Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• Design of a Multi-epitope Vaccine Against Human Glanders Targeting Outer Membrane β-barrel Proteins of Burkholderia mallei

  Kapoor, J.; Panda, A.; Kumar, S.; Bandyopadhyay, A.

  Abstract

  Burkholderia mallei, a facultative intracellular Gram-negative pathogen, is the causative agent of glanders that primarily affects solipeds and sporadically transmitted to humans. Current interventions mainly rely on antibiotics; however, increasing resistance and the lack of a licensed vaccine further complicate disease management. In the present study, a consensus-based computational framework was employed on the B. mallei turkey2 proteome. Total 59 proteins - including porins, TonB receptors, autotransporters, and efflux components - were identified as surface exposed outer membrane {beta}-barrel (OMBB) proteins that were used to design a multi-epitope vaccine (MEV) construct. B- and T-cell epitopes were predicted from 59 proteins, and ten epitopes each of cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell were chosen based on their antigenicity, non-allergenicity, non-toxicity, surface accessibility, and conservation across 32 B. mallei strains. The MEV was included with suitable adjuvants at the N-terminus to enhance its immunogenicity. The 780 amino acid MEV construct was predicted to be antigenic, and soluble upon overexpression with 62.69% random coils, while the rest formed -helices and {beta}-strands. The tertiary structure of the MEV was generated and subsequently validated, indicating good structural quality. Molecular docking of the MEV with toll-like receptor 4 (TLR4) demonstrated strong affinity, and molecular dynamics simulation confirmed the structural stability of the MEV-TLR4 complex. In-silico immune simulation showed the capability of MEV to induce a strong immune response. Codon optimization and in-silico cloning were performed for efficient protein expression in the E. coli host. The study proposes an MEV construct by utilizing surface exposed OMBB proteins which directly interact with the host and serve as effective immunogenic targets against B. mallei infection.

  bioinformatics2026-07-08v2

• Modeling patient tissues at molecular resolution with Eva

  Liu, Y.; Sharma, R.; Bieniosek, M.; Kang, A.; Wu, E.; Chou, P.; Li, I.; Rahim, M.; Bauer, E.; Ji, R.; Duan, W.; Qian, L.; Luo, R.; Sharma, P.; Dhanasekaran, R.; Schürch, C. M.; Charville, G.; Mayer, A.; Zou, J.; Trevino, A. E.; Wu, Z.

  Abstract

  Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.

  bioinformatics2026-07-08v2

• Decoding UTRs by applying explainable AI to a genomic foundation model

  Brase, L.; Creamer, D. R.; Shapovalova, Y.; Ashe, M. P.; Ashe, H. L.; Rattray, M.

  Abstract

  Background The regulation of mRNA decay and translation is crucial for cellular function and development; however, the complex interplay of RNA-binding proteins (RBPs) regulating these processes remains incompletely understood. Recent advances in genomic foundation models present new opportunities for decoding the regulatory grammar embedded within mRNA untranslated regions (UTRs). Here, we leverage explainable artificial intelligence to systematically identify RBP motifs that influence translation and mRNA decay during Drosophila melanogaster development. Results We extended the training of GENA-LM Fly, a genomic foundation model, on all 5' and 3' UTR pairs from the D. melanogaster genome. We separately fine-tuned it using ribosome density (RD) and mRNA decay (half-life) data from the embryonic maternal-to-zygotic transition (MZT). Using SHapley Additive exPlanations (SHAP) analysis, we identified the sequence regions most influential for prediction and performed motif enrichment analysis to discover associated RBP binding sites. We identified 42 unique RBPs associated with increasing (n=23; e.g., Rnp4f, Mxt) or decreasing (n=19; e.g., Aret/Bruno, Rox8, Sxl, Orb2) RD and 18 unique RBPs associated with increasing (n=6) and decreasing (n=12; e.g., Cnot4, Rbp9, Rox8) mRNA half-life. Using publicly available PAR-CLIP data, we validated our Orb2 signal in a Drosophila cell line. Furthermore, feature ablation and shuffling experiments revealed the contributions of different sequence components to model performance. Our approach significantly outperformed naive high-versus-low RD comparisons, demonstrating the power of model explainability in biological discovery. Conclusions This study demonstrates that genomic foundation models, when combined with explainability methods, can discover meaningful biology even without drastically improving the underlying prediction accuracy. The identified RBP motifs provide new insights into post-transcriptional regulatory elements that govern RD and decay during early development.

  bioinformatics2026-07-08v2

• A Spatio-Temporal Analysis Framework for Characterizing Radiation-Induced Genomic Instability

  Chopra, K.; Cucinell, C.; Titov, M.; Weinberg, R.; Forrester, S.; Kilic, O.; Zhu, Y.; Turilli, M.; Jha, S.; Schabacker, D. S.; Brettin, T.; Yoon, B.-J.

  Abstract

  Chronic low-dose ionizing radiation induces complex genomic instability encompassing both structural variants and point mutations, yet these alterations are typically analyzed as independent events limiting detection of mechanistic coupling between rearrangement formation and localized mutagenesis at breakpoint junctions. This gap is particularly consequential given the widespread occupational and environmental exposure contexts; nuclear energy, medical imaging, and environmental contamination, where coupled genomic alterations may contribute to cancer risk through mechanisms invisible to type-agnostic analyses. We developed an integrated analytical framework combining temporal pattern tracking, breakpoint-proximal mutation enrichment analysis, and systematic testing across all structural variant types to resolve these coupled dynamics across dose and time. Applying this framework to whole-genome sequencing data from primary human endothelial cells (HUVEC) exposed to chronic low-dose gamma radiation (0.001 - 2 mGy/hr) over three weeks, we discovered 7.13-fold enrichment of doublet base substitutions (DBS) within 10bp of inversion breakpoints, a signal absent from other structural variant types. This enrichment decayed sharply with distance (to [~]1.9 fold at 100bp), indicating localized mutagenesis at these junctions. Temporal analysis revealed divergent fates: inversions appeared transiently (100% single-timepoint) while DBS showed greater persistence (9.0% multi-timepoint). Among the INV-DBS events identified, affected genes include 16 high-constraint loci (pLI [≥] 0.9) involved in DNA damage response, signal transduction, and chromatin regulation; pathways critical for maintaining genomic stability. Our framework provides a generalizable approach for investigating structural variant-mutation relationships, with applications to radiation biology, cancer genomics, and mechanistic studies of DNA repair fidelity.

  bioinformatics2026-07-08v2

• An Integrated Knowledge Graph and Network Medicine Pipeline for Drug Repurposing: Benchmarking Across Human Diseases and Application to Amyotrophic Lateral Sclerosis

  Jiang, A.; Hu, J.; Abdulle, Y.; Pain, O.; Iacoangeli, A.

