Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• EMReady2: improvement of cryo-EM and cryo-ET maps by local quality-aware deep learning with Mamba

  Cao, H.; Zhu, Y.; Li, T.; Chen, J.; He, J.; Wang, X.; Huang, S.-Y.

  AI Summary

  • The study addresses the challenge of improving cryo-EM map quality by introducing EMReady_mamba, a deep learning model using a Mamba-based dual-branch UNet architecture.
  • EMReady_mamba employs a local resolution-guided learning strategy to handle map heterogeneity, extending its applicability to various map types including nucleic acids and cryo-ET maps.
  • Evaluated on 136 maps, EMReady_mamba demonstrated superior performance in enhancing map quality and interpretation compared to existing methods._

  Abstract

  Cryo-electron microscopy (cryo-EM) has emerged as a leading technology for determining the structures of biological macromolecules. However, map quality issues such as noise and loss of contrast hinder accurate map interpretation. Traditional and deep learning-based post processing methods offer improvements but face limitations particularly in handling map heterogeneity. Here, we present a generalist Mamba-based deep learning model for improving cryo-EM maps, named EMReady_mamba. EMReady_mamba introduces a fast Mamba-based dual-branch UNet architecture to jointly capture local and global features. In addition, EMReady_mamba also uses a local resolution-guided learning strategy to address map heterogeneity, and significantly extends the training set. These advances render EMReady_mamba applicable to a broader range of cryo-EM maps, including those con taining nucleic acids, medium-resolution maps, and cryo-electron tomography (cryo-ET) maps, while substantially reducing computational cost. EMReady_mamba is extensively evaluated on 136 diverse maps at 2.0-10.0 A resolutions, and compared with existing map post-processing methods. It is shown that EMReady_mamba exhibits state-of-the-art performance in both map quality and map interpretation improvement. EMReady2 is freely available at https://github.com/huang-laboratory/EMReady2/.

  bioinformatics2026-02-14v2

• IntelliFold-2: Surpassing AlphaFold 3 via Architectural Refinement and Structural Consistency

  Qiao, L.; Yan, H.; Liu, G.; Guo, G.; Sun, S.

  AI Summary

  • IntelliFold-2 enhances biomolecular structure prediction through architectural refinements like latent space scaling and atom-attention, improving over AlphaFold 3.
  • Key improvements include better performance in therapeutic contexts, especially for antibody-antigen interactions and protein-ligand co-folding.
  • Three variants (Flash, v2, Pro) are released to cater to different needs from efficient fine-tuning to high-precision inference.

  Abstract

  IntelliFold-2 is an open-source biomolecular structure prediction model that improves accuracy and robustness through architectural refinement and multiscale structural consistency. We introduce latent space scaling in Pairformer blocks, a principled atom-attention formulation with stochastic atomization, policy-guided optimization for diffusion sampling and difficulty-aware loss reweighting. On Foldbench, IntelliFold-2 improves performance in therapeutically relevant settings, with particularly strong gains for antibody-antigen interactions and protein-ligand co-folding relative to AlphaFold 3. We release three variants (Flash, v2, and Pro) to cover efficient fine-tuning through high-precision server-side inference.

  bioinformatics2026-02-14v2

• RNApdbee 3.0: A unified web server for comprehensive RNA secondary structure annotation from 3D coordinates

  Pielesiak, J.; Niznik, K.; Snioszek, P.; Wachowski, G.; Zurawski, M.; Antczak, M.; Szachniuk, M.; Zok, T.

  AI Summary

  • RNApdbee 3.0 is a web server that integrates 2D and 3D data to annotate RNA secondary structures, classifying base pairs and identifying various nucleotide interactions.
  • It handles incomplete or modified residues, providing results in standard formats like dot-bracket notation, BPSEQ, and CT, along with graphical visualizations.
  • The tool standardizes inputs to PDBx/mmCIF, integrates seven annotation tools, and decomposes structures into stems, loops, and single strands, ensuring comprehensive RNA structural analysis.

