Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• TogoMCP: Natural Language Querying of Life-Science Knowledge Graphs via Schema-Guided LLMs and the Model Context Protocol

  Kinjo, A. R.; Yamamoto, Y.; Bustamante-Larriet, S.; Labra-Gayo, J. E.; Fujisawa, T.

  Abstract

  Querying the RDF Portal knowledge graph maintained by DBCLS, which aggregates approximately 60 life-science databases, requires proficiency in both SPARQL and database-specific RDF schemas, placing this resource beyond the reach of most researchers. Large Language Models (LLMs) can, in principle, translate natural-language questions into executable SPARQL, but without schema-level context, they frequently fabricate non-existent predicates or fail to resolve entity names to database-specific identifiers. We present TogoMCP, a system that recasts the LLM as a protocol-driven inference engine orchestrating specialized tools via the Model Context Protocol (MCP). Two mechanisms are essential to its design: (i) the MIE (Metadata-Interoperability-Exchange) file, a concise YAML document that dynamically supplies the LLM with each target database's structural and semantic context at query time; and (ii) a two-stage workflow separating entity resolution via external REST APIs from schema-guided SPARQL generation. On a benchmark of 50 biologically grounded questions spanning five types and 23 databases, TogoMCP achieved a large improvement over an unaided baseline (Cohen's d = 1.82, Wilcoxon p < 0.001), with win rates exceeding 80% for question types with precise, verifiable answers. An ablation study shows that all component configurations deliver significant improvements, with MIE schema files providing the largest marginal contribution on mean per-question score ({Delta} = +0.50 relative to a no-MIE condition, two-sided Wilcoxon p = 0.067; 90% bootstrap CI [+0.04, +0.94] excludes zero); a one-line instruction to load the relevant MIE file recovers the same mean improvement as a full procedural protocol, while the protocol additionally reduces downside risk (loss rate 1.6% vs. 4.8%, Fisher p = 0.036). These results suggest a general design principle: concise, dynamically delivered schema context is more valuable than complex orchestration logic for mean-score performance, while procedural guidance plays a complementary role in narrowing variance.

  bioinformatics2026-07-06v3

• TAFFISH: shell-native command-level reproducibility for bioinformatics

  Han, K.; Wang, T.; Yuan, S.-S.; Ma, C.-Y.; Su, W.; Li, X.; Deng, K.; Lin, H.; Lyu, H.

  Abstract

  Bioinformatics analyses often rely on shell commands and small shell scripts whose executable context is difficult to preserve, inspect and reuse. TAFFISH addresses this gap by packaging command-line tool calls and lightweight shell flows as installable, versioned and inspectable executable units. Through a curated public Hub, TAFFISH indexes command interfaces, execution backends, platform constraints, release metadata and smoke-test/validation records. Together, these components provide a command-level reproducibility layer that works directly in ordinary shells and can also be invoked from existing workflow systems.

  bioinformatics2026-07-06v3

• Beyond additivity: zero-shot methods cannot predict impact of epistasis on protein properties and function

  Kolchina, A.; Dubanevics, I.; Kondrashov, F. A.; Kalinina, O. V.

  Abstract

  Accurate prediction of properties and function of mutated proteins is crucial for both research and industrial applications. Experimental assessment of mutations relies on biochemical techniques, which, while accurate, are costly and labour-intensive. As an alternative, computational methods have emerged as a scalable and cost-effective solution. A key challenge for predicting functional consequences of mutations is epistasis, a phenomenon where the effect of one mutation is influenced by others. We evaluated the ability of 95 zero-shot models to predict the impact of epistasis on proteins using datasets from ProteinGym. Our results demonstrate that while the current models perform well for single mutations and non-epistatic combinations of mutations, they fail to predict the effect of strongly epistasic combinations of mutations. This exposes deficiencies of the state-of-the-art models and the need for focusing on capturing complex mutational interactions, which is essential for advancing both evolutionary studies and protein design.

  bioinformatics2026-07-06v2

• Cell signaling pathways discovery from multi-modal data

  He, C.; Simpson, C.; Cossentino, I.; Zhang, B.; Tkachev, S.; Eddins, D. J.; Kosters, A.; Yang, J.; Sheth, S.; Levy, T.; Possemato, A.; Huang, L.; Tabatsky, E.; Lee, S. H.; Ghosh, D.; George, A.; Gregoretti, I.; Ariss, M.; Dandekar, D.; Ausekar, A.; Roan, N. R.; Ghosn, E. E. B.; Colonna, M.; Rikova, K.; Nie, Q.; Orlova, D.

  Abstract

  Deciphering cell signaling pathways is key to understanding biology, disease mechanisms, and developing new therapies. Although advances in multi-omics technologies provide richer insight into signaling, the data remain high-dimensional, heterogeneous, and difficult to interpret, and current computational tools for inferring signaling pathways are limited. To address this, we developed Incytr, a method for efficient discovery of cell signaling pathways through integration of diverse data modalities, including transcriptomics, ATAC-seq, proteomics, phosphoproteomics, and kinomics. We demonstrate its application in COVID-19, Alzheimer's disease, and cancer, where it successfully recovers known pathways and generates novel, cell-type-specific hypotheses supported by multiple data types. We further show how integrating Incytr-derived pathways with biomarker and drug databases can support target and drug discovery. Finally, we show that using Incytr-derived signaling pathways as training data for simple natural language processing models can deepen our understanding of cell-cell communication and immune cell dynamics, while helping identify new therapeutic targets.

  bioinformatics2026-07-06v2

• A High-Quality Acetylation Dataset Reveals Modest Data Requirements for Transfer Learning to Identify Little Studied Post-Translational Modifications

  Hartmaring, Y.; Wang, S.; Jones, A. R.; Vizcaino, J. A.; Schlaffner, C. N.; Renard, B. Y.

  Abstract

  Dysregulation of post-translational modifications (PTMs) is associated with severe pathologies, including cancers and Alzheimer's disease. Despite their biological importance, identifying modified peptides remains challenging due to the immense combinatorial search space. While searches benefit from prior knowledge of a peptide's modification status, the data scarcity for most PTMs hinders the development of accurate deep learning classifiers like AHLF (ad hoc learning of peptide fragmentation). Here, we overcome this data bottleneck for acetylation and ubiquitination. We harmonised a dataset with about 500,000 high quality acetylated peptide-spectrum matches (PSMs) from nine publicly available acetylation-enriched datasets. We fine-tuned AHLF with the acetylation and a 2-million spectra strong ubiquitination dataset separately and assessed the minimum data requirement for training by iteratively downsampling. Training separate models on SILAC and label-free subsets also assessed the impact of data diversity. The resulting acetylation and ubiquitination models achieve an AUC of 0.87 and 0.90 respectively. Beyond 28,500 acetylated spectra, corresponding to roughly 0.3% of the original model's training data, additional data just provides minor performance gains. Finally, we show that data diversity is beneficial for generalizability, while models trained on homogeneous data sources tend to overfit to their respective data type. All code, and model weights are available at https://gitlab.com/dacs-hpi/ahlf-ptmai.

  bioinformatics2026-07-06v2

• The Portable Microhaplotype Object and Tools

  Hathaway, N. J.; Murie, K.; Murphy, M.; Simkin, A.; Amaya-Romero, J.; Hubbard, A.; Briggs, J.; Aranda-Diaz, A.; Early, A. M.; Wesolowski, A.; Neafsey, D. E.; Bailey, J. A.; Greenhouse, B.

  Abstract

  Motivation: The rapid increase in the generation of targeted sequencing data offers immense potential for research, medicine, and public health, however the lack of an established standard for these data has led to disparate solutions for data storage. A widely accepted standard is essential for data sharing, reuse, and the coordinated development of interoperable analysis tools. Results: We propose the Portable Microhaplotype Object (PMO), a standardized format for efficiently and losslessly storing phased targeted sequencing data (microhaplotypes). The PMO format is JSON-based, allowing efficient, relational storage of genetic data together with relevant metadata to minimize orphaned data. The format includes required fields and a curated set of optional fields leveraging established ontologies. To facilitate ease of use, we developed pmotools-python, an open-source package for creating, manipulating, and exporting PMO data into common formats. Additionally, we provide a simple web-based app to quickly create PMO files from tabular inputs, making the format accessible to a wide variety of users. Example datasets from Plasmodium, Anopheles, Escherichia coli, and Staphylococcus aureus demonstrate the broad applicability of the approach. PMO will streamline data sharing, foster interoperability, and accelerate the development of harmonized analysis tools. Availability and implementation: The Portable Microhaplotype Object (PMO) project, including the ontology specification, software tools, example datasets, and tutorials, is freely available at https://plasmogenepi.github.io/PMO_Docs/. Key software components and datasets have archived releases with DOIs to ensure permanence, detailed in the Supplementary Text 1-5. Contact: kathrynmmurie@gmail.com or nickjhathaway@gmail.com

  bioinformatics2026-07-06v2

• OrthoGLMM: Phylogenetic Association Testing for Gene Content and Trait Evolution

  Guhlin, J. G.; Keddell, P.; Dearden, P.