  Abstract

  Drug repurposing offers a practical strategy to identify new therapeutic uses for approved drugs, potentially reducing the time and cost associated with conventional drug development. We present a novel three-stage drug repurposing pipeline that integrates knowledge graph-based gene prediction, network-based drug-disease association analysis, and systematic classification of candidate drugs by therapeutic class. The pipeline integrates DGLinker to predict novel disease-associated genes, SAveRUNNER to identify drug repurposing candidates, and ATC Category Enrichment Analysis (ATCEA) to prioritise candidates by pharmacological class. We benchmarked the pipeline across twelve diseases using DrugBank and MEDI2-HPS as validation resources. Utilising DGLinker-expanded disease-gene sets as input increased the number of predicted repurposed drugs, while overall discriminative performance remained stable across diseases (AUROC 0.71-0.77). Application of ATCEA consistently improved precision, F1-score, and specificity, while reducing recall, reflecting a conservative prioritisation strategy that contracts the candidate space while retaining pharmacologically coherent drug-disease candidates. We further applied the pipeline to amyotrophic lateral sclerosis (ALS), a neurodegenerative disease with limited therapeutic options, and performed a deeper literature-based validation of the results. Incorporation of DGLinker-predicted genes substantially increased the number of significant candidate drugs and uncovered enriched ATC categories not identified using known ALS genes alone, including antidepressants and antipsychotics. Moreover, several drugs with supporting evidence available in the literature were identified only when DGLinker-predicted genes were used. Overall, 77 candidate drugs were prioritised within significantly enriched ATC categories, several of which are supported by previously published studies. To provide exploratory real-world support for these findings, we further evaluated candidate drugs in a longitudinal electronic health record (EHR) dataset of 2361 patients with ALS from King's College Hospital. Although the number of evaluable drugs was limited due to sample size, the EHR analysis provided additional clinically relevant context for selected prioritised drugs and pharmacological classes. Our pipeline demonstrates potential to accelerate drug repurposing by integrating complementary computational approaches to each step of the process, providing an end-to-end framework that showed robust performance across benchmarking experiments and use cases.

  bioinformatics2026-07-08v1

• Gene regulatory co-expression networks decipher potential lncRNA-miRNA-mRNA interactions modulating transcription regulation in neurodegeneration

  Venkatesan, A.; Sinha, P.; Basak, J.; Bahadur, R.

  Abstract

  Neurodegenerative diseases are complex disorders characterised by progressive neuronal loss and widespread transcriptomic dysregulation; however, the coordinated interactions among coding and non-coding RNAs that contribute to disease progression remain incompletely understood. In this study, RNA-seq datasets from disease-relevant neuronal populations and brain regions representing Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) were analysed using an integrative network-based framework. Differential expression analysis coupled with weighted gene co-expression network analysis identified modules significantly correlated with disease and prioritised highly connected hub genes. Integration of these hub genes with curated RNA interaction database enabled the construction of candidate lncRNA-miRNA-mRNA regulatory networks. Functional enrichment analysis revealed Gene Ontology biological processes associated with synaptic signalling, mitochondrial function, RNA metabolism and neuroinflammatory responses across neurodegenerative conditions. The inferred regulatory networks suggested both disease-specific and shared post-transcriptional regulatory modules involving key hub genes and non-coding RNAs. Additionally, putative sequence variants were identified within untranslated regions of selected hub genes, suggesting potential alterations in miRNA-mediated regulations. Therefore, this study provides a systems-level view of transcriptomic dysregulation across major neurodegenerative diseases and identifies candidate regulatory interactions and molecular targets for future functional investigation

  bioinformatics2026-07-08v1

• A systematic analysis of machine learning pipelines for robust antimicrobial resistance prediction

  Aselstyne, A.; Karthik, E. N.; El Azami, M.; Pogorelcnik, R.; Fournier, Q.; Chandar, S.

  Abstract

  Motivation: Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-genome sequencing data is an essential tool for accelerating clinical decision-making and mitigating resistance spread. Although many previous works have explored the use of tree-based machine learning (ML) models to predict resistance, the field lacks a systematic evaluation of the training pipeline across a variety of pathogenic species and antibiotics. Results: Using nine clinically relevant species-antibiotic combinations from the NCBI antimicrobial susceptibility testing database, we present a detailed analysis of the ML pipeline and identify key factors affecting model performance and evaluation. We begin by relabelling all isolates using current CLSI minimum inhibitory concentration breakpoints to resolve inconsistencies and increase available data, resulting in up to a 19% label swap and 56% data enlargement per species-antibiotic combination. We identify several key training parameters including k-mer length, which can increase classification F1 scores by over 20 points compared to commonly used k-values, feature matrix truncation, which can induce polynomial time reductions with limited performance reduction, and ML model class. By comparing 5-fold cross-validation with evaluation on an unseen clinical dataset, we show that random cross-validation splits--often criticized as overly optimistic--can act as a strong proxy for downstream clinical performance, yielding closer F1 scores than phylogeny-aware splits in all cases. We finally present an interpretability study which shows that over 95% of k-mers used by our models are associated with identifiable genomic features. Our results highlight the importance of feature design, evaluation protocol, and biological analysis in genomic AMR prediction, and support tree-based models as a robust and interpretable method.

  bioinformatics2026-07-08v1

• CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction

  Yan, F.; Cao, X.; Mao, F.; You, Z.; Chen, Y.; Du, Z.; Huang, Y.-A.

  Abstract

  Intratumoral heterogeneity drives cancer recurrence and metastasis, yet single-cell drug response prediction faces severe "cross-domain" challenges, such as applying in vitro models to in vivo tissues or inferring metastatic resistance from primary tumors. These scenarios trigger distribution shifts arising from heterogeneous sequencing platforms, distinct tissue microenvironments, and metastatic evolution - problems rarely addressed by existing methods. We introduce CSGDA, a cell state-guided graph domain adaptation framework designed to predict drug responses across these biological heterogeneities. CSGDA incorporates biological priors to map gene expression into functional cell states, guiding a structure learning module to construct robust cell topology. To conquer distribution shifts, the model employs graph domain adaptation combined with a novel overlap penalty mechanism. Extensive benchmarks on five scRNA-seq datasets demonstrate that CSGDA outperforms state-of-the-art methods, achieving an average gain of ~6% in ACC and AUPR. Beyond prediction accuracy, we employed integrated gradients to effectively pinpoint key genes involved in drug resistance within a challenging cross-metastasis cisplatin dataset. These findings underscore CSGDA's superior performance in single-cell drug response prediction and its potential in resolving single-cell heterogeneity, paving the way for precision medicine.

  bioinformatics2026-07-08v1

• Beyond infinite sites: Generalized ABBA-BABA statistic for deeper phylogenies

  Zhang, C.; Nielsen, R.