  Abstract

  RNApdbee 3.0 (publicly available at https://rnapdbee.cs.put.poznan.pl/) offers an advanced pipeline for comprehensive RNA structural annotation, integrating 2D and 3D data to build detailed nucleotide interaction networks. It classifies base pairs as canonical or noncanonical using the Leontis-Westhof and Saenger schemes and identifies stacking, base-ribose, base-phosphate, and base-triple interactions. The tool handles incomplete or modified residues, marking missing nucleotides and distinguishing noncanonical base pairs for accurate and effective visualization. Results are provided in standard formats - namely, extended dot-bracket notation, BPSEQ, and CT - and in highly valuable graphical visualizations. RNApdbee decomposes secondary structures into stems, loops, and single strands and offers flexible pseudoknot encoding. Its unified framework addresses inconsistencies across structural data formats by standardizing all inputs to PDBx/mmCIF and integrating seven widely used annotation tools. Finally, RNApdbee ensures reliable, format-independent, and comprehensive RNA structural annotation and interpretation.

  bioinformatics2026-02-14v2

• DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data

  Li, R.; Yang, J.; Su, P.-C.; Jaffe, A.; Lindenbaum, O.; Kluger, Y.

  AI Summary

  • DiCoLo is introduced to detect localized differential gene co-expression in single-cell data without relying on cell clustering or cross-condition alignment.
  • It uses Optimal Transport distances to construct gene graphs and identify changes in gene connectivity patterns between conditions.
  • DiCoLo effectively identifies differential gene co-localization in complex scenarios, revealing new insights in mouse hair follicle development related to dermal condensate differentiation.

  Abstract

  Detecting changes in gene coordination patterns between biological conditions and identifying the cell populations in which these changes occur are key challenges in single-cell analysis. Existing approaches often compare gene co-expression between predefined cell clusters or rely on aligning cells across conditions. These strategies can be suboptimal when changes occur within small subpopulations or when batch effects obscure the underlying biological signal. To address these challenges, we introduce DiCoLo, a framework that identifies genes exhibiting differential co-localization, defined as changes in coordinated expression within localized cell neighborhoods - subsets of highly similar cells in the transcriptomic space. Importantly, DiCoLo does not rely on cell clustering or cross-condition alignment. For each condition, DiCoLo constructs a gene graph using Optimal Transport distances that reflect gene co-localization patterns across the cell manifold. Then, it identifies differential gene programs by detecting changes in connectivity patterns between the gene graphs. We show that DiCoLo robustly identifies differential gene co-localization even under weak signals or complex batch effects, outperforming existing methods across multiple benchmark datasets. When applied to mouse hair follicle development data, DiCoLo reveals coordinated gene programs and emerging cell populations driven by perturbations in morphogen signaling that underlie dermal condensate differentiation. Overall, these results establish DiCoLo as a powerful framework for uncovering localized differential transcriptional coordinated patterns in single-cell data.

  bioinformatics2026-02-14v2

• DVPNet: A New XAI-Based Interpretable Genetic Profiling Framework Using Nucleotide Transformer and Probabilistic Circuits

  Kusumoto, T.

  AI Summary

  • The study introduces DVPNet, an XAI-based framework for genetic profiling that uses a Nucleotide Transformer and probabilistic circuits to classify cancer vs. normal cells.
  • Using the GSE131907 dataset, 900 genes per sample were selected, transformed into embeddings, and used to train the model, which then provided probabilistic contributions for classification.
  • Key findings include identification of 1,524 genes with unexpected contribution scores, highlighting genes like ITGA5 and TP73, offering new insights beyond traditional statistical methods.

  Abstract

  In this study, we present an XAI-based genetic profiling framework that quantifies gene importance for distinguishing cancer cells from normal cells based on an interpretable AI decision process. We propose a new explainable AI (XAI) classification model that combines probabilistic circuits with the Nucleotide Transformer. By leveraging the strong feature-extraction capability of the Nucleotide Transformer, we design a tractable classification framework based on probabilistic circuits while preserving probabilistic interpretability. To demonstrate the capability of this framework, we used the GSE131907 single-cell lung cancer atlas and constructed a dataset consisting of cancer-cell and normal-cell classes. From each sample, 900 gene types were randomly selected and converted into embedding vectors using the Nucleotide Transformer, after which the classification model was trained. We then extracted class-specific probabilistic contributions from the tractable model and defined a contribution score for the cancer-cell class. Genetic profiling was performed based on these scores, providing insights into which genes and biological pathways are most important for the classification task. Notably, 1,524 of the 9,540 observed genes showed contribution scores that contradicted what would be expected from their class-wise occurrence frequencies, suggesting that the profiling goes beyond simple statistics by leveraging biological feature representations encoded by the Nucleotide Transformer. The top-ranked genes among these contradictory cases include several well-studied genes in cancer research (e.g., ITGA5, SIGLEC9, NOTUM, and TP73). Overall, these analyses go beyond traditional statistical or gene-expression-level approaches and provide new academic insights for genetic research.