  Abstract

  Motivation: Comparative genome projects can now assemble and annotate hundreds of species, creating an opportunity to test whether species-level traits are associated with repeated changes in gene content. These tests must account for shared ancestry, sparse orthogroups, rare trait origins, and thousands of simultaneous associations. Results: We present OrthoGLMM, a phylogenetically informed framework for the association of traits and orthogroup presence/absence or copy number across species. OrthoGLMM combines deterministic GLMM scans with solver-rerun empirical calibration and calibrated FDR estimation. In three benchmark datasets, OrthoGLMM recovered expected signals for bacterial diazotrophy, plant nodulation, and marine mammals. Availability and Implementation: Source code, documentation, example data, and reproducibility scripts will be available at http://github.com/jguhlin/OrthoGLMM.

  bioinformatics2026-07-06v1

• DELPHAI predicts heterogeneous perturbation responses with learned cell fitness and gene-space retrieval

  Zhang, X.; Wu, H.; Liu, H.

  Abstract

  Modelling heterogeneous cellular responses to perturbation holds the promise of scalable in silico screening and mechanistic insight. However, mass conservation despite cell-type-specific depletion, and lossy projections from gene space to latent space, hinder performance of state-of-the-art methods. DELPHAI, with learned per-cell-fitness filtering out depleted cells and gene-space retrieval bypassing the latent bottleneck, outperforms all baseline methods across two benchmark frameworks and offers explainability with inferred cell-type-specific survival without any biological priors.

  bioinformatics2026-07-06v1

• PEPstrMOD2: Next-generation tertiary structure prediction of chemically modified and non-natural peptides

  Jain, S.; Mehta, N. K.; Raina, S.; Kumar, P.; Varun, ; Raghava, G. P. S.

  Abstract

  While most existing methods are limited to predicting the tertiary structures of proteins containing only canonical residues, the PEPstrMOD server (developed in 2015) pioneered structure prediction for chemically modified and non-natural peptides. Despite its widespread use, the original framework was restricted to peptides of 7 to 25 residues and relied on older backbone-prediction algorithms. To address these limitations, we present PEPstrMOD2, which introduces three major advancements over its predecessor. First, it replaces the original in-house coordinate generation with state-of-the-art deep learning (DL) algorithms, leveraging AlphaFold2 and ESMFold for highly accurate initial structure prediction. Secondly, it greatly expands the accessible chemical space through incorporation of new, AMBER force-field compatible library of 257 post-translational modifications (PTMs), 428 non-canonical amino acids (NCAAs), and 243 terminal modifications. Lastly, through the application of native scalability of AlphaFold2 (AF2) and ESMFold (EF), PEPstrMOD2 eliminates the original restrictions of the length, enabling the structural modeling of longer, complex therapeutic peptides and small proteins. We evaluated the performance of PEPstrMOD2 against state-of-the-art methods across three distinct peptide datasets. For the AfCyc dataset consisting of 80 cyclic peptides, PEPstrMOD2 obtained a competitive average atom-level Root Mean Square Deviation (RMSD) of 2.05 angstroms, compared to 1.13 angstroms by AlphaFold3 (AF3) and 1.82 angstroms by AfCycDesign. Remarkably, for the modified peptide ModPep433 dataset, PEPstrMOD2 outperformed AF3, achieving the lower average RMSD score of 4.49 angstroms against 4.67 angstroms of AF3. Furthermore, in the case of the ModPep16 benchmark, PEPstrMOD2 achieved 2.50 angstroms average RMSD value, which is two times more accurate than that of the original PEPstrMOD (5.84 angstroms). In summary, PEPstrMOD2 provides a powerful, high-throughput, and highly accurate platform to facilitate peptide-based drug development and structural biology research. While the original PEPstrMOD was restricted to a web server interface, PEPstrMOD2 is available as both an intuitive webserver and a standalone command-line tool via GitHub, featuring Docker support for easy deployment and reproducible, large-scale modeling pipelines (https://webs.iiitd.edu.in/raghava/pepstrmod/).

  bioinformatics2026-07-06v1

• A control-validated pan-proteome deep-learning pipeline nominates GPR35 as a candidate target of the orphan bacterial metabolite ligiamycin A

  Martin, J.

  Abstract

  Most microbial natural products with documented bioactivity lack an identified molecular target, which limits their development. We present an open, control-validated computational pipeline for natural-product target hypothesis generation. It combines a pan-proteome deep-learning drug-target interaction (DTI) model (a graph neural-network ligand encoder, an ESM-2 protein language-model encoder, and bidirectional cross-attention) with bias-corrected ranking and control-anchored molecular docking. Applying it to ligiamycin A, a 2022-described Streptomyces/Achromobacter co-culture decalin-amino-maleimide with no reported target, we find that the predicted interactions of the compound are dominated by class-A G-protein-coupled receptors. Using a drug with a known target (losartan) we identify and correct a frequent-hitter bias in the raw model; after correction the standout candidates are uniformly class-A GPCRs, led by the orphan receptor GPR35. Structure-based docking with matched positive and negative controls across three candidates corroborates GPR35 specifically: ligiamycin A scores comparably to the known GPR35 agonist zaprinast at the agonist pocket (-8.1 vs -8.3 kcal/mol; non-binder floor -5.5), whereas FFAR1 is excluded and histamine H2 is inconclusive. We propose GPR35 as a prioritized, experimentally testable target and release the workflow as a reusable tool. The result is a computational hypothesis that requires experimental validation.

  bioinformatics2026-07-06v1

• Selecting Chromosomes for Polygenic Traits: Algorithms and Complexity

  Zuk, O.

  Abstract

  We define and study the problem of genomic block selection for multiple complex traits. In this problem, one constructs a genome by selecting different genomic parts (e.g. chromosomes) from different source genomes. The constructed genome is associated with a vector of polygenic scores, obtained by summing the polygenic scores of the different genomic parts, and the goal is to minimize a given loss function of this vector. The problem is motivated by several emerging technologies: chromosome substitution lines in crop breeding, where chromosomal segments from wild relatives are combined to improve polygenic traits such as yield and stress tolerance; chromosome transfer between yeast strains for optimizing complex industrial phenotypes; and chromosomal transplantation technologies in mammalian cells. We suggest and study several natural loss functions relevant for both quantitative and threshold traits, and show that the problem is NP-complete even for a single trait and two copies, yet only weakly so, being pseudo-polynomially solvable for any fixed number of traits. We propose three algorithms with complementary roles: a Branch-and-Bound algorithm that returns the certified global optimum for any monotone loss, a fast Block-Coordinate-Descent (BCD) heuristic with random restarts that applies to any loss, and a semidefinite-programming (SDP) relaxation that provides a certified lower bound on the optimal loss for quadratic losses, and hence an optimality-gap bound when paired with the BCD solution - empirically tight in our experiments. Using the infinitesimal model for genetic architecture, we further derive, for linear losses, a closed-form approximation for the expected gain of block selection relative to random selection across multiple traits. On yeast-scale simulations BCD matches the certified Branch-and-Bound optimum on 100% of threshold-loss instances at 466x the speed, attains a certified optimality gap of at most ~10% of the SDP lower bound for stabilizing-loss instances, and the realized gain roughly matches the analytic prediction.

  bioinformatics2026-07-05v2

• GTcomplex: Spatial indexing-powered search and alignment of macromolecular complexes

  Margelevicius, M.

  Abstract

  Structural alignment of macromolecular complexes is essential for understanding their function and evolution, yet existing methods often rely on aligning individual chains before inferring complex-level correspondences, leading to inaccuracies and inefficiencies. Here we present GTcomplex, a novel algorithm that employs spatial indexing to perform holistic complex-level alignment, directly deriving chain assignments from optimal global superpositions. Benchmarking on diverse datasets---including protein complexes, viral capsids, and nucleic acid complexes---demonstrates that GTcomplex achieves state-of-the-art accuracy with substantial speed improvements over current methods. These advances enable scalable, accurate comparison of compositionally diverse and large assemblies, facilitating structural annotation, evolutionary studies, and multimeric structure prediction. GTcomplex is available as a user-friendly software package and as a web service supporting high-throughput searches.

  bioinformatics2026-07-05v2

• Novel 4D tensor decomposition-based approach integrating tri-omics profiling data can identify functionally relevant gene clusters

  Turki, T.; Taguchi, Y.-h.

  Abstract

  Understanding gene expression requires integrating multiple regulatory layers, because transcript abundance does not necessarily correspond to translational activity or protein abundance. Ribosome profiling and proteomics help distinguish increased translation from ribosome stacking or translational buffering, but no de facto standard framework exists for unsupervised integration of transcriptome, translatome, and proteome profiles. Here, we propose a four-dimensional tensor decomposition-based unsupervised feature extraction approach for tri-omics integration. We applied higher-order singular value decomposition to transcriptome, Ribo-seq, and proteome profiles measured under branched-chain amino acid starvation. The resulting singular value vectors captured relationships among the three omics layers, including a component consistent with ribosome stacking, where transcriptome and translatome signals increased while proteome signals decreased, and another consistent with translational buffering, where proteome variation was suppressed despite transcriptome and translatome changes. Gene selection identified 1,781 genes associated with ribosome stacking and 227 genes associated with translational buffering. Enrichment analyses linked the former to translation, post-translational protein modification, RNA polymerase II transcription, cell cycle regulation, endoplasmic reticulum protein processing, ubiquitin-mediated proteolysis, and stress-related pathways, and the latter to ribosome, translation elongation and termination, spliceosome, immune- and stress-related pathways, and ribosomopathy-associated diseases. Robustness analyses indicated that the results were not substantially affected by the duplicated proteome replicate or missing-value handling. Under the tested settings, comparison with MOFA+ and mixOmics suggested that our approach more directly extracted components interpretable as ribosome stacking and translational buffering. These results demonstrate that tensor decomposition-based unsupervised feature extraction is useful for identifying functionally relevant gene clusters from tri-omics data.

  bioinformatics2026-07-04v3

• Binary search and set operations on compacted k-mer lists

  Dufresne, Y.; Andreace, F.