  Abstract

  The Patterson's D statistic detects gene flow from ABBA-BABA site patterns, but its biallelic site patterns fail under deeper divergences where multiple hits cause false positives. We propose two extensions, D+ and D*. Both incorporate multiallelic site patterns to reduce saturation bias under JC and F84 model. Simulations show that D+ and D* both remain correctly null under all conditions and detect gene flow effectively, with distinct advantages: D+ guarantees non-negativity of the denominator, while D* provides greater robustness when mutation rates vary across genomic regions. The source code and binary files are publicly available at https://github.com/chaoszhang/ASTER.

  bioinformatics2026-07-08v1

• Residual Multi-Modal Learning for Pan-Breast-Cancer Drug Response Prediction

  Huang, B.; Tasaka, L.; Li, J.; Islam, T.; Zhang, S.

  Abstract

  Predicting drug sensitivity across diverse cancer cell lines remains a fundamental challenge in precision oncology, particularly for data-scarce cell lines where per-cell-line models overfit and lookup-table approaches cannot generalise to unseen biological contexts. We present DL4DR, a Two Tower Residual Late Fusion deep learning model that addresses this challenge through content-based, identity-free genomic conditioning. The Cell Line Tower encodes each cell line as a 3 x 139 x 139 genomic image - encoding gene expression, mutation severity, and copy-number variation as RGB channels - using a convolutional encoder that maps directly from biological content, never from a cell line ID. The Compound Tower combines three complementary molecular representations: D-MPNN graph message passing, ORNN octave convolutional image features, and an ECFP hard-memorization head that preserves activity-cliff resolution. Predictions are composed as a residual sum: f = fhard + {lambda}(zc). fresidual, where the learned gate $\lambda$ modulates how much interaction signal supplements the memorization baseline. Evaluated across 51 breast cancer cell lines(136,342 records), Residual Fusion outperforms the ECFP-Only baseline in 48/51 cell lines (94.1%), with {Delta}R2 > 0.02 in 26/51 (51.0%). On the leave-cell-line-out split - the decisive test of genomic generalisation - the mean {Delta} R2 = 0.016 across all 51 lines demonstrates that the genomic encoder learns transferable biological signal beyond cell line identity. External validation on 601 cell lines across 27 cancer tissue types (CellTiter-Glo dataset; 0 cell line overlap with training) achieves median R2 = 0.627, within the range of the internal random-split performance (R2 = 0.61--0.69), confirming pan-cancer generalisation. GradCAM interpretability on the Cell Line Tower recovers TP53 among the top-five cross-cell-line genomic activators (5/51 cell lines) alongside several uncharacterised candidate genes (e.g.FSIP2, 6/51) - without any prior pathway annotation - providing partial biological validation of the learned representation, while also indicating that a substantial share of the encoder's top-ranked signal corresponds to genes with no current annotation as breast cancer drivers. Code and data are available at https://github.com/bayjuan5/DL4DR.

  bioinformatics2026-07-08v1

• Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture

  Yang, A. J.; Tan, C.; Ma, Y.

  Abstract

  Recent advances in spatially resolved transcriptomics (SRT) enabled measurement of sets of pathway genes activity within tissues. However, existing gene set activity scoring methods overlook spatial dependencies among tissue locations, restricting their ability to capture region-specific pathway activities associated with disease pathology or cellular communication. Moreover, these methods lack significance-level inference for activity scores, provide limited interpretability of gene-level contribution to a pathway, and scale poorly to advanced large-size SRT datasets. To address these limitations, we present GESSO (Gene sEt activity Score analysis with Spatial lOcation), a spatially informed gene set scoring method adaptable to diverse SRT platforms. GESSO models gene set activity levels through a graph-regularized matrix decomposition algorithm, jointly inferring spatially coherent gene set activity scores (GASs) and interpretable metagene weights that capture gene-level contributions. It further implements a permutation-based local significance test and a stratified low-resolution approximation that scales to high-resolution SRT datasets such as Visium HD, Stereo-seq, and Xenium Prime. Across 13 datasets from five SRT platforms, GESSO outperformed all existing methods in accuracy, calibration, interpretability, and scalability. Applications revealed novel biological programs, including spatially confined EMT activation within tumor-stroma interfaces, developmental signaling gradients across embryonic tissues, and coordinated B-cell, T-cell, and signaling pathways within germinal centers of human lymph node tissue, revealing the spatial organization of immune function at subregional resolution.

  bioinformatics2026-07-08v1

• FEATMAP: Targeted Correction of Acquisition Signatures Harmonizes Medical Foundation Model Embeddings and Enables Robust Task Generalization

  Donle, L.; Phillips, M.; Gaber, F.; Ramesh, S.; Sacco, M.; Hautaniemi, S.; Virtanen, A.; Bressem, K.; Adams, L.; Goon, K.; Nevins, E.; Robinett, R. A.; Kochanny, S.; Hassan, S.; Dolezal, J.; Pearson, A. T.; Lengyel, E.

  Abstract

  Medical foundation models compress biomedical data into embeddings that support diverse downstream clinical tasks. However, successful model deployment is hampered by performance degradation on external data. It is recognized that embeddings capture acquisition signatures, such as hardware and technical differences, in addition to biology. Effective harmonization must remove the acquisition signature while preserving biological signals, a trade-off that current methods fail to balance adequately. Input-level normalization fails to eliminate acquisition signatures from embeddings, whereas embedding-level methods adjust features in an untargeted manner. We present FEATMAP, a harmonization approach that models acquisition signatures as geometric distortions between manifolds of similarly arranged embeddings. Using paired data that isolate the effect of acquisition signatures, FEATMAP fits a single global affine transformation per foundation model to correct acquisition signatures directly in the embedding space. This targeted, reusable correction aims to preserve biological and demographic variation while harmonizing across acquisition signatures. Across scanner and foundation-model harmonization in digital pathology and field-strength harmonization in brain MRI, FEATMAP improves cross-condition embedding similarity, reduces performance gaps without retraining, and suggests potential for the alignment of disparate embedding spaces.

  bioinformatics2026-07-08v1

• PredHLM: quantitative and interpretable prediction of metabolic half-life in human liver microsomes

  Jang, J.; Cho, N.-C.; Oh, K.-S.