  bioinformatics2026-02-14v2

• Discovery of TDP-43 aggregation inhibitors via a hybrid machine learning framework

  Kapsiani, S.; Vora, S.; Fernandez-Villegas, A.; Kaminski, C. F.; Läubli, N. F.; Kaminski Schierle, G. S.

  AI Summary

  • Researchers developed a hybrid machine learning approach using GNN embeddings, chemical descriptors, and biological annotations to identify TDP-43 aggregation inhibitors.
  • The model screened 3,853 compounds, identifying berberrubine and PE859 as effective inhibitors, with molecular docking showing favorable interactions with TDP-43's RRM domain.
  • Experimental validation confirmed that both compounds reduced TDP-43 aggregation in HEK cells, with PE859 significantly improving locomotor defects in C. elegans.

  Abstract

  TAR DNA-binding protein 43 (TDP-43) aggregation is a hallmark of several neurodegenerative diseases, including amyotrophic lateral sclerosis and frontotemporal dementia. Recent therapeutic efforts have highlighted the potential of small molecules capable of inhibiting TDP-43 aggregation; however, no effective treatments currently exist. Here, we developed a hybrid machine learning approach combining graph neural network (GNN) embeddings with traditional chemical descriptors and biological target annotations. Using XGBoost as the final classifier enabled model interpretability through SHAP analysis, allowing the identification of key chemical features and target annotations associated with TDP-43 anti-aggregation activity. Complementary Monte Carlo Tree Search analysis highlighted specific chemical substructures linked to predicted activity. By screening an external library of 3,853 small molecules, the model identified two compounds not previously evaluated against TDP-43 aggregation, namely berberrubine and PE859. Molecular docking analysis revealed that both compounds interact favourably with the TDP-43 RNA recognition motif (RRM) domain through distinct binding modes. Experimental validation showed that both compounds significantly reduced TDP-43 aggregation in HEK cells. Further testing in Caenorhabditis elegans expressing human TDP-43 demonstrated that PE859 significantly rescued locomotor defects, while berberrubine showed partial improvement. This work establishes a hybrid machine learning approach for accelerating small molecule drug discovery, yielding two promising therapeutic candidates for TDP-43 proteinopathies.

  bioinformatics2026-02-14v1

• CPLfold: Chimeric and Pseudoknot-capable almost Linear-time RNA Secondary Structure Prediction

  Wang, K.; Kudla, G.; Cohen, S. B.

  AI Summary

  • CPLfold is a new RNA folding method that integrates thermodynamic modeling with chimeric evidence from RNA cross-linking and ligation to predict RNA secondary structures, including pseudoknots.
  • It scales effectively for long sequences and outperforms existing methods in predicting global structures and long-range interactions, as shown in benchmarks like COMRADES and IRIS.
  • The method offers flexibility through two parameters to balance the incorporation of chimeric evidence and pseudoknot prediction.

  Abstract

  Motivation: RNA structure plays a central role in how transcripts function, but inferring it reliably remains difficult, especially when pseudoknots need to be part of the prediction. Chemical probing experiments provide additional signals, yet these signals do not directly identify base pairing partners. RNA proximity ligation provides direct evidence of base pairing, but balancing this evidence with pseudoknot prediction accuracy and scalability of structure prediction for long sequences remains challenging. Results: We present CPLfold, a fast and flexible RNA folding method that combines thermodynamic modeling with chimeric evidence from RNA cross-linking and ligation experiments, while naturally supporting pseudoknots. CPLfold scales to long sequences and recovers more accurate global structures and long-range interactions than existing approaches across multiple benchmarks such as COMRADES and IRIS. By tuning two simple trade-off parameters (, {beta}) the method allows flexibility in the level of incorporating chimeric evidence and asserting pseudoknots. Availability and Implementation: Source code and scripts are available at https://github.com/Vicky-0256/CPLfold.

  bioinformatics2026-02-14v1

• Analysis of Age-Specific Dysregulation of miRNAs in Lung Cancer Via Machine learning: Biomarker Identification and Therapeutic Implications in Patients Aged 60 and Above.