  Abstract

  Sorted lists of elements are particularly good for computing set operations. A single scan of the two lists is sufficient to materialize or count the results of the union, intersection, difference, and xor operators. In bioinformatics, only a few tools are designed to perform these operations on k-mers. A fast tool like KMC allows set operations at the cost of storing individual k-mers. In this paper, we introduce a novel way to represent sorted k-mers as a collection of recomposed super-k-mer sorted lists. We introduce the concept of virtual super-k-mer and show how to construct, query and perform set operations on sorted lists of virtual super-k-mers. In the implementation sklib, we demonstrate high throughput of the data structure for construction and set operations, while remaining competitive in query capabilities, within a controlled memory footprint (2-5x decrease in bits/element compared to KMC).

  bioinformatics2026-07-04v2

• A High-Confidence Atlas of Protein Methylation Enables AI-Driven Detection of Methylated Peptides

  Wang, S.; Hartmaring, Y.; Schlaffner, C. N.; Bowler-Barnett, E.; Martin, M.; Fan, J.; Sun, Z.; Renard, B. Y.; Jones, A. R.; Vizcaino, J. A. R.

  Abstract

  Lysine and arginine methylation regulate chromatin dynamics, transcription, and cellular signaling, however confident mass spectrometry (MS)-based detection and localization of this modification remain challenging. We reanalyzed eight public human methylation-enriched datasets using an open and standardized workflow that integrates database searching via the Trans-Proteomic Pipeline with a decoy-based statistical method for the independent estimation of false localization rates (FLR). This yielded a high-confidence Human Methylation Atlas of 1,828 sites (57 methyl-lysine, 1,771 methyl-arginine) across 1,021 proteins, classified into Gold, Silver, and Bronze confidence tiers. This is far fewer sites than reported in previous studies, reflecting the application of stringent FLR control, and what we hypothesise is potential high-false discovery in previous analyses. We then leveraged this resource to adapt a deep learning-based methodology for the improved detection of methylated peptides. Three mouse methylation-enriched datasets were reanalysed to augment training and the phosphoproteomics-trained AHLF (ad hoc learning of peptide fragmentation) model was fine-tuned by transfer learning to create AHLF-Methylation. The model achieved mean ROC-AUC values of 0.824 on human spectra, and 0.829 on combined human-mouse spectra. The atlas is available through PTMeXchange and PRIDE, with curated site evidence integrated into UniProt and PeptideAtlas

  bioinformatics2026-07-04v1

• Unbalanced Perturbation Dynamics For Cell Fate Design

  Peng, Q.; Wang, Y.; Li, J.; Wang, X.; Xiao, Y.; Zhou, P.

  Abstract

  Large-scale single-cell perturbation sequencing provides an unprecedented opportunity to construct virtual cells for the in silico simulation of cellular responses and the inverse design of optimal interventions. However, most perturbation-response models treat cellular responses primarily as mass-preserving shifts in transcriptomic state, whereas single-cell perturbation measurements are inherently unbalanced: the recovered endpoint population is shaped by technical sampling as well as biological perturbation-induced proliferation, apoptosis and selection. Here we introduce U-Pert, an unbalanced generative framework that learns condition- and context-dependent perturbation dynamics from unpaired single-cell snapshots. U-Pert jointly models transcriptomic state transitions and cell-number dynamics, enabling scalable and robust forward prediction of unseen perturbations and contexts, as well as inverse design to screen for desired genetic or pharmacological interventions that achieve user-defined transcriptomic or population-level outcomes. Across controlled simulations, genetic perturbation benchmarks, sciPlex3 drug responses and PBMC cytokine perturbations, U-Pert predicts unseen responses, captures both molecular and abundance changes, and performs inverse design for target gene-expression programs and cell-type compositions. These results show that cell abundance is an integral component of the perturbation phenotype, providing a mass-aware framework for virtual-cell modeling and perturbation cell fate design.

  bioinformatics2026-07-04v1

• Artificial intelligence virtual cell immune recovery model for screening traditional Chinese medicine ingredients

  Hu, C.; Xiao, B.; Chen, C. Y.-C.

  Abstract

  Screening therapeutic candidates from single-cell transcriptomes requires a target that is closer to treatment response than disease-signature reversal. In immune diseases, post-treatment recovery may follow patient- and lineage-specific trajectories rather than a simple return along the pretreatment disease axis. We developed ImmuneNavi, an artificial intelligence virtual cell (AIVC) immune recovery model for ranking traditional Chinese medicine ingredients from paired PBMC data. The model maps heterogeneous PBMC cohorts to a common healthy immune coordinate system, constructs patient-lineage disease and recovery states, and processes ITCM treated-control profiles into a fixed ingredient perturbation bank. Patient and ingredient states are represented in matched gene, pathway and transcription-factor views, allowing the model to combine local transcriptional direction with more stable program-level features. A matcher trained on one paired treatment cohort preserved recovery-aligned ingredient rankings in independent PBMC cohorts without redefining the feature space, candidate set or preprocessing procedure. ImmuneNavi provides an AIVC model that uses paired immune-state measurements to screen natural-product candidates for experimental follow-up.

  bioinformatics2026-07-03v2

• ECLIPSE: Exploring the dark proteome of ESKAPE pathogens through the sequence similarity network of the Protein Universe Atlas

  Lata, S.; Heinz, D. W.

  Abstract

  The accelerating crisis of antimicrobial resistance among the critical, so-called ESKAPE bacterial pathogens demands the urgent identification of novel molecular targets. However, a substantial fraction of ESKAPE proteomes remains functionally uncharacterized, with many genes annotated as encoding hypothetical proteins. These protein sequences often lack significant similarity to known protein families when using conventional homology-based annotation methods and thus remain "dark". This limits our ability to explore their role in pathogenicity, and it is thus crucial to bridge this substantial gap in pathogen biology by developing novel strategies to illuminate these "dark" regions of the ESKAPE pan-proteomes.We introduce ECLIPSE (ESKAPE Connectome Linkage and Inference for Proteome Sequence Exploration), a network-based computational framework that systematically identifies and prioritises functionally dark protein families in ESKAPE pan-proteomes. ECLIPSE embeds target ESKAPE pathogen proteomes within the global sequence similarity network of the Protein Universe Atlas (Durairaj et al. 2023). It detects connected components composed entirely of unannotated proteins, called the "dark proteome". As a case study, we applied ECLIPSE to a pan-proteome of 3,460,657 protein sequences from 635 strains of Pseudomonas aeruginosa (PA). ECLIPSE identified 120,985 proteins (4%) residing in completely dark connected components. Furthermore, we performed a taxonomic diversity analysis using normalized Shannon indices to characterize each dark component by its enrichment in ESKAPE pathogens. The analysis utilized the evenness (E) value (see Methods 2.1), which distinguishes Pseudomonas-specific (target-specific) from ESKAPE-enriched dark components. We then developed the Dark Proteome Prioritization Score (DPPS), a composite multi-dimensional scoring framework (see Methods 2.5). It ranks these dark components by biological relevance across four orthogonal axes: (i) functional darkness, (ii) P. aeruginosa proportion in the Atlas, (iii) AMR-clade taxonomic restriction, and (iv) conservation across the 635 P. aeruginosa strains. This framework outputs a robust four-tier scoring system; the prioritized Tier I components were validated by weight sensitivity analysis and remained stable across 500 Monte Carlo weight perturbations. Structural characterization of one of the top-ranked ESKAPE-enriched dark component revealed that it belongs to the beta-barrel fold DUF1302 (PF06980) family for which no experimentally solved three-dimensional structure exists in the PDB. The genomic context analysis indicates that it is co-localized with a LuxR-type transcriptional regulator. Collectively, ECLIPSE identifies evolutionarily conserved, structurally defined, and functionally dark proteins enriched across ESKAPE pathogens; these candidates can further facilitate the experimental characterization of dark proteins as an alternative antimicrobial target.

  bioinformatics2026-07-03v2

• ViralEpiBase: a manually curated repository of epitranscriptomic modification sites across viral RNA genomes and virus-encoded transcripts

  Srinivasan, S.; Chande, A.

  Abstract

  Post-transcriptional chemical modifications of RNA, collectively termed the epitranscriptome, have emerged as critical regulatory layers governing viral replication, pathogenicity, and host-virus interactions. Despite the rapid accumulation of experimental data on viral RNA modifications, no dedicated, freely accessible resource existed for systematically cataloguing these sites across diverse viral species. Here we present ViralEpiBase, a manually curated database of epitranscriptomic modification sites identified in viral RNA genomes and virus-encoded transcripts at single-nucleotide resolution. ViralEpiBase currently integrates seven chemically distinct RNA modification types: N6-methyladenosine (m6A), N1-methyladenosine (m1A), pseudouridine ({Psi}), 5-methylcytosine (m5C), 2'-O-methylation (2'OMe), inosine and N4-acetylcytidine (ac4C); across 12 viral species encompassing both DNA and RNA viruses of clinical and biological significance. Each entry is linked to its primary literature source or deposited dataset and is retrievable by modification type, genomic coordinates, or viral taxonomy. The database is freely accessible through an intuitive web interface and is updated continuously as new experimental evidence becomes available. ViralEpiBase thus provides the first unified platform dedicated exclusively to viral epitranscriptomics and is designed to facilitate mechanistic investigation of RNA modification functions in viral biology.

  bioinformatics2026-07-03v1

• Simulating population pangenomes under coalescent demographic models with MSpangenome

  Piat, L.; Denni, S.; Dubois, S.; Linard, B.; Duvaux, L.