  Abstract

  Motivation: Human liver microsome (HLM)-based metabolic stability assays are fundamental in early drug discovery, shaping pharmacokinetic profiles and oral bioavailability. However, these experimental assays are labor-intensive and time-consuming, limiting their application in large-scale virtual screening. Computational models can prioritize compounds at scale, yet most are classification-based, leaving quantitative and interpretable prediction of HLM half-life limited. Results: In this study, we developed a quantitative machine learning model for the direct prediction of HLM half-life (T1/2) by integrating 11,790 compounds combining in-house and curated public data. Among various combinations of molecular features and learning algorithms, the XGBoost model with RDKit 2D descriptors achieved the best predictive performance, with an RMSE of 0.507 and an R2 of 0.431 on an independent test set. Shapley Additive Explanations (SHAP) analysis identified lipophilicity and known metabolic soft-spot features as the primary contributors to the predictions. These results suggest that this quantitative approach provides a practical framework for defining metabolic stability margins, thereby supporting rapid Go/No-go decisions in preclinical drug discovery. Availability: The source code, data, and trained model are available at https://github.com/joshua-416/PredHLM.

  bioinformatics2026-07-08v1

• Immunoinformatics-Guided Design and In Silico Evaluation of a Multi-Epitope Vaccine Against Influenza A H10N5 and H3N2 Strains Based on Hemagglutinin and Neuraminidase Proteins

  Shabbir, M. Z.; Kumar, P.; Rehman, M. A. U.; Kumar, J.; Urooj, U.; Batool, S. I.; Sourav, C.; Ghazanfar, R.; Nagari, Z.; Hameed, D.; Wahid, A.; Atique, A.; Siddique, M. D.

  Abstract

  Influenza A viruses H3N2 and H10N5 represent, respectively, a persistently dominant seasonal pathogen and a newly documented zoonotic threat with the latter strain variants responsible for the first confirmed human fatality in January 2024, yet no vaccine platform currently addresses co-protection against both subtypes within a unified immunogen. We report here the immunoinformatics based vaccine design and multi-layered computational validation of a 419-amino-acid multi-epitope subunit vaccine construct targeting conserved hemagglutinin (HA) and neuraminidase (NA) antigens identified through multiple sequence alignment of the avian H10N5 (A/swine/Hubei/10/2008) and H3N2 human reference strain sequences to identify viral agents undergoing mammalian adaptations. Linear B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were predicted using ABCpred, BCEpred, BepiPred 2.0, NetMHCpan 2.1, and NetMHCpan 4.0, then filtered through VaxiJen 3.0, AllerTOP v2.1, and ToxinPred to retain only antigenic, non-allergenic, non-toxic candidates. The final construct, incorporating an avian {beta}-defensin N-terminal adjuvant with GPGPG, AAY, and EAAAK linkers, exhibited a molecular weight of 43.9 kDa, instability index of 31.15, and SOLPro solubility probability of 0.763. Tertiary structure modeling via I-TASSER and GalaxyRefine achieved 84.4% Ramachandran-favored residues. Molecular docking against TLR3 and TLR7 yielded binding free energies of -16.1 and -16.8 kcal/mol with picomolar dissociation constants. Molecular dynamics simulations confirmed complex stability over extended trajectories. Furthermore, codon optimization produced a Codon Adaptation Index of 1.0 for E. coli K12 expression. In silico immune simulation demonstrated robust activation of humoral and cellular immunity including elevated IgG1, IgM, IFN-{gamma}, IL-2, rapid NK cell expansion, and broad B-cell clonal diversity. These findings establish a computationally validated candidate capable of providing protection against influenza in multiple host organisms, warranting experimental advancement.

  bioinformatics2026-07-08v1

• Navigating the pangenome coordinate system with Shredtools

  Shivakumar, V. S.; Langmead, B.

  Abstract

  Existing notions of pangenome coordinates rely on hard-to-compute multiple sequence alignments. On the other hand, pangenome-wide exact unique matches (multi-MUMs) can be computed efficiently, and represent conserved stretches of columns in the underlying MSA. We introduce Shredtools, which uses multi-MUMs as pangenome waypoints and allows for sophisticated queries in pangenome coordinates. Its primary query is extract, which takes an interval of one sequence and extracts the smallest window containing it that is syntenic pangenome-wide. Shredtools' extract query can extract a gene region from 476 human genomes in half a second. Other queries help to refine these results, by finding local exact matches to improve the density of multi-MUM coverage ("enhance") and by selectively discarding sequences to improve the precision of the syntenic region ("zoom"). The Shredtools web interface (available at https://vikshiv.github.io/shredtools) allows for client-side handling of extract queries with index queries handled via simple and fast HTTP Range requests, simplifying usage and enabling pangenome-scale discoveries.

  bioinformatics2026-07-08v1

• AllTheBacteria: a community resource empowers biology and discovers novel peptide antibiotics

  Hunt, M.; Torres, M. D. T.; Alikhan, N.-F.; Anderson, D.; Andreani, M. L.; Blom, J.; Bouras, G.; Brinkman, F.; Carroll, L. M.; Croxen, M. A.; Floto, A.; Hall, M. B.; Hawkey, J.; Horsfield, S. T.; Jia, B.; Lacey, J. A.; Lee, H.-S.; Lima, L.; MacAlasdair, N.; Mallawaarachchi, S.; Matlock, W.; Moustafa, A. M.; Petit, R.; Raghuram, V.; Ramnath, V.; Russell, M. J.; Sanderson, T.; Saratto, T.; Schwengers, O.; Seemann, T.; Shaw, L. P.; Shen, W.; Thomson, N.; Tonkin-Hill, G.; Toussaint, J.; Viet, T. L.; Wachsmann, J. v.; Wan, F.; Weimann, A.; Wheatley, R. M.; Wiatrak, M.; Xie, O.; Fuente-Nunez, C. d.