  Hasan, A.; Muzaffar, A.

  AI Summary

  • This study analyzed miRNA dysregulation in lung cancer patients aged 60 and above using RNA sequencing data from TCGA.
  • Differential expression analysis identified 25 significant miRNAs, with hsa-mir-1911 upregulated and others like hsa-mir-196a downregulated.
  • Machine learning highlighted key miRNAs involved in lung cancer biology, suggesting their potential as biomarkers for early diagnosis and personalized therapy targets.

  Abstract

  Lung cancer is the leading cause of cancer-related mortality worldwide, predominantly affects older individuals, with non-small cell lung cancer (NSCLC) comprising 85% of cases. Despite advancements in diagnosis and treatment, prognosis for elderly patients remains poor. This study investigates the role of microRNAs (miRNAs) involved in lung cancer, focusing on individuals aged 60 and above. RNA sequencing data from The Cancer Genome Atlas (TCGA) was used to conduct differential expression analysis of miRNA profiles from elderly and senile patient groups. Results showed that out of 1,881 miRNA profiles, 801 were found to be differentially expressed. Filtering for significance identified that 25 miRNAs, with hsa-mir-1911 upregulated and 24, including hsa-mir-196a and hsa-mir-323b found to be downregulated. Studies showed that these miRNAs play roles in apoptosis, senescence, and inflammation. Another Experimental approach in this study, used Machine learning analysis which highlighted key miRNAs, including hsa-mir-181b, hsa-mir-542, hsa-mir-450b, hsa-mir-584, and hsa-mir-21 as crucial in lung cancer biology. Moreover, Functional enrichment analysis revealed their involvement in gene silencing, translational repression, and RNA-induced silencing complex (RISC) regulation. This research identifies the association of miRNAs and aging in lung cancer and finds potential biomarkers that can be helpful in early diagnosis and targets for personalized therapies.

  bioinformatics2026-02-14v1

• Feature-based in-silico model to predict the Mycobacterium tuberculosis bedaquiline phenotype associated with Rv0678 variants

  Quispe Rojas, W.; de Diego Fuertes, M.; Rennie, V.; Riviere, E.; Safarpour, M.; Van Rie, A.

  AI Summary

  • The study developed an in-silico model to predict bedaquiline resistance in Mycobacterium tuberculosis based on 13 features of Rv0678 variants.
  • Key features included evolutionary conservation and proximity to functional sites, with the model achieving high accuracy (ROC-AUC 0.826, sensitivity 87.1%, specificity 88.2%).
  • External validation showed reduced performance, likely due to varied phenotypic measurement methods.

  Abstract

  Bedaquiline resistance is emerging globally and threatens the effectiveness of the novel short all-oral regimens for rifampicin-resistant tuberculosis. Following a systematic literature review, we quantified 13 sequence, biochemical, and structural features of 62 Rv0678 missense variants reported in 136 Mycobacterium tuberculosis isolates. Using rigorous machine learning methods, we show that the strongest contributing features were the evolutionary conservation score and the shortest atomic distance to key functional sites. The final 5-feature model had good performance (ROC-AUC 0.826) and classified the bedaquiline phenotype with high accuracy [sensitivity 87.1% (95% CI, 78.3-92.6) and specificity 88.2% (95% CI, 76.6-94.5)]. Performance was lower in external validation, likely due to the measurement error introduced when using diverse phenotypic methods. missense variants on the mmpR5 protein structure and function. Integrating the five-feature in-silico in variant interpretation software could improve the prediction of the effect of Rv0678 variants and guide clinical management of rifampicin-resistant tuberculosis.

  bioinformatics2026-02-14v1

• CodonRL: Multi-Objective Codon Sequence Optimization Using Demonstration-Guided Reinforcement Learning

  Du, S.; Kaynar, G.; Li, J.; You, Z.; Tang, S.; Kingsford, C.