  Abstract

  Motivation: Pangenome variation graphs (PVGs) are increasingly used to represent genomic diversity, yet there is currently no general framework for generating population pangenomes directly from explicit evolutionary histories. Existing simulators typically focus on individual classes of variation and do not integrate these variations within a genealogy-aware framework driven by explicit demographic histories. As a result, evaluating pangenome methods in realistic population-genetic settings remains challenging, and benchmark datasets with known evolutionary ground truth are scarce. Results: We present MSpangenome, a genealogy-aware frame- work that bridges coalescent population genetic simulations and pangenome graph analyses. The pipeline combines ancestry simulation with msprime and a de novo graph construction algorithm to generate PVGs directly from simulated genealogies. By explicitly modeling recombination, demographic history and incomplete lineage sorting, MSpangenome produces structurally complex pangenomes in which nested and overlapping structural variants emerge naturally from the underlying genealogies, while their evolutionary history and graph topology remain known by construction. This provides a general framework for generating realistic population pangenomes and establishing ground-truth datasets for methodological evaluation. We demonstrate its utility by generating population-scale pangenomes and using them as controlled references to benchmark the widely used graph construction tools, PGGB and Minigraph-Cactus. Our analyses reveal contrasting performance regimes across levels of sequence diversity, sample sizes and classes of structural variation, highlighting the value of simulation-based benchmarking for identifying reconstruction errors that are hard to detect using empirical datasets alone. Availability and implementation: MSpangenome is imple- mented in Python, fully containerized, freely available at https://forge.inrae.fr/pangepop/MSpangepop and mirrored at https://github.com/inrae/MSpangepop.

  bioinformatics2026-07-03v1

• Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant

  Bhattarrai, P.; Yuan, W.; Chi, H.; Zhou, X. M.; Mallory, X.

  Abstract

  Distinguishing malignant from normal cells in single-cell RNA sequencing data remains a critical yet challenging task in cancer genomics. Existing methods often suffer from poor precision, limited generalizability across cancer types, and reduced robustness across different sequencing platforms. We developed DeepMalignant, an unsupervised multimodal graph attention autoencoder for malignant cell identification that jointly integrates gene expression and copy number alteration (CNA) information. We applied DeepMalignant to five datasets covering 26 samples and four cancer types (breast, colorectal, pancreatic, and ovarian cancers), generated by three platforms (10x Genomics, inDrop, and Drop-seq) for benchmarking and compared it with existing state-of-the-art methods including scMalignantFinder, PreCanCell, CopyKAT, ikarus, and Cancer-Finder. DeepMalignant achieved the best overall balance of precision and recall and consistently outperformed the existing methods that used either gene expression or CNA in F1 scores. Ablation studies showed that both CNA-based edge weighting and graph attention aggregation contribute independently to performance, and attribution analysis further indicated that the learned embeddings capture biologically meaningful malignant programs. We further applied DeepMalignant to two ductal carcinoma in situ (DCIS) samples, DCIS2 and DCIS1, that have matched spatial transcriptomics and scRNA-seq data. DeepMalignant identified tumor-enriched regions that were highly consistent with the matched histological image. The downstream cell-cell communications analysis revealed that fibroblast-derived C3 and MIF both directed signaling more toward normal epithelial cells than tumor epithelial cells, demonstrating that accurate tumor-normal cell classification by DeepMalignant enables biologically meaningful interrogation of the tumor microenvironment and revealing how stromal cells differentially communicate with malignant versus normal epithelial populations.

  bioinformatics2026-07-03v1

• Multiscale Analysis of Cellular Senescence through Ripley's Functions and Functional Statistics.

  Verrier, C.; Dabo-Niang, s.; Dehennaut, V.

  Abstract

  Cellular senescence is a heterogeneous and evolving process involved in development, tissue repair, aging, and age-related diseases. Although senescence burden in tissues has been widely studied, its spatial organization remains poorly understood, particularly in vivo. Senescence encompasses a spectrum of distinct states, with cells differing in molecular signatures, secretory activity, persistence, and interactions with their microenvironment depending on the inducing stimulus and tissue context. This heterogeneity suggests that spatial organization may reflect underlying processes such as tissue repair, regeneration, or maladaptive remodeling, providing insight into senescence function and its pathological roles. Here, we propose a quantitative, multi-scale framework to characterize the spatial organization of senescent cell populations in post-infarction mouse hearts. By combining a senescence-signature scoring strategy with spatial statistical methods and functional data analysis, we assess whether senescent cells exhibit clustered or dispersed patterns, and how these spatial distributions evolve over time following infarction. This approach aims to provide new insights into the spatiotemporal dynamics of senescence in vivo and to identify spatial features that may inform therapeutic strategies targeting age-related and tissue repair-associated pathologies.

  bioinformatics2026-07-03v1

• GLproxScape reconstructs spatial chromatin occupancy landscapes from tiled genomic locus proteomics

  Ozcan, S. C.; Sergi, B.; Yildirim, B.; Cagiral, U.; Gonen, M.; ACILAN AYHAN, C.

  Abstract

  Genomic locus proteomics combines proximity labeling with mass spectrometry to identify the proteins associated with user-defined genomic loci. However, per-region enrichment values from tiledguide designs are typically pooled before hit calling, collapsing the latent spatial structure encodedby overlapping measurements. Here, we describe GLproxScape, an R package that treats per-region enrichments as indirect spatial measurements and reconstructs latent chromatin occupancylandscapes through a Gaussian labeling-kernel forward model. Sequence-specific transcriptionfactors are resolved by motif-anchored non-negative least-squares deconvolution against JASPARor HOCOMOCO position weight matrices, while chromatin regulators which lack defined DNA-binding motifs are inferred as broad occupancy zones, enabling recovery of overlapping membersof multi-subunit complexes. Applied to published genomic locus proteomics datasets at the humanTERT, MYC, FOXP2, and FOXQ1 loci and the mouse Ripk3 locus, GLproxScape recovered knownregulators with predicted positions independently supported by ChIP-Atlas peaks, reconstructedcandidate co-binding relationships, and identified chromatin complexes inaccessible to pooledanalyses. Systematic sgRNA-ablation experiments further showed that densely tiled designsimprove event recovery and positional stability, providing concrete experimental guidance for futuregenomic locus proteomics studies.

  bioinformatics2026-07-03v1

• Multimodal computational framework identifies B cell convergence in autoimmunity and ageing

  Lou, H.; Zhang, M.; Zhang, B.; Lu, Q.; Zheng, J.; Cao, X.

  Abstract

  Identification of the origin of pathogenic immune cells is crucial for therapeutic interventions and diagnosis but pseudotime methods struggle to trace immune cells accurately. Current trajectory inference methods for B cell development and response in health and disease either ignore or underutilize antigen receptor sequence information, limiting their ability to resolve developmental pathways, particularly for pathogenic populations. Widely used methods such as Monocle 3, reconstruct developmental paths from transcriptomic similarity alone, discarding the features from immune receptors. Dandelion has combined the immune receptor features with transcriptomics but it struggles to simulate the trajectory path of B cells. Here we present ClonoTrace, a computational framework that integrates BCR sequence features with transcriptomic trajectory inference through gated fusion of multimodal embeddings. In fetal B cell development and germinal centre development, ClonoTrace achieves higher trajectory inference accuracy than Monocle 3 and Dandelion. Applied to systemic lupus erythematosus, ClonoTrace identifies memory B cell extrafollicular maturation pathway in addition to naive B cell, accompanied by induction of ZEB2 with a concomitant decline of BACH2 along the trajectory, as the alternative origin of pathogenic double negative 2 B cells (DN2) in systemic lupus erythematosus (SLE) patients. In healthy ageing, ClonoTrace identified three pathways from naive, IgM+ memory B cells and switched-memory B cells mature through a DN2-associated transcriptional state that precedes age-associated B cells. ClonoTrace's fate probability algorithm indicated that IgM+memory B cell to ABC transition emerged as the leading candidate age-associated transition, that is a process distinct from SLE DN2 maturation. ClonoTrace provides a generalizable framework for receptor-informed trajectory inference, revealing the developmental pathways of pathogenic B cell populations that are untraceable to single modality approaches in autoimmunity and aging.

  bioinformatics2026-07-03v1

• AART enables fast and accurate cross-platform proteomic translation

  chen, y.; Zhang, S.