  Abstract

  Public microbial genomes encode an immense record of biological diversity, evolution and molecular function, but much of this information remains difficult to reuse because raw sequencing data are not uniformly assembled, quality controlled, annotated or searchable at scale. Here we present AllTheBacteria, an open, community-built resource that transforms public bacterial short-read whole-genome sequencing reads into a uniformly processed discovery platform. The current analysed release contains 2,440,377 high-quality bacterial and archaeal genomes from 11,273 species, together with standardized taxonomic assignments, genome annotations, antimicrobial resistance calls, antiphage-defence annotations, protein structure predictions and AI-ready sequence tables. We show that this infrastructure enables applications that would otherwise be impractical, from global sequence search and outbreak contextualization to pangenome method development, antimicrobial resistance reservoir mapping and antiphage-defence ecology. As a stringent experimental demonstration, we mined 3,919,096 encrypted peptide fragments from AllTheBacteria proteomes using our deep learning model APEX 1.1, identifying 1,867 candidates with predicted antimicrobial activity. We synthesized 24 representative peptides and tested them against 20 clinically relevant bacterial strains, including antibiotic-resistant pathogens. Multiple peptides showed low-micromolar activity, membrane-responsive conformational transitions and selective envelope perturbation. A lead molecule, ATB20, reduced Acinetobacter baumannii burden in a murine skin abscess model with efficacy comparable to polymyxin B and no overt toxicity. Together, these results establish AllTheBacteria as both a foundational community resource for microbiology and a renewable engine for AI-guided antimicrobial discovery.

  bioinformatics2026-07-07v8

• The RdRp Thumb-1 Pocket is a Conserved Target for Broad-Spectrum Antiviral Development

  Woods, V.; Umansky, T.; Russell, S. M.; Gallay, P.; Smith, D.; Haders, D.

  Abstract

  Single-stranded RNA (ssRNA) viruses cause human diseases ranging from mild colds to deadly pandemics. Broad-spectrum non-nucleoside antivirals have been characterized as impossible to develop because allosteric binding sites are poorly conserved. The Thumb-1 allosteric site identified in HCV's RNA-dependent RNA polymerase (RdRp) governs an essential conformational change in the {Lambda}1-loop required for polymerase initiation. The only approved Thumb-1 inhibitor, beclabuvir, has been shown to be inactive against a broad panel of non-HCV viruses, including poliovirus, rhinovirus, coronavirus, coxsackievirus, influenzavirus, and HIV. It subsequently failed to inhibit SARS-CoV-2 despite favorable docking predictions. A conserved, homologous allosteric site on RdRp that spans multiple viral families has not been reported. Here, we demonstrate that the Thumb-1 pocket and its associated {Lambda}1-loop are conserved across ssRNA viral families through comparative structural analysis and multiple sequence alignments. We demonstrate that beclabuvir's dependence on its indole C6 carbonyl to interact with the HCV-specific residue R503 and its C3 cyclohexyl chemistry restricts its activity to HCV. We validate the target discovery with MDL-001, which does not contain a C6 carbonyl or a C3 cycloalkyl substituent. MDL-001 directly blocks viral RNA synthesis in isolated replication complexes and selects for the canonical Thumb-1 resistance mutation P495S in HCV. MDL-001 demonstrates broad-spectrum in vitro inhibition of both HCV and SARS-CoV-2. Preclinical proof of concept and development of MDL-001 across HCV, HBV, HDV, influenza, SARS-CoV-2, and RSV are reported in a companion manuscript. These findings establish RdRp Thumb-1 as a conserved allosteric pocket and a druggable target for broad-spectrum antiviral development.

  bioinformatics2026-07-07v6

• PHI: A Galaxy-based workflow for reproducible prophage-host interaction analysis and standardized viral-genomics reporting

  Saraiva, J. P.; Borim Correa, F.; Bernt, M.; Ghanem, N.; Nieto, E.; Brizola Toscan, R.; Y. Wick, L.; Chatzinotas, A.

  Abstract

  Background: Viruses that infect bacteria, known as bacteriophages or phages, are widespread in nature and play important roles in shaping microbial communities and ecosystem functions. Some phages can integrate into bacterial genomes as "prophages", where they may influence the biology of their host by carrying genes that affect metabolism, virulence, or environmental adaptation. Despite their importance, studying prophages and their interactions with bacterial hosts remains challenging because it typically requires combining many complex computational tools and can be resource-intensive. Results: In this study, we introduce the Prophage-Host Interaction Toolkit (PHI), a user-friendly and automated workflow available through the Galaxy platform. PHI brings together multiple established tools into a single, reproducible pipeline that identifies candidate prophages, evaluates their quality, predicts host relationships, and characterizes key functional genes. Importantly, all results are summarized in an interactive report that simplifies interpretation. When applied to a mock community composed of 22 bacteria as a workflow demonstration, PHI detected 41 prophages across 14 hosts, classifying them into high- and medium-quality phage genomes. Host assemblies exhibited > 99 % completeness and < 1 % contamination for most genomes, while DefenseFinder revealed between 3 and 24 antiviral systems per genome. Conclusions: By removing installation barriers and consolidating the outputs of multiple established tools, PHI lowers the barrier to advanced phage analysis, enabling both specialists and non-experts to explore phage-host interactions and their implications in areas such as microbiome research, biotechnology, and environmental science.

  bioinformatics2026-07-07v2

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

  Jain, S.; Mehta, N. K.; Raina, S.; Kumar, P.; Varun, V.; 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/).

  bioinformatics2026-07-07v2

• Real-time mass defect-driven prediction of glycopeptide precursors enables enrichment-free serum glycoproteomics

  Zhang, B.; Chau, T. H.; Bienes, K. M.; Arakawa, H.; Kaji, H.; Kawahara, R.; Ashwood, C.; Matsui, Y.; Thaysen-Andersen, M.

  Abstract

  Glycopeptide enrichment remains a cornerstone in glycoproteomics, but bias and reproducibility issues continue to hinder biological insight and clinical translation. Using curated glycoproteomics datasets and machine learning, we trained a glycopeptide classifier to promptly recognize N-glycopeptide precursor ions in peptide mixtures through mass defect signatures. Integration of the classifier into a data-dependent acquisition framework facilitated efficient and unbiased real-time prediction of N-glycopeptides directly from serum opening avenues for enrichment-free glycoproteomics.

  bioinformatics2026-07-07v2

• FAMUS: A Few-Shot Learning Framework for Large-Scale Protein Annotation

  Shur, G.; Burstein, D.