  AI Summary

  • CodonRL uses reinforcement learning to optimize codon sequences for translation efficiency, RNA stability, and compositional properties, addressing challenges like large action spaces and delayed rewards.
  • It employs LinearFold for training and ViennaRNA for evaluation, with expert sequences to guide learning and milestone rewards to manage long-range optimization.
  • On a benchmark of 55 human proteins, CodonRL outperformed GEMORNA, showing improvements in CAI by 9.5%, MFE by 25.4 kcal/mol, and reducing uridine content by 3.4%, enhancing translation efficiency, stability, and reducing immunogenicity.

  Abstract

  Optimizing synonymous codon sequences to improve translation efficiency, RNA stability, and compositional properties is challenging because the search space grows exponentially with protein length and objectives interact through long range RNA structure. Dynamic programming-based methods can provide strong solutions for fixed objective combinations but are difficult to extend to additional constraints. Deep generative models require large-scale, high-quality mRNA sequence datasets for training, limiting applicability when such data are scarce. Reinforcement learning naturally handles sequential decision-making but faces challenges in codon optimization due to delayed rewards, large action spaces, and expensive structural evaluation. We present CodonRL, a reinforcement learning framework that learns a structural prior for mRNA design from efficient folding feedback and demonstration-guided replay, and then enables user-controlled multi-objective trade-offs during inference. CodonRL uses LinearFold for fast intermediate reward computation during training and ViennaRNA for final evaluation, warms up learning with expert sequences to accelerate convergence for global structure objectives, and introduces milestone-based intermediate rewards to address delayed feedback in long range optimization. On a benchmark of 55 human proteins, CodonRL outperforms GEMORNA, a state-of-the-art codon optimization method, across multiple metrics, achieving 9.5% higher codon adaptation index (CAI), 25.4 kcal/mol more favorable minimum free energy (MFE), and 3.4% lower uridine content on average, while improving codon stabilization coefficient (CSC) in over 90% of benchmark proteins under matched constraints. These gains translate into designs that are predicted to be more efficiently translated, more structurally stable, and less immunogenic, while supporting continuous objective reweighting at inference time.

  bioinformatics2026-02-14v1

• Cell phenotypes in the biomedical literature: a systematic analysis and text mining corpus

  Rotenberg, N. H.; Leaman, R.; Islamaj, R.; Kuivaniemi, H.; Tromp, G.; Fluharty, B.; Richardson, S.; Eastwood, C.; Diller, M.; Xu, B.; Pankajam, A. V.; Osumi-Sutherland, D.; Lu, Z.; Scheuermann, R. H.

  AI Summary

  • The study introduces CellLink, a corpus of over 22,000 annotated mentions of human and mouse cell populations from recent literature, linked to Cell Ontology terms.
  • Analysis showed lineage-specific patterns in cell naming based on various attributes.
  • Fine-tuning transformer models on CellLink improved named entity recognition, and embedding approaches enhanced zero-shot entity linking, with applications in refining the chondrocyte branch of Cell Ontology.

  Abstract

  The variety of cell phenotypes identified by single-cell technologies is rapidly expanding, yet this knowledge is dispersed across the scientific literature and incompletely represented in structured resources. We present the CellLink corpus, a manually annotated collection of over 22,000 mentions of human and mouse cell populations in recent journal articles, distinguishing specific cell phenotypes, heterogeneous cell populations, and vague cell populations, and linking to Cell Ontology (CL) terms as either exact or related matches, covering nearly half of the terms in the current CL. A systematic analysis reveals lineage-specific patterns in how authors utilize anatomical context, molecular signatures, functional roles, developmental stage, and other attributes in cell naming. We show that fine-tuning transformer-based models on CellLink yields strong performance for named entity recognition, while embedding-based approaches support zero-shot entity linking and distinguishing exact from related matches. We further demonstrate the utility of CellLink to expand and refine the chondrocyte branch of CL.

  bioinformatics2026-02-14v1

• evoCancerGPT: Generating Zero-Shot Single-Cell and Single-Sample Cancer Progression Through Transfer Learning

  Wang, X.; Tan, R.; Cristea, S.