  Abstract

  Plasma proteomic profiling has been widely used for biomarker discovery, disease prediction and diagnosis, and patient stratification. However, technical differences across assay platforms often result in low-to-moderate agreement, limiting study reproducibility, data integration, and model transferability. Here we present AART, a cross-platform proteomic translation framework that integrates matched-protein ridge regression with proteome-wide residual learning. We benchmarked AART spanning three independent cohorts profiled using three major platforms, including Olink, SomaScan, and mass spectrometry. Across all six translation directions, AART achieved the best performance compared with baseline methods for both overlapping and non-overlapping protein translations, with a relative improvement of 92.0% on average over direct mapping and by up to 31.6% over cpiVAE, the strongest baseline. Proteins that were accurately translated and improved by AART were enriched for extracellular, vesicle-associated, and tissue-restricted plasma biology. In downstream applications, AART improved the reproducibility of proteomic association analyses relative to direct cross-platform comparison by 75.5% for type 2 diabetes and 370.6% for Alzheimer's disease. AART-enabled cohort integration enhanced diagnostic accuracy for amyotrophic lateral sclerosis by 92.6% compared with non-integration analysis. AART was overall one to three orders of magnitude faster than cpiVAE, facilitating biobank-scale applications. Together, these results establish AART as a fast, accurate, and scalable framework for cross-platform proteomic translation, enabling more reproducible, transferable, and integrated proteomic research.

  bioinformatics2026-07-03v1

• Scalable and rare-variant aware genome inference across the 1kGP cohort

  Ebler, J.; Prodanov, T.; Blair, A.; Lee, S. K.; Ebert, P.; Human Pangenome Reference Consortium, ; Paten, B.; Marschall, T.

  Abstract

  Pangenome graphs built from haplotype-resolved de novo assemblies enable accurate analysis of genetic variation. The short-read-based tool PanGenie efficiently genotypes variants discovered in a pangenome across large cohorts and outperforms linear reference-based methods for structural variants (SVs). However, it cannot detect novel variants absent from the graph, missing many rare SVs (allele frequency <1%) and was limited to graphs with 254 haplotypes. First, we introduce a haplotype sampling step that reduces the number of haplotypes using sample-specific k-mers before genotyping, decreasing runtime twelvefold and memory usage 1.4-fold at 30x coverage. Second, we present a polishing workflow that corrects residual errors in haplotypes inferred from PanGenie genotypes and incorporates rare and private mutations. We genotype 3,202 samples from the 1000 Genomes Project and use low-coverage ONT data (967 samples) for polishing. We achieve a median QV of 46 and provide the 1,934 polished haplotype sequences as a community resource.

  bioinformatics2026-07-03v1

• Disease Stage- and Risk-Associated RNA Editing Signatures in Acute Myeloid Leukemia and Their Utility for Peripheral Blood-Based Assessment

  Gu, T.; Bui, D.; Lee, J.-H.

  Abstract

  RNA editing is a widespread post-transcriptional regulatory mechanism, but its role in acute myeloid leukemia (AML) remains incompletely understood. We analyzed RNA editing in 59 paired diagnosis-relapse AML samples and eight age-matched healthy controls using a stringent discovery pipeline and beta-binomial regression framework accounting for overdispersion and repeated measurements. A total of 166,323 high-confidence RNA editing sites mapping to 5,917 genes were identified. Of tested sites, 1.2%-3.6% varied significantly by disease stage or ELN-2022 risk group. Disease stage-specific editing signatures distinguished healthy controls, diagnosis, and relapse samples, with relapse-associated signals validated in an independent AML cohort. ELN-2022 risk-specific editing signatures showed substantial overlap between intermediate- and adverse-risk groups. Cross-cohort analyses identified four bone marrow (BM) editing sites in TMEM165, COQ4, TIMM17A, and PLXDC2 reproducibly associated with relapse and one peripheral blood (PB) editing site in ABHD18 elevated in higher-risk ELN-2022 groups. Most editing sites were shared between BM and PB; only 2.1%-2.3% exhibited tissue-specific differences. Higher global editing levels were correlated with leukemic state, white blood cell count, and selected clinical features. These findings identify reproducible RNA editing signatures linked to AML disease stage and risk and support the use of RNA editing biomarkers for PB disease assessment.

  bioinformatics2026-07-03v1

• Location dependence of protein intrinsic disorder in Drosophila melanogaster

  Abdulla Daanaa, H. S.; Kuraku, S.; Akashi, H.; Saito, K.

  Abstract

  The relevance of protein structural flexibility in function remains contested, but experimental and computational evidence continues to accumulate. Many efforts to address this investigate intrinsic disorder, which commonly refers to peptide segments or entire protein sequences that presumably lack structure and exhibit high flexibility/conformational heterogeneity under physiological conditions. These efforts face challenges such as conflicting computational predictions and ambiguous relationships among intrinsic disorder locations and other protein properties. We address these challenges at a genome-wide scale in Drosophila melanogaster using residue-level predictions for various protein properties. We employ single and consensus approaches to quantify the prevalence of intrinsic disorder and attempt to infer function by testing for differences along protein sequences. Intrinsic disorder is likely more common at terminals than internal regions, and amino acid frequencies can vary substantially between regions in a manner that plausibly reflects functions of intrinsic disorder, rather than only proteome-wide effects. Tertiary structure potentially underlies the prevalence of intrinsic disorder along protein sequences; this prevalence varies more in a putatively solvent-exposed context than a solvent-buried one. Protein-binding appears to be a main function of intrinsic disorder, and we find support consistent with the notion that structural flexibility fosters binding plasticity, and show that location and protein length are factors in this relationship. Nucleic acid-binding and linker are ostensibly less common disorder functions than protein-binding, but nucleic acid-binding seems more localized at terminals. Residue-level estimates of selection pressure indicate that disordered regions generally evolve under weaker sequence constraints than structured regions, except at the N-terminal region. Biases in disorder prediction are a considerable factor in many of the observations, but unlikely a full explanation. The findings strengthen support for functional relevance of flexibility, offer insight into protein architecture and function, and lend impetus for experimental inquiry.

  bioinformatics2026-07-03v1

• Raw-count embeddings improve single-cell foundation models

  Schlede, S.; Muruganandan, T. P.; Gojjam Kantharaju, S.; Kisis, I.; Boecker, M.; Kim Alves Carpinteiro, M.; Schmitz, A.; Buchwald, L. M.; Sakthivelu, V.; Gülcüler Balta, G. S.; Anstötz, M.; Rueger, M. A.; Thomas, R. K.; Beleggia, F.

  Abstract

  Single-cell transformer foundation models have grown to hundreds of millions of parameters, yet the preprocessing choices that underlie them, including gene ranking and library-size normalisation, have not been systematically benchmarked. Testing seven strategies, we find these elaborations are largely unnecessary: non-normalised, log-transformed counts give the best performance, and gene order barely matters, with even random ordering outperforming sophisticated rank-based schemes. The resulting model, Gene Intelligence, projects log1p-transformed raw counts directly onto each token embedding and jointly predicts masked tokens and counts, using no normalisation, positional encoding, or read-depth tokens. Despite this simplicity, it achieves state-of-the-art performance in the tested gene-level tasks and in doublet detection, and matches large current foundation models on cell-classification tasks while using 10- to 200-fold fewer parameters.

  bioinformatics2026-07-03v1

• GenPerturb: sequence-grounded interpretation of perturbation transcriptomes using pretrained genomic models

  Nikaido, I.; Shiihashi, T.

  Abstract

  Background: Perturb-seq captures transcriptional responses to thousands of genetic and chemical perturbations, but does not directly resolve the cis-regulatory elements or transcription factor motifs underlying those responses. Existing approaches rely on indirect post hoc analyses or external epigenomic annotations, making it difficult to connect gene-level responses to specific regulatory element Results: We present GenPerturb, a framework that leverages pretrained sequence-to-expression models to link perturbation-induced expression changes to candidate cis-regulatory elements. By contrasting perturbation and control states, GenPerturb prioritizes regulatory regions and transcription factor motifs associated with each perturbation. The model recapitulates perturbation-dependent gene expression patterns and enables sequence-level interpretation without requiring matched chromatin data. Across multiple perturbation types, GenPerturb identifies biologically meaningful regulatory programs, including lineage-specific and signaling-associated motif activities, even when corresponding transcription factor expression changes are limited. Conclusions: GenPerturb converts gene-level expression responses from Perturb-seq into perturbation-specific, sequence-grounded cis-regulatory hypotheses. By prioritizing candidate regulatory elements and transcription factor motifs responsive to each perturbation without requiring matched chromatin data, GenPerturb enables mechanistic interpretation of transcriptional regulation and guides downstream experimental validation.

  bioinformatics2026-07-03v1

• Replication fork directionality reveals how structural variants arise under replication stress

  Glodzik, D.; Rigby, M.; Andreopoulos, M.; Crawford, J.; Ehmsen, S.; Tapinos, A.; Cornish, A.; Houlston, R.; Wedge, D. C.; Scully, R.; Park, P. J.

  Abstract

  Structural variants (SVs) in cancer are associated with defects in DNA repair and replication stress, but the mechanisms generating common SV types remain unresolved. We propose that large (>100 kb) tandem duplications originate through a novel sister-fork breakage-fusion mechanism. To capture replication-related context beyond breakpoints, we developed an algorithm to characterize replication timing, origin density, and fork direction across SV-spanned regions, features that refine and differentiate previously defined SV signatures. Large tandem duplications frequently overlap replication origins from which forks proceed bidirectionally; combined with independent evidence from APOBEC strand asymmetry, this pattern is compatible uniquely with the proposed mechanism. Although tandem duplications in CCNE1-amplified and CDK12-mutant cancers also concentrate around origins and highly transcribed genes, they display distinct contexts: CDK12-mutant SVs arise near later-firing origins, whereas those in CCNE1--amplified tumors often coincide with genes in specific strand configurations, suggesting different causes of fork stalling. Incorporating replication features into signature analysis enabled the discovery of new SV signatures, which we used to build SVIG, a multi-class classifier of SV phenotypes. SV signatures attributed to replication stress may help guide therapies targeting this vulnerability.

  bioinformatics2026-07-03v1

• RD-OMICS: An Integrative Multi-Omics Data Inventory in Rare Diseases

  Sun, S.; Wang, H.; Mathe, E. A.; Zhu, Q.