  Abstract

  Predicting gene function is a pivotal and challenging step in genomic and metagenomic data analysis. Current automatic annotation tools typically rely on the single most similar sequence from the query database and struggle to robustly set hit thresholds for annotation. The sparsity of proteins per annotation makes it challenging to confidently assign gene function for underrepresented families. Here, we present a contrastive learning framework for functional annotation. FAMUS (Functional Annotation Method Using Supervised contrastive learning) compares query sequences to a full array of profile Hidden Markov Models and transforms the similarity scores into a condensed vector space that minimizes the distance of proteins from the same family. The similarity scores of a query to all profiles are used for its representation instead of considering only the top-ranking hit. Unannotated sequences are incorporated as negative examples during training, enabling robust detection of proteins that fall outside the scope of the reference database without requiring a user-defined threshold. Using this approach, FAMUS outperformed KEGGs native KofamScan for KEGG Orthology annotation and InterPros InterProScan for PANTHER family annotation. We thus created four protein annotation models using protein families from the KEGG Orthology, InterPro family, OrthoDB, and EggNOG databases. All four models are available as a conda package and via our user-friendly web server, allowing users to annotate large-scale datasets. FAMUS is the first comprehensive and modular annotation framework based on contrastive learning. It supports both pre-defined and user-specific databases for tailored annotation, and can be easily integrated into any genomic and metagenomic analysis pipeline to facilitate accurate, large-scale functional annotation.

  bioinformatics2026-07-07v2

• Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets

  Brown, S. M.; Cohen, A. B.; Dean, S. N.

  Abstract

  Proteins are highly diverse functional polymers where the specific sequence of amino acids, selected from a standard genetically-encoded alphabet of twenty (C20), determines the structure and ultimately the function of the resulting folded protein. This standard alphabet has been identified to be non-randomly distributed in physicochemical properties crucial to both structure-formation and function, often referred to as coverage theory. While machine learning models have drastically improved protein structure prediction, success of protein design models lags structure prediction, particularly for custom secondary structure motifs and amino acid alphabets. Here we therefore bridge contemporary biological theory with recent advancements in artificial intelligence (AI) to develop and evaluate a generative AI protein design model, trained on hundreds of thousands of proteins within the RSCB PDB, for custom secondary structure motifs using reduced amino acid alphabets (RAAs). Results indicate an overall success in designing novel proteins with desired secondary structure motifs for a broad range of amino acid alphabets and complexity of designs. Interestingly, this tool often captures the full three-dimensional tertiary structure of a target protein despite training only on physicochemical sequence space and secondary structure information. The development of this model advances research across multiple disciplines, from general scientific AI architecture development to protein design for biotechnology, astrobiology, and early-Earth evolutionary biology.

  bioinformatics2026-07-07v2

• PLANET-MD: Ultra-fast Proteome-scale Prediction of Allosteric Networks in Proteins

  Sledzieski, S.; Hanson, S. M.

  Abstract

  Proteins are dynamic molecules that depend on conformational flexibility to carry out functions in the cell, yet despite significant advances in the modeling of static protein structure, prediction of these dynamics remains challenging. We introduce PLANET-MD, a machine learning model that predicts dynamic protein properties from sequence or static structure with unprecedented speed and accuracy. Trained on thousands of molecular dynamics trajectories spanning diverse protein families, PLANET-MD simultaneously models multiple dynamics features: root-mean-square fluctuations (RMSF), generalized correlation coefficients (GCC-LMI), and a novel structural heterogeneity profile (SHP) based on recent structure quantization methods. PLANET-MD significantly outperforms existing methods in predicting simulation-derived dynamics. We reduce RMSF prediction error by 57% compared to BioEmu and calibrated Dyna-1 predictions, including an up to 73% error reduction for long proteins. We validate these predictions with experimental hetNOE data, and we demonstrate the ability to adapt predictions to different physical temperatures. We highlight PLANET-MD's utility in constructing allosteric networks in the oncogene KRAS and identify structural sub-modules with correlated motions, and we validate PLANET-MD by showing that changes in node centrality within predicted KRAS allosteric networks correlate with changes of folding free energy in experimental DMS data. Our approach makes predictions in seconds rather than hours or days, enabling us to perform the first comprehensive dynamics analysis of the entire human proteome. PLANET-MD bridges the gap between static structural biology and dynamic functional understanding, enabling dynamics-aware structural analysis and variant effect prediction at scales previously unavailable. PLANET-MD is available as free and open-source software at https://github.com/flatironinstitute/PLANET-MD.

  bioinformatics2026-07-07v2

• Identifying intervention strategies from machine learning models with COALA: a counterfactual optimization framework

  Han, B.; Duan, Q.; Hu, T.

  Abstract

  Motivation: Machine learning models in biomedicine have become increasingly complex, often functioning as black boxes. However, understanding contributors to disease and making actionable health interventions requires interpretable models. Common explainable AI methods like SHAP focus on feature importance but fall short in explaining why features contribute in certain patterns or what interventions to take. Counterfactual explanations address this by proposing "what if" scenarios but current tools focus on individual predictions and fail to generalize complex trends. Results: We introduce the framework Counterfactual Optimization for Actionable interpretabiLity in AI (COALA). COALA interprets models by identifying optimal counterfactuals across user-defined mutable feature subsets and constraining remaining features to reveal how constraint features determine what interventions are optimal. By analyzing counterfactual profiles of features rather than individual features, COALA reveals holistic patterns. Using synthetic and real datasets, COALA reveals simple and complex model trends and provides more intuitive, multi-feature interventions than SHAP. Availability and Implementation: Code for COALA implementation, synthetic data, models trained on synthetic data, and code to replicate results and figures are available at https://github.com/brt-solo/COALA.

  bioinformatics2026-07-07v2

• LINKER-Pred: A Deep Learning Method and Web-Server for the Prediction of Disordered Flexible Linkers in Proteins

  Meng, D.; Garcia Alvarez, H. M.; Glavina, J.; Leonetti, C. O.; Pollastri, G.; Chemes, L. B.

  Abstract

  Disordered Flexible Linkers (DFLs) are unstructured regions that play critical roles in inter-domain communication and multivalent protein interactions. Despite their biological significance, the accurate identification of DFLs remains challenging due to limited experimental annotations and sparsity of dedicated prediction tools. Here we introduce LINKER-Pred, a publicly available web server featuring two convolutional neural network-based predictors trained on a novel large-scale dataset of linkers connecting folded domains (DLD dataset) and DisProt linkers. LINKER-Pred2 combines ProtTrans and MSA-Transformer embeddings within an ensemble CNN framework, achieving state-of-the-art performance on CAID2 and CAID3 benchmarks. LINKER-Pred-Lite excludes MSA-based features, improving speed while maintaining competitive predictive accuracy. LINKER-Pred predictors offer robust residue-level DFL predictions directly from sequence, providing a scalable solution for DFL annotation across proteomes. The LINKER-Pred web server and associated resources are freely available at https://linkerpred.chemeslab.org/, offering the research community an accessible tool for studying protein disorder and modularity.

  bioinformatics2026-07-07v2

• SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling

  Bowman, M.; Bandopadhyay, R.; Singh, V.; Telpoukhovskaia, M.; Vander Velde, R.; Shaffer, S. M.; Trowbridge, J. J.; Bowman, R. L.