  AI Summary

  • The study introduces evoCancerGPT, a transformer model designed to predict future gene expression in cancer evolution using single-cell RNA sequencing data.
  • It uses transfer learning from 2.76 million cell tokens across 7 cancer types, ordered by pseudotime, to forecast cancer progression.
  • evoCancerGPT showed high accuracy in predicting cancer trajectories, outperforming linear models and scGPT in low-context scenarios.

  Abstract

  Cancer evolution is driven by complex changes in gene expression as cells transition and change states during tumorigenesis. Single-cell RNA sequencing has provided snapshot insights into how the transcriptomics of tumors evolve, but whether the existing knowledge can be used to reliably learn and generate the patterns behind the evolution of cancers remains unknown. Here, we introduce evoCancerGPT, a generative pre-trained transformer decoder-only single-cell foundation model designed to forecast future gene expression profiles in cancer evolution by leveraging previous cell states at the level of single patients. This model integrates the continuous gene expression data of each cell to create a comprehensive representation of a cell token. Training sentences are constructed for each cancer type, each patient and each cell type separately, ordered via inferred pseudotime algorithms, using 2.76 million cell tokens, each with 12,639 genes, spanning 7 cancer types. By learning from long-range dependencies between cells arranged in pseudotime from a large corpus of data, evoCancerGPT captures key transitions in cancer evolution, achieving high concordance to ground truth trajectories and outperforming linear and scGPT baselines in held-out test samples in low-context scenarios. Our work suggests evoCancerGPT's potential utility in characterizing tumor progression at a single-cell and single-patient level and ultimately contributing to more personalized cancer care.

  bioinformatics2026-02-14v1

• Theseus: Fast and Optimal Affine-Gap Sequence-to-Graph Alignment

  Jimenez-Blanco, A.; Lopez-Villellas, L.; Moure, J. C.; Moreto, M.; Marco-Sola, S.

  AI Summary

  • Theseus is a novel algorithm for optimal affine-gap sequence-to-graph alignment, designed to reduce memory and computational demands while maintaining optimality.
  • It uses the diagonal transition property and a sparse-data strategy to accelerate alignment, applicable to arbitrary directed graphs including those with cycles.
  • Theseus outperforms existing methods in speed for multiple sequence alignment (2.0x to 232.2x faster) and pangenome read mapping (1.9x to 16.9x faster).

  Abstract

  Motivation: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. Results: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus exploits the diagonal transition property to process only a subset of the dynamic programming cells, combined with a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: multiple sequence alignment (MSA) and pangenome read mapping. For MSA, we compare it against the state-of-the-art methods SPOA, abPOA, and POASTA. Theseus is 2.0x to 232.2x faster than the other two optimal aligners, SPOA and POASTA. Compared with abPOA, a heuristic aligner, Theseus is 3.3x faster on average, while ensuring optimality. For pangenome read mapping, we benchmark Theseus against the alignment stage of the popular mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9x to 16.9x speed improvement on short reads.

  bioinformatics2026-02-14v1

• Machine learning-guided design of artificial microRNAs for targeted gene silencing

  Belter, A.; Synak, J.; Mackowiak, M.; Kotowska-Zimmer, A.; Figlerowicz, M.; Szachniuk, M.; Olejniczak, M.

  AI Summary

  • The study developed miRarchitect, a machine learning-based platform for designing artificial microRNAs (amiRNAs) to enhance targeted gene silencing.
  • miRarchitect integrates neural network-guided selection, siRNA design, and scaffold choice, using data from human pri-miRNAs and next-generation sequencing.
  • Validation experiments targeting TMPRSS2 and ACE-2 showed miRarchitect-designed amiRNAs had precise processing, robust knockdown, and high specificity, outperforming other tools in benchmarking.

