promoters activities, with and without an external stimulus (hormonal activation). By using robotics and a dual-luciferase reporter assay, we tested [~]3000 mutational variants representing 19 different Drosophila melanogaster promoter architectures. We explored the impact of different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, positioning, and flanking sequences. We observe strong effects of the mutations on activity, and a linear combination of the individual motif features can largely account for the combinatorial effects on core promoter activity. Our findings shed new light on the quantitative assessment of gene expression, a fundamental process in all metazoans.

systems biology↗

bioRxiv2020

SpacePHARER: Sensitive identification of phages from CRISPR spacers in prokaryotic hosts

SpacePHARER (CRISPR Spacer Phage-Host Pair Finder) is a sensitive and fast tool for de novo prediction of phage-host relationships via identifying phage genomes that match CRISPR spacers in genomic or metagenomic data. SpacePHARER gains sensitivity by comparing spacers and phages at the protein level, optimizing its scores for matching very short sequences, and combining evidence from multiple matches, while controlling for false positives. We demonstrate SpacePHARER by searching a comprehensive spacer list against all complete phage genomes.

bioinformatics↗

bioRxiv2020

Reverse regression increases power for detecting trans-eQTLs

Trans-acting expression quantitative trait loci (trans-eQTLs) are genetic variants affecting the expression of distant genes. They account for [≥]70% expression heritability and could therefore facilitate uncovering mechansisms underlying the origination of complex diseases. However, unlike cis-eQTLs, identifying trans-eQTLs is challenging because of small effect sizes, tissue-specificity, and the severe multiple-testing burden. Trans-eQTLs affect multiple target genes, but aggregating evidence over individual SNP-gene associations is hampered by strong gene expression correlations resulting in correlated p-values. Our method Tejaas predicts trans-eQTLs by performing L2-regularized reverse multiple regression of each SNP on all genes, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Combined with a novel non-linear, unsupervised k-nearest-neighbor method to remove confounders, Tejaas predicted 18851 unique trans-eQTLs across 49 tissues from GTEx. They are enriched in open chromatin, enhancers and other regulatory regions. Many overlap with disease-associated SNPs, pointing to tissue-specific transcriptional regulation mechanisms. Tejaas is available under GPL at https://github.com/soedinglab/tejaas.

genetics↗