  Abstract

  Rare diseases (RD) impact over 30 million individuals in the United States, yet fewer than 5% of the identified conditions have FDA-approved treatments. Progress in RD research is hindered by small patient cohorts, biological heterogeneity, and the fragmented, inconsistently annotated publicly available omics data, which limits integrative analysis and translational discovery. Here, we present RD-OMICS, a data inventory with integrated and structured RD omics data from Gene Expression Omnibus (GEO), in the form of a knowledge graph. We developed a metadata harmonization pipeline that combines rule-based mapping and large language model (LLM)-assisted semantic categorization. The graph-based data model was defined to integrate different types of data including disease conditions, experiments, samples, platforms, projects, and publications into a centralized inventory graph. In this preliminary study, 11,049 GEO series for 126 rare diseases were processed and integrated into RD-OMICS, which includes 375,930 individual biospecimen samples, 1,578 sequencing and array platforms, 10,938 biological projects. Case studies demonstrate the use of RD-OMICS in supporting rare disease research, omics cohort construction, and transcriptome-based drug repurposing for amyotrophic lateral sclerosis (ALS). RD-OMICS provides a scalable foundation for transforming fragmented omics data into a structured, harmonized and interoperable resource, facilitating therapeutic development and other translational discoveries in rare diseases.

  bioinformatics2026-07-03v1

• Structural Organization of the Nvj3-Mdm1 Complex Reveals a Conserved Lipid-Compatible Contact Site Module

  Aboumourad, M.; Hariri, H.

  Abstract

  Membrane contact sites are organized by protein assemblies that physically couple organelles and coordinate lipid metabolism, yet the structural principles that enable lipid exchange across these junctions remain poorly defined. At the nuclear-vacuolar junction (NVJ) in budding yeast, the tethering protein Mdm1 and its binding partner Nvj3 form a complex that regulates lipid metabolic pathways, but the structural features underlying their interaction have not been resolved. Here, we use AlphaFold-based complex prediction and comparative structural analysis to define the organization of Nvj3-Mdm1 complex assembly. We identify a high-confidence heterodimer in which conserved PXA and PXC domains generate an extended tunnel spanning both proteins. Tunnel analysis predicts a core hydrophobic conduit traversing the Nvj3-Mdm1 interface, consistent with a lipid-compatible architecture. Evolutionary conservation is enriched at the Nvj3-Mdm1 interface. The predicted conduit shares geometric and physicochemical properties with bridge-like lipid transfer proteins, including Atg2, Fmp27, and Hob2, suggesting that heteromeric tether assemblies may contribute directly to inter-organelle lipid transfer. Cophylogenetic analysis reveals coordinated coevolution of Nvj3 and Mdm1 across Saccharomycetes. Together, these findings define Nvj3 as a structural partner of Mdm1 and support a conduit-based model of lipid transfer at the NVJ.

  bioinformatics2026-07-03v1

• RegulomeXplorer: Interactive exploration of drug effects on subcellularly resolved proteomes

  Uiberacker, M.; Iellici, T.; Afanaseva, E.; Meier-Menches, S.; Zanghellini, J.

  Abstract

  Mass spectrometry-based proteomics allows the quantification of drug-induced changes in protein abundance. However, the integration of perturbation data across subcellular compartments remains a challenging bottleneck. Here, we present RegulomeXplorer, a web-based tool for automated processing and interactive exploration of subcellular compartment-resolved proteomics data. RegulomeXplorer employs MaxQuant output files to determine differential protein regulations upon drug perturbation, performs functional enrichment analysis, and visualizes enriched terms on a two-dimensional cytoplasmic-nuclear plane, called regulome. The data visualization by means of regulomes allows to simultaneously assess the magnitude of drug perturbation effects within separate subcellular compartments as well as the contribution of regulated proteins to the position of each enriched term in the regulome plane. We validated RegulomeXplorer against previously published, manually curated regulome analyses. It was then applied on subcellular compartment resolved breast cancer cell line proteomes, revealing drug- and cell-line-specific responses to Doxorubicin and Taxol, both in line with their described mode of action. RegulomeXplorer provides an accessible workflow for interpreting compartment-resolved perturbation proteomics and generating mode of action hypotheses in drug-response studies. RegulomeXplorer is freely available without registration at https://chemnettools.anc.univie.ac.at/RegulomeExplorer/.

  bioinformatics2026-07-03v1

• Segmentation and classification of retinal pigment granules in fluorescence lifetime imaging microscopy (FLIM) data

  Ali, M.; Ahmad, H. A.; Alderzy, H.; Hammer, M.; Heintzmann, R.; Stranik, O.

  Abstract

  Alterations of fluorescence properties in retinal pigment epithelium (RPE) cells caused by diseases such as age-related macular degeneration (AMD) highlight the need for detailed analysis of the fluorescent RPE granules at the individual level. Precise segmentation and classification of these granules remain challenging due to their limited visual separability. In this study, we present Classi4RPE, a computational algorithm designed to accurately segment RPE granules and classify them into three categories -- lipofuscin (L), melanolipofuscin (ML), and melanin (M) -- based on fluorescence lifetime imaging data, which provide distinctive contrast. The method is implemented in a custom Python framework and employs seeded watershed segmentation to isolate individual granules. Lipofuscin granules are identified as hyperfluorescent structures with longer lifetimes, while granules with shorter lifetimes are further analyzed based on their spatial lifetime distribution from the center to edge, enabling discrimination of ML from other melanin-rich granules. Our approach achieves high performance, with mean sensitivities of 0.99 for L granules and 0.90 for ML granules, and corresponding specificities of 0.93 and 0.98, respectively, compared to manually annotated ground truth. These results demonstrate the potential of Classi4RPE to surpass human visual limitations and provide a robust tool for quantitative RPE analysis.

  bioinformatics2026-07-03v1

• Quantifying Asymmetric Coevolutionary Dynamics using Normalized Phylogenetic Costs

  Wagle, S.; Markin, A.; Sherman, T. J.; Mayo, C.; Dunham, T. J.; Brelsfoard, C.; Cohnstaedt, L. W.; Wilson, W. C.; Anderson, T. K.; Eulenstein, O.

  Abstract

  Coevolutionary studies aim to characterize associations, such as virus-host relationships, by using phylogenetic distances to quantify the topological concordance between the phylogenies of interacting taxa. However, phylogenetic distances cannot capture asymmetrical relationships that arise from differences in sampling, evolutionary rates, or characterizations between datasets. Furthermore, a lack of accurate normalization complicates the interpretation and validation of coevolutionary analyses. To address these limitations, we employed the Asymmetric Cluster Affinity and Cluster Support costs as a general framework to quantify coevolutionary patterns across multiple biological scales. We benchmarked the precision of these costs by reanalyzing a curated dataset documenting interspecies transmission frequencies across nineteen virus-host phylogenies. Our results corroborate prior findings showing that all virus families under study can cross species boundaries; however, the asymmetric costs provide a more granular representation, demonstrating that the frequency of such events varies significantly across families. We then applied the Asymmetric Cluster Support cost to quantify preferential gene segment pairings within the Bluetongue virus genome. This analysis revealed a close phylogenetic association between the outer capsid proteins VP2 and VP5, likely reflecting shared selective pressures due to their critical roles in cell entry and exit. In contrast, gene segments encoding nonstructural proteins exhibited discordant evolutionary histories relative to other segments. Finally, we demonstrated that the Asymmetric Cluster Support cost can detect coevolutionary dynamics in swine influenza A virus, identifying novel gene pairings indicative of major viral reassortment events. Overall, our approach demonstrates that normalized asymmetric phylogenetic costs accurately capture complex biological relationships and provide a robust framework for quantifying fine-scale coevolutionary dynamics in rapidly evolving pathogens.

  bioinformatics2026-07-03v1

• SpatialFuser: a unified framework for integrative analysis of unpaired spatial multi-omics data

  Cai, W.; Li, W.

  Abstract

  Recent advances in spatial multi-omics technologies provide unprecedented opportunities to interpret molecular features in tissue microenvironments, but integrative analysis across heterogeneous datasets remains challenging. Here we present SpatialFuser, a deep learning framework for integrative analysis of unpaired spatial multi-omics data across epigenomics, transcriptomics, proteomics, and metabolomics. SpatialFuser consists of three coordinated modules: MCGATE, a Multi-head Collaborative Graph Attention auToEncoder that learns multi-scale spatial representations to decipher fine-grained spatial heterogeneity beyond predefined spatial neighbourhoods; an optional geometric pre-matching module that provides coarse initialization under tissue geometry mismatch; and an iterative matching-fusion module that couples geometry-constrained optimal transport matching with contrastive-learning-guided modality fusion for cross-slice alignment and integration. Systematic benchmarks demonstrate superior performance and reliability compared with existing state-of-the-art methods in spatial domain identification, cross-slice alignment, and multi-omics integration. Applications to real datasets illustrate that SpatialFuser resolves precise spatial molecular patterns, reveals developmental dynamics, and recovers complementary signals across modalities. Cross-resolution integration of weakly correlated modalities by our method further uncovers previously obscured biological variation. The generalizability and versatility of our framework enable customized analytical scenarios and potential extension for emerging omics.

  bioinformatics2026-07-02v3

• Computational Binding Affinities of Disheveled PDZ Protein-Ligand Complexes

  Singh, A.; Jubintoro, A.; Kancharla, H.; Blankenberg, P.; Zheng, J.