  Abstract

  Artificial microRNAs (amiRNAs) offer a powerful strategy for targeted gene silencing, but their rational design is limited by complex sequence-structure-processing relationships and the lack of tools capable of optimizing efficacy and specificity. To address this need, we developed miRarchitect, a web-based platform that uses machine learning to support the customizable design of amiRNAs. miRarchitect integrates neural network-guided target-site selection, siRNA insert design, and scaffold choice, utilizing large-scale data from human primary microRNAs (pri-miRNAs) and next-generation sequencing. The platform generates molecules that closely resemble endogenous pri-miRNAs and includes comprehensive off-target analysis to enhance specificity. Experimental validation targeting TMPRSS2 and ACE-2 confirmed precise processing, robust knockdown, and high specificity of miRarchitect-designed amiRNAs. In comparative benchmarking, miRarchitect consistently produced functional amiRNAs, whereas only half of the top candidates generated by other tools showed measurable activity. miRarchitect is freely available at https://rnadrug.ichb.pl/mirarchitect and provides an intuitive interface with an automated workflow for generating, ranking, and selecting candidate amiRNAs for research and therapeutic applications.

  bioinformatics2026-02-14v1

• MassID provides near complete annotation of metabolomics data with identification probabilities

  Stancliffe, E.; Gandhi, M.; Guzior, D. V.; Mehta, A.; Acharya, S.; Richardson, A. D.; Cho, K.; Cohen, T.; Patti, G. J.

  AI Summary

  • MassID is a cloud-based untargeted metabolomics pipeline designed to process LC/MS data from raw spectra to identified metabolite profiles, addressing challenges like noise and non-quantitative identification.
  • It uses deep learning for peak detection, comprehensive noise filtering, and introduces DecoID2 for probabilistic metabolite identification with FDR control.
  • Applied to human plasma, MassID annotated nearly all signals, identifying over 4,000 metabolites, with over 1,200 at FDR <5%, enhancing specificity and discovery beyond traditional MSI levels.

  Abstract

  Liquid chromatography coupled to mass spectrometry (LC/MS) is a powerful tool in metabolomics research, generating tens-of-thousands of signals from a single biological sample. However, current software solutions for unbiased assessment of metabolomics data analysis are limited by complex sources of noise and non-quantitative metabolite identifications that make results difficult to interpret. Here, we present MassID, a cloud-based untargeted metabolomics pipeline that aims to overcome the innate challenges of unbiased metabolite analysis and perform end-to-end data processing, transforming raw spectra to normalized and identified metabolite profiles. MassID incorporates a suite of software functionalities, including deep learning-based peak detection and comprehensive noise filtering. In addition, with MassID we introduce a novel software module: DecoID2 that enables probabilistic metabolite identification for false discovery rate (FDR)-controlled metabolomics. When applied to a human plasma dataset, MassID results in near-complete signal annotation, identification of >4,000 metabolites (including >1,200 compounds at an FDR <5%) across four complementary LC/MS runs, and enables integrated downstream analyses to understand biochemical dysregulation at both the molecular and pathway level. When compared to the Metabolomics Standards Initiative (MSI) confidence levels, identification probability generally correlated with MSI levels. However, only 356/418 of MSI Level 1 compounds were identified with <5% FDR and the remaining 884 FDR < 5% compounds were identified from MSI L2-L3 compounds, highlighting the enhanced specificity and discovery potential achieved by MassID.

  bioinformatics2026-02-14v1

• TOXsiRNA: A web server to predict the toxicity of chemically modified siRNAs

  Dar, S.; Kumar, M.

  AI Summary

  • The study developed TOXsiRNA, a web server to predict the toxicity of chemically modified siRNAs, addressing the challenge of experimental testing.
  • Machine learning models, including SVM, LR, KNN, and ANN, were used, with the SVM model based on mononucleotide composition showing the best performance (PCC of 0.91 and 0.92).
  • The server, available at http://bioinfo.imtech.res.in/manojk/toxsirna, also integrates models for predicting siRNA knockdown efficacy and off-target effects.