  Abstract

  Single cell RNA-seq (scRNA) has provided unprecedented resolution into cellular and clonal heterogeneity. Computational approaches have enabled recovery of differentiation dynamics, yet current approaches do not evaluate discontinuous differentiation processes present in malignant leukemia. To address these gaps, we developed SupeRJump: a jump-drift-diffusion based supervised cell-fate model (https://github.com/namwob44/SupeRJump/). We deploy this approach in human bone marrow, murine aging hematopoiesis, and lentivirally barcoded mouse models of acute myeloid leukemia. Our framework introduces a semi-supervised pseudotime strategy to fit a jump-drift-diffusion model and batch correction for lineage fate predictions from absorbing Markov chains. We introduce metrics to quantify cell skewness toward particular lineages, transitions through intermediate progenitor states toward terminally differentiated states, and discontinuous transition dynamics. We use these metrics to identify cells preferentially biased for differentiation, their underlying transcriptional networks, and gene programs responsible for differentiation discontinuity.

  bioinformatics2026-07-07v1

• Molecular Clock Dating of Ancient Environmental DNA Reveals Damage Beyond Deamination

  Lemmon-Kishi, M.; Pipes, L.; De Sanctis, B.; Nielsen, R.

  Abstract

  Ancient environmental DNA (aeDNA) from permafrost, lake, cave, and marine sediments provides a rich source of genetic data that captures broad perspectives of past biodiversity. Accurate dating is crucial for discovering ecologically relevant patterns from aeDNA, and molecular clock dating would allow for sample ages to be estimated from the recovered genetic material itself instead of the geological components. However, the fragmented and damaged nature of short-read ancient DNA (aDNA) from multiple taxonomic sources poses significant challenges and has limited this dating approach for aeDNA. Here we developed ratePlacer, a phylogeny-based method for analyzing aeDNA that can combine information from many short reads in a sample while accounting for DNA damage to provide maximum likelihood estimates of sample ages. Simulations demonstrate that ratePlacer accurately dates samples even under the fragmented, damaged conditions characteristic of aeDNA and outperforms Bayesian tip-dating approaches for taxonomically mixed samples commonly found in aeDNA. Yet age estimates from re-dating Kap Kobenhavn varied across taxa, highlighting the difficulty of molecular clock dating in aeDNA. This dating also revealed elevated G[-&gt;]T and C[-&gt;]A mismatches consistent with oxidative damage. These patterns reveal aDNA damage beyond deamination and that remains understudied, suggesting that aeDNA should be carefully evaluated in genomic and evolutionary analyses. The new dating method, ratePlacer, extends molecular clock dating of aDNA from single-specimen to pooled environmental DNA data, where traditional methods struggle.

  bioinformatics2026-07-07v1

• ORBIT: Annotation-Aware Empirical Enrichment and Semantic Reranking for Interpretable Functional-Class Recovery

  Kidder, B. L.

  Abstract

  Gene-set interpretation workflows are widely used to summarize transcriptomic and proteomic experiments, yet standard enrichment tools often return long, redundant result tables that require substantial manual consolidation. We developed ORBIT (Ontology-Ranked Biological Interpretation Tool), an annotation-aware interpretation workflow that combines empirical enrichment, semantic reranking, and redundancy-aware representative-term selection to prioritize interpretable functional summaries from gene sets. We evaluated ORBIT on a curated tiered benchmark of human functional-class gene sets spanning clean reference sets, size-ladder variants, and mixed-difficulty cases. On the 45-set core benchmark, ORBIT semantic achieved higher expected-class recovery than Enrichr and PANTHER Gene Ontology molecular-function baselines, with a mean reciprocal rank of 0.916 and top-1 recovery of 0.889. Bootstrap confidence intervals and paired permutation testing supported the robustness of this advantage, and supplemental analyses extended the comparison to g:Profiler. In a GPCR mixed-function case study, ORBIT compressed redundant enriched terms into semantic representative neighborhoods, illustrating how long enrichment outputs can be converted into reviewable biological summaries. We then used ORBIT to interpret immune-cell identity, interferon-response biology, and breast-cancer subtype programs. ORBIT linked PBMC3K markers to cytotoxic, antigen-presentation, and innate-immune cell states; prioritized antiviral, cytokine-response, RNA-binding, and secreted-factor biology after IFNB stimulation; and separated TCGA-BRCA basal-like proliferative chromosome/cell-cycle programs from luminal transporter and receptor-associated biology while retaining gene-level support.

  bioinformatics2026-07-07v1

• UVfinder: a tool to extract bryophyte sex-linked gene copies from the GoFlag408 probe set

  Kim, S.; Bowman, J.; Braun, E. L.; McDaniel, S.

  Abstract

  Target enrichment sequencing using probe sets like GoFlag 408 has revolutionized phylogenetics, yet recent genomic data indicate that some probes may be sex-linked, potentially introducing topological conflict while also allowing studies of sex-specific evolutionary processes. To test for sex-linkage across the bryophytes, we developed UVfinder, a pipeline designed to identify sex-linked GoFlag loci across published moss genomes and enable sex-aware downstream analyses. Applying UVfinder to 50 dioicous moss genomes, we identified 93 probes that exhibit sex-linkage in one or more lineages, providing genomic evidence for neo-sex chromosome formation via autosome-sex chromosome fusion and gene translocation. Furthermore, by comparing species trees derived from sex-linked versus autosomal loci in Hypnales and Dicranidae, we demonstrate that sex-linked loci harbor phylogenetic information that is distinct from that in autosomes. We also discovered a pervasive female sampling bias in the genomic data, perhaps reflecting a preference among collectors for plants with sporophytes. Ultimately, our findings highlight the dynamism in sex linkage across bryophytes and suggest that sex-aware phylogenomics can be used to reconstruct ancestral karyotypes and potentially resolve topological conflict. We expect that UVfinder will facilitate the further study of sex-specific evolutionary processes, particularly with improved genome assemblies and increased sampling in males.

  bioinformatics2026-07-07v1

• ThermoFusion: A Multimodal Deep Learning Framework for Generalizable Prediction of Enzyme Thermostability

  Wei, Y.; Eberini, I.; Meyer, F.