  Abstract

  Wnt/B-catenin signaling is critical for cell growth and development, with its hyperactive dysregulation implicated in the development of cancer. Current therapeutic research on inhibition of Wnt/B-catenin signaling is impeded by the high cost of experimentally determining binding affinities. Consequently, interest has risen in screening potential inhibitors binding affinities with computational tools to reduce costs. Here, we test the validity of a computational molecular dynamics simulator, Binding Free Energy Estimator 2 (BFEE2), for determining peptide ligand affinity for Wnt/B-catenin signaling. We focus on the Dishevelled (DVL) PDZ domain, a key mediator in WNT signaling through its ability to bind to various peptide ligands. We analyze the binding affinities of several DVL PDZ domain-peptide and domain-ligand complexes against previously established results to determine the validity of computational analysis. We conclude that computational molecular dynamics simulations were successful for peptide-ligand complexes with mixed results for small-molecule scenarios.

  bioinformatics2026-07-02v3

• Mechanisms Matter: Transportability of Cellular Perturbation Effects

  Qi, S.-a.; Chapfuwa, P.

  Abstract

  Predicting cellular responses to genetic or chemical perturbations across biological contexts is central to drug development and disease understanding. Despite increases in data and model scale, deep learning models have not consistently outperformed simple baselines. Leveraging causal transportability theory, we show that cross-context generalization is governed by shared causal mechanisms, not merely distributional similarity. To enable controlled evaluation, we develop a causal simulator that generates realistic semi-synthetic Perturb-seq datasets with tunable mechanistic divergence, providing benchmarks with known ground-truth causal structure. Further, we adapt the Vendi diversity score to the perturbation setting as a diagnostic for mode collapse, a failure mode invisible to standard per-perturbation metrics. Extensive experiments across four deep learning models and six simple baselines on semi-synthetic and real Perturb-seq datasets reveal a cross-context generalization gap: performance under cross-context splits drops substantially, often to simple baseline levels. Notably, even on synthetic data with fully specified causal structure, no model generalized across contexts with different causal mechanisms. These results underscore the need for cross-context evaluation, diversity-aware metrics, and mechanistically grounded inductive biases.

  bioinformatics2026-07-02v2

• Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales

  Palla, G.; Hillsley, A.; Kim, Y.-J.; Royer, L. A.

  Abstract

  Predicting how cells respond to genetic and chemical perturbations is a central challenge in drug discovery and functional genomics. A growing ecosystem of specialized single-cell foundation models has been developed to address this problem, yet their practical advantage over domain-agnostic approaches remains unclear. Here we evaluate the power of Tabular Foundation Models such as TabICL and TabPFN, general-purpose pre-trained regression models, against domain-specific architectures including PRESAGE, scGPT, scLAMBDA, STACK and Prophet across four complementary evaluation settings: cell-level in-context cross-cell-type prediction, pseudobulk perturbation prediction on five Perturb-seq datasets of cell-lines, a genome-wide CRISPR screen in primary human CD4+ T cells, and embryo-level cell-type composition prediction in a zebrafish developmental perturbation atlas. In the cell-level cross-cell type perturbation prediction, Tabular Foundation Models perform on par or better than specialized models. On pseudobulk perturbation prediction, Tabular Foundation Models consistently outperform specialized baselines across multiple evaluation metrics and datasets. On whole-emrbryo cell-type composition prediction, Tabular Foundation Models are competitive with specialized baselines. These results demonstrate that general-purpose tabular in-context learning provides a strong and scalable alternative to bespoke biological architectures for perturbation response modeling across cell systems and scales.

  bioinformatics2026-07-02v2

• Evidence for post-allopolyploidy genetic exchanges between duplicated regions in three ancient polyploidies

  Dhillon, A. K.; Pasagadugula, H.; Pitts, I.; Rohilla, M.; Conant, G. C.

  Abstract

  Many successful lineages, including flowering plants and vertebrates, owe some of their evolutionary prosperity to whole genome duplications (WGD). However, in the immediate aftermath of a WGD, the new polyploid species that is formed often experiences multivalent pairings during meiosis, which can produce inviable gametes. To mitigate the potential harm caused by such pairings, most lineages eventually undergo "diploidization" to restore typical bivalent pairing. A key component of this process is the loss of duplicated genes. While diploidization was once thought to be rapid, recent analyses of polyploidies suggest the process may be more drawn out, with multivalent pairing persisting long after the initial WGD event. Here, we assess evidence for "late" diploidization after three different polyploidies: the teleost genome duplication (TGD), nested polyploidies in Paramecium lineages, and the ancient WGD in bakers yeast. Using our tool POInT (the Polyploidy Orthology Inference Tool), we model the resolution of these events. By analyzing discordance between expected species trees and observed gene trees, we argue that late diploidization was a likely feature in the resolution of all three polyploidies.

  bioinformatics2026-07-02v2

• ProLoc: Text-guided Localization of Protein Functional Regions

  Liu, P.; Fan, J.; Pan, M.; Zhang, J.

  Abstract

  MotivationProtein function is often mediated by specific sequence regions, such as domains, motifs and functional sites. Identifying these regions is important for understanding protein mechanisms, annotating newly sequenced proteins and prioritizing residues for experimental validation. However, existing protein function prediction and protein-text models mainly capture global protein-level associations, making it difficult to determine which residues support a given textual functional description. This limits their use for mechanistic interpretation and residue-level experimental prioritization. ResultsWe introduce text-guided protein functional region localization, a span-level grounding task that identifies residue regions corresponding to natural-language functional descriptions. We construct an InterPro-derived localization benchmark of explicit protein-text-region examples, covering both domain-level and functional-site annotations with sequence-similarity-aware splits and a unified span-level evaluation protocol. We further propose ProLoc, a text-conditioned localization model built on raw ESM2-650M and PubMedBERT with direct residue-level localization and anchor-free span proposal generation. On the held-out test set, ProLoc substantially outperforms window-based adaptations of representative protein and protein-text models. Its direct output achieves the strongest single-region localization performance, reaching 0.7730 IoU@1, while its anchor-free proposal output improves visible multi-site recovery, reaching 0.9671 VM R@10 IoU50 and 0.9489 VM All-Hit@50. Availability and ImplementationSource code and evaluation scripts are available at https://github.com/ShiDeng7rz/Proloc. The processed benchmark and data splits are archived at Zenodo: https://doi.org/10.5281/zenodo.20729714. Contactliupeishuo@nju.edu.cn

  bioinformatics2026-07-02v2

• BacNeMu: neutral mutation spectra reconstruction pipeline for bacteria

  Skudnov, A.; Badamshin, E.; Efimenko, B.; Popadin, K.; Gunbin, K.; Denisov, S.

  Abstract

  The mutational spectrum is an increasingly important molecular phenotype that quantitatively describes mutagenesis in a given gene and species, enabling future comparative analyses to reveal differences in underlying mutagenic processes, whether internal, such as DNA repair processes, or external, such as ecological niches and conditions. Mutation accumulation experiments, although time-consuming and costly, remain the standard approach for reconstructing bacterial neutral mutation spectra. Here, we present BacNeMu, a phylogenetically informed pipeline that reconstructs neutral mutational spectra of bacterial genomes using open databases GTDB, AnnoTree and KEGG Orthology, building on previously developed NeMu pipeline. BacNeMu reconstructs mutation spectra that closely match mutation accumulation experiments results while requiring substantially less time, enabling comparative analyses across diverse bacterial taxa. Applied to obligate aerobes and anaerobes, BacNeMu recovered the expected excess of T:A>C:G transitions, consistent with oxidative-damage-associated mutational patterns previously described in mitochondrial genomes and yeast single-strand. We further asked if any other ecologic factors influence a mutational spectrum. As a pilot we compared three species living under different temperatures: one strong thermophile - Thermotoga maritima, one psychrophile - Clostridium algidicarnis, and one with intermediate temperature tolerance - Psychrobacter sanguinis. In the thermophile, the relative frequency of T:A>C:G substitutions was higher than in the psychrophile, consistent with the hypothesis that GC-biased mutagenesis contributes to thermal adaptation, although C:G>T:A transitions predominate across all three species. BacNeMu provides a rapid, phylogenetically informed framework for generating biologically meaningful mutation spectra from open databases.

  bioinformatics2026-07-02v1

• Knowledge-guided Bayesian optimization using pre-trained LLMs speeds up the identification of superior genotypes from germplasm collection

  Hamazaki, K.; Tsuda, K.