  Abstract

  Small interfering RNAs (siRNAs) are largely modified with chemical molecules to enhance their properties for use in molecular biology research and therapeutic applications. Toxicity effects may arise due to these chemical moieties as well as sequence based off-targets at cellular level. Enormous resources are required to experimentally design and test the toxicity of these chemical modifications and their combinations on siRNAs. To address this problem, we developed TOXsiRNA web server to computationally predict the toxicity of chemically modified siRNAs and their off-targets. We selected 2749 siRNAs with different permutations and combinations of 21 different chemical modifications engineered on them. Next, we used Support Vector Machine (SVM), Linear Regression (LR), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) machine learning applications to develop models. Best performance was displayed by mononucleotide composition based model developed with SVM, offering Pearson Correlation Coefficient (PCC) of 0.91 and 0.92 on training testing and independent validations respectively. Other sequence features like dinucleotide composition binary pattern and their combinations were also tested. Finally, three models of chemically modified siRNAs were implemented on the web server. Other algorithms that include predicting normal as well as chemically modified siRNA knockdown efficacy, off target etc. are also integrated. The resource is hosted online for scientific use freely at url: http://bioinfo.imtech.res.in/manojk/toxsirna.

  bioinformatics2026-02-14v1

• Inferring a novel insecticide resistance metric and exposurevariability in mosquito bioassays across Africa

  Denz, A.; Kont, M. D.; Sanou, A.; Churcher, T. S.; Lambert, B.

  AI Summary

  • This study introduces a new predictive model to assess insecticide resistance in mosquitoes by incorporating data from intensity-dose susceptibility bioassays, addressing variability due to genetic factors.
  • The model was fitted to data from across Africa, focusing on Burkina Faso, to estimate location-specific resistance heterogeneity and exposure differences in bioassays versus experimental huts.
  • The approach aims to enhance malaria transmission models by providing a mechanistic understanding of insecticide resistance's public health impact.

  Abstract

  Malaria claims approximately 500,000 lives each year, and insecticide-treated nets (ITNs), which kill mosquitoes that transmit the disease, remain the most effective intervention. However, resistance to pyrethroids, the primary insecticide class used in ITNs, has risen dramatically in Africa, making it difficult to assess the current public health impact of pyrethroid-ITNs. Past work has modelled the relation between pyrethroid susceptibility measured in discriminating-dose susceptibility bioassays and ITN effectiveness in experimental hut trials. Here, we introduce a new predictive approach that accounts for heterogeneity in insecticide resistance within wild mosquito populations, for example, due to genetic variability, by incorporating data from newly recommended intensity-dose susceptibility bioassays. We fit our mathematical model to a comprehensive data set that combines discriminating dose bioassays from all over Africa, intensity dose bioassays from Burkina Faso, and concurrent experimental hut trials. Our analysis estimates location- and insecticide-specific variation in resistance heterogeneity in Burkina Faso and quantifies differences in insecticide exposure in bioassays and experimental huts. By providing a mechanistic understanding of these experimental data, our approach could be integrated into malaria transmission models to account for the public health impact of insecticide resistance detected by surveillance programmes.

  bioinformatics2026-02-12v4

• LineageSim: A Single-Cell Lineage Simulator with Fate-Aware Gene Expression

  Lai, H.; Sadria, M.

  AI Summary

  • LineageSim is introduced as a simulator that generates single-cell lineage data with fate-aware gene expression, addressing the limitation of existing simulators which lack long-range temporal dependencies.
  • The simulator includes latent signals in progenitor states that predict future cell fates, providing a benchmark for cell fate prediction algorithms.
  • Validation through logistic regression showed a 68.3% balanced accuracy, confirming the presence of predictive fate information in the simulated data.

  Abstract

  Single-cell lineage data paired with gene expression are critical for developing computational methods in developmental biology. Since experimental lineage tracing is often technically limited, robust simulations are necessary to provide the ground truth for rigorous validation. However, existing simulators generate largely Markovian gene expression, failing to encode the fate bias observed in real biological systems, where progenitor states exhibit early signatures of future commitment. Consequently, they cannot support the training and evaluation of computational methods that model long-range temporal dependencies. We present LineageSim, a generative framework that introduces fate-aware gene expression, where progenitor states carry latent signals of their descendants' terminal fates. This framework establishes a new class of benchmarks for cell fate prediction algorithms. We validate the presence of these temporal signals by training a logistic regression baseline, which achieves 68.3% balanced accuracy. This confirms that the generated data contain subtle but recoverable fate information, in contrast to existing simulators, where such predictive signals are systematically absent.

  bioinformatics2026-02-12v2

• Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI

  Stewart, R.; Laval, F.; Coppin, G.; Spirohn-Fitzgerald, K