  Abstract

  Protein thermostability is a critical property for both industrial and biomedical enzyme applications, yet experimental evaluation of mutation-induced stability changes remains laborious and costly. Here, we present ThermoFusion, a hybrid deep learning framework that integrates 3D protein structure embeddings from ThermoMPNN with sequence-based embeddings from the pretrained protein language model ESM2 to predict the effects of single-point mutations on protein stability ({Delta}{Delta}G). ThermoFusion exhibits robust generalization, maintaining high predictive accuracy across out of distribution sequences with low identity to the training set -- a scenario where many other machine learning models, including ThermoMPNN and state-of-the-art tools, perform poorly due to reliance on memorization. Benchmarking on a curated enzyme dataset comprising of 105 enzymes and 3144 mutations shows that ThermoFusion reliably identifies stabilizing mutations while accurately predicting stability for enzymes beyond its training set. These results establish ThermoFusion as a powerful tool for rational enzyme design beyond its training set.

  bioinformatics2026-07-07v1

• Cross-architecture ensembling of DNA foundation models improves the precision and stability of chimera detection in long-read metagenomic bins

  MinSeo, K.; Jae-Ho, S.

  Abstract

  Motivation: Chimeric metagenome-assembled genomes (MAGs) that pool DNA from multiple organisms contaminate downstream analyses. Marker-gene tools such as CheckM2 miss low-level chimerism, and DNA foundation models have been proposed as a sequence-composition alternative, but whether large autoregressive models (Evo2, 7B parameters) outperform smaller contrastive models (DNABERT-S, 117M) has not been rigorously tested.

  bioinformatics2026-07-07v1

• BRAID: RT-PCR-calibrated conformal intervals for splicing ΔPSI

  Park, J.; Kang, K.

  Abstract

  Differential splicing workflows usually report a {Delta}PSI point estimate and a statistical score, but these outputs do not directly state whether the RNA-seq estimate is close enough to an orthogonal validation measurement. We developed BRAID as a post-processing calibration step for splicing analyses. BRAID estimates RNA-seq {Delta}PSI from rMATS inclusion and skipping counts, retains the upstream caller evidence, and adds a 95% interval whose width is calibrated from empirical RNA-seq-to-RT-PCR residuals using split conformal prediction. The packaged differential-splicing calibrator uses a residual half-width of q = 0.341, estimated from 162 RT-PCR-validated skipped-exon events. We evaluated BRAID on three RT-PCR validation datasets covering TRA2 knockdown, mouse cerebellum versus liver, and a prostate epithelial-to-mesenchymal comparison. On the pooled common set of 139 cassette-exon events, BRAID reached 0.971 RT-PCR coverage, whereas MAJIQ, betAS, and rMATS-derived intervals reached 0.518, 0.734, and 0.633, respectively. BRAID also had the lowest pooled interval score, 0.720, compared with 2.040 for MAJIQ, 1.414 for betAS, and 1.625 for rMATS. Applying the same residual calibration to other caller outputs brought MAJIQ, betAS, rMATS, and SUPPA2 {Delta}PSI estimates close to nominal RT-PCR coverage, indicating that the gain came from interval calibration rather than from a caller-specific point estimate. In a TRA2 positive-negative validation panel, using q as a hard rMATS effect-size cutoff reduced recall, whereas using q as an interval half-width improved RT-PCR coverage. Applied to a public DM1 skeletal-muscle rMATS table, BRAID reduced 967 large-effect significant events to 68 high-confidence interval-supported events and retained known DM1 and muscle-splicing signals. BRAID provides a practical calibrated reliability layer for RNA-seq splicing studies where downstream follow-up depends on the precision of reported {Delta}PSI estimates.

  bioinformatics2026-07-07v1

• PACMOS: an R package for Projection And Classification of Multi-Omic Samples

  Kalson, L.; Sexton-Oates, A.; Drevet, G.; Fernandez-Cuesta, L.; Foll, M.; Alcala, N.

  Abstract

  Motivation: Integrated multi-omic analyses have transformed our understanding of cancer biology, giving rise to data-driven molecular classifications that capture disease heterogeneity beyond conventional histopathology. Among these approaches, multi-omic factor analysis (MOFA), a multimodal extension of principal component analysis, has been widely used to identify sources of molecular variation across omic layers and classify samples into molecular groups. However, classifying query samples according to an existing MOFA-based classification remains challenging, as there is no validated computational method for projecting samples into pretrained MOFA latent factor spaces. Results: We present PACMOS, an R package that provides a generalizable approach to project query samples into pretrained MOFA latent factor spaces. We validate PACMOS using two cancer datasets with published MOFA-based classifications - lung neuroendocrine neoplasms and pleural mesothelioma - showing that PACMOS preserves the existing MOFA latent factor space while allowing to classify query samples. Availability and implementation: PACMOS is an open-source R package available on the IARC bioinformatics GitHub organization (submitted to Bioconductor) at https://github.com/IARCbioinfo/PACMOS and DOI in Zenodo: https://doi.org/10.5281/zenodo.20933824, along with installation instructions and a vignette with an application. Supplementary information: Supplementary data are available in separate files.

  bioinformatics2026-07-07v1

• Determinants of Blood Group Antigen Expression and Prediction of Phenotypes by Machine Learning

  Kranz, A.-C.; Schneider, J.; Gassner, C.; Bublitz, M.

  Abstract

  Blood group antigens, defined by epitopes on the erythrocyte surface, are central to transfusion safety and maternal-fetal compatibility. While the genetic basis of many clinically relevant blood group antigens is well established, which structural and biophysical parameters determine whether a single-nucleotide variant gives rise to an antigenic phenotype remains unclear. Here, we integrate structural, biophysical, and evolutionary analyses to systematically evaluate features associated with single amino acid substitutions across 24 human protein-based blood group systems. We analyse 319 variants with curated phenotypic annotations alongside 481 control variants, identifying key determinants of null and antigenic phenotypes. Null variants are characterized by high evolutionary conservation, burial within the protein core, loss of hydrophobicity, increased polarity, and a propensity for arginine substitutions. Antigenic variants are also enriched in arginine; however, in contrast to null variants, they tend to occur at less conserved, more solvent-accessible, and structurally flexible sites. Supervised machine learning models trained on structural and biophysical descriptors were applied to distinguish (i) null and (ii) antigenic variants from controls, achieving balanced accuracies of 0.82 and 0.63, respectively. Feature importance analysis identified predicted pathogenicity, solvent accessibility, and evolutionary conservation as the most predictive determinants of null variants, whereas hydrophobicity, conservation, and flexibility dominated antigen prediction. This work establishes a framework linking molecular variation to blood group phenotypes and provides a foundation for predicting the impact of novel missense mutations in transfusion medicine and beyond.

  bioinformatics2026-07-07v1

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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