  Abstract

  Background: Germplasm collections contain wide genetic diversity that is valuable for plant breeding, but conducting phenotypic evaluation for all genotypes in field trials is rarely feasible. Bayesian optimization offers a way to decide, season by season, which genotypes to cultivate in order to identify superior genotypes with fewer evaluations. However, standard Bayesian optimization commonly starts from randomly selected genotypes and mainly relies on surrogate models built from marker genotype information, while the text-based passport information that accompanies germplasm is not fully used. We examined whether pre-trained large language models can provide prior knowledge that improves these decisions in germplasm evaluation. Results: We constructed a large-language-model-guided Bayesian optimization framework that introduces large language models into two parts of the Bayesian optimization workflow. In zero-shot warmstarting, a large language model proposes initial genotypes using passport information such as cultivar name, country of origin, and subpopulation, optionally together with principal component scores derived from genome-wide single-nucleotide-polymorphism markers. In addition, we evaluated a large-language-model-based surrogate model that predicts phenotypic values for untested genotypes using in-context learning from previously evaluated genotypes. Using a rice germplasm panel and two target traits (seed number per panicle for maximization and protein content for minimization), we compared strategies. For seed number per panicle, zero-shot warmstarting with a general-purpose instruction-following model reduced the number of evaluated genotypes needed to reach the best genotype, whereas improvements were small for protein content. When genomic information was available, Gaussian-process-based Bayesian optimization was the strongest overall approach, while the large-language-model-based surrogate model outperformed random baselines and was competitive in some settings. When genomic information was not available, predictions based on passport information improved efficiency compared with fully random strategies. Conclusions: Pre-trained large language models can inject useful agronomic knowledge into Bayesian optimization for germplasm evaluation, particularly by improving early-stage genotype selection, and can also support optimization when genomic information is unavailable. As models better handle long genomic sequences together with passport information, large-language-model-guided Bayesian optimization may become a practical and explainable decision-support approach for agricultural optimization.

  bioinformatics2026-07-02v1

• Bridging Gene Expression and Morphology: A Cell Size Score and Its Applications Across Multiple Diseases and Physiological Contexts

  Ji, X.; Cui, Q.

  Abstract

  Cell size is a critical morphological parameter determining cellular functional homeostasis, yet existing large-scale transcriptomic databases lack direct cell size measurement data. By integrating high-resolution immunofluorescence images with transcriptomics, we identified 457 genes significantly correlated with cell area. Based on these findings, we developed an algorithm, Cell Size Score (CSS), to predict cell size from gene expression profiles. Validation across multiple independent datasets, including human cell lines, mouse models, and single-cell spatial transcriptomics, confirmed that CSS accurately predicts cell size. Furthermore, we observed a significant positive correlation between CSS and broad-spectrum chemotherapy drug resistance, suggesting that increased cell volume confers survival advantages to cancer cells. Moreover, CSS analysis of aging revealed sex-dependent, tissue-specific patterns of change, wherein male adipose and cardiac tissues exhibited progressive hypertrophy with age, while female reproductive organs showed significant atrophy. Additionally, CSS significantly increased in skeletal muscle after exercise, indicating that this metric can capture dynamic physiological adaptation processes. This study establishes a bridge between transcriptomics and cell morphology, providing novel insights into retrospectively analyzing the role of cell size in pathological and physiological processes such as cancer and aging using existing omics data, as well as understanding the molecular mechanisms underlying cell size regulation.

  bioinformatics2026-07-02v1

• Permute-match tests detect significant correlations between time series despite nonstationarity and limited replicates

  Yuan, A. E.; Shou, W.

  Abstract

  Researchers frequently analyze correlations between pairs of time series by determining whether an observed correlation is stronger than expected under the null hypothesis of independence. However, the time series are often nonstationary, with statistical properties that change over time, thereby making standard tests invalid. If sufficient replicates exist, a trial-swapping permutation test can be performed that handles nonstationarity by comparing within-replicate correlations to between-replicate correlations. Although largely assumption-free, this test is fundamentally limited by the number of replicates (n) because its minimum p-value is 1/n!. With n=3, this minimum is 1/6, rendering thresholds like 0.05 unattainable. This limits its use considerably in animal experiments, where n may be as low as 3. We propose permute-match tests -- modified permutation tests that can report lower p-values of 2/nn or 1/nn under strong evidence of dependence. Permute-match tests guarantee a false positive rate at or below the significance level when replicates are independent and identically distributed. The bound of 1/nn is not gratuitously conservative, since it cannot be further lowered without additional assumptions. We demonstrate our approach using synthetic data and apply it to an existing dataset with 3 independent groups of zebrafish, confirming the observation that zebrafish swim faster when directionally aligned.

  bioinformatics2026-07-01v5

• MORPH Predicts the Single-Cell Outcome of Genetic Perturbations Across Conditions and Data Modalities

  He, C.; Zhang, J.; Dahleh, M. A.; Uhler, C.

  Abstract

  Modeling cellular responses to genetic perturbations is a significant challenge in computational biology. Measuring all gene perturbations and their combinations across cell types and conditions is experimentally challenging, highlighting the need for predictive models that generalize across data types to support this task. Here we present MORPH, a MOdular framework for predicting Responses to Perturbational cHanges. MORPH combines a discrepancy-based variational autoencoder with an attention mechanism to predict cellular responses to unseen perturbations. It supports both single-cell transcriptomics and imaging outputs and can generalize to unseen perturbations, combinations of perturbations, and perturbations in new cellular contexts. The attention-based framework enables inference of gene interactions and regulatory networks, while the learned gene embeddings can guide the design of informative perturbations, as demonstrated in two applications. Overall, MORPH is a flexible tool for optimizing perturbation experiments, enabling efficient exploration of the perturbation space to advance understanding of cellular programs for fundamental research and therapeutic applications.

  bioinformatics2026-07-01v2

• Age-related erosion of X chromosome inactivation in human tissues

  Rocca, C.; Gylemo, B.; Edwards, M.; Cing, Z.; Gibbs, J. R.; Nestor, C. E.; DeCasien, A. R.

  Abstract

  Age-related diseases often show sex differences, yet their molecular bases remain unclear. Animal models suggest that age-related disruption of X-chromosome inactivation (XCI) occurs in female mice. We test whether this phenomenon extends to humans using bulk and single-cell datasets. We find that age-dependent escape from XCI also occurs in human females, particularly among genes at the distal ends of the X-chromosome and those involved in genome stability. These findings provide preliminary evidence that XCI erosion represents a human female-specific aging process.

  bioinformatics2026-07-01v2

• Generative design of antigen-specific T-cell receptor sequences with a conditional diffusion model

  Zhang, Y.; Liang, W.; Xu, S.; Witney, M.; Su, X.; Andrews, M. C.; Rossjohn, J.; Purcell, A. W.; Wang, F.; Song, J.

  Abstract

  T cell receptor (TCR)-based immunotherapy holds immense potential for treating cancers, autoimmunity, and infectious diseases, where antigen-specific TCR recognition is crucial for adaptive immune responses. Engineering or de novo generation of the complementarity-determining region 3 (CDR3) loops of TCRs using artificial intelligence offers a powerful alternative to designing antigen-specific TCRs rather than laborious experimental screening. However, current in silico approaches are constrained by weak conditional guidance, limited flexibility, and a lack of rigorous functional validation. To address these limitations, we introduce TCRDiff, a generative diffusion framework for designing antigen-specific TCRs conditioned on peptide-MHC (pMHC) targets and germline-encoded TCR variable genes. By leveraging pre-trained knowledge from massive T-cell repertoires and TCR-pMHC recognition data, TCRDiff generates CDR3{beta} sequences that closely resemble native-binding TCRs via a denoising diffusion process. Furthermore, incorporating interface geometry features generated TCR-pMHC complexes with superior structural plausibility than models relying solely on sequence-based diffusion or structure-based modeling. As a proof of concept, we deployed TCRDiff in a systematic pipeline to design candidate TCRs against a clinically validated cancer antigen. In vitro activation assays validated that TCRDiff-generated TCRs efficiently recognize the MAGE-A3 epitope with minimal off-target reactivity. Thus, TCRDiff establishes a powerful, validated computational paradigm to accelerate the development of TCR-based immunotherapies.

  bioinformatics2026-07-01v2

• Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales

  Palla, G.; Hillsley, A.; Kim, Y.-J.; Royer, L. A.

  Abstract

  Predicting how cells respond to genetic and chemical perturbations is a central challenge in drug discovery and functional genomics. A growing ecosystem of specialized single-cell foundation models has been developed to address this problem, yet their practical advantage over domain-agnostic approaches remains unclear. Here we evaluate the power of Tabular Foundation Models such as TabICL and TabPFN, general-purpose pre-trained regression models, against domain-specific architectures including PRESAGE, scGPT, scLAMBDA, STACK and Prophet across four complementary evaluation settings: cell-level in-context cross-cell-type prediction, pseudobulk perturbation prediction on five Perturb-seq datasets of cell-lines, a genome-wide CRISPR screen in primary human CD4+ T cells, and embryo-level cell-type composition prediction in a zebrafish developmental perturbation atlas. In the cell-level cross-cell type perturbation prediction, Tabular Foundation Models perform on par or better than specialized models. On pseudobulk perturbation prediction, Tabular Foundation Models consistently outperform specialized baselines across multiple evaluation metrics and datasets. On whole-emrbryo cell-type composition prediction, Tabular Foundation Models are competitive with specialized baselines. These results demonstrate that general-purpose tabular in-context learning provides a strong and scalable alternative to bespoke biological architectures for perturbation response modeling across cell systems and scales.

  bioinformatics2026-07-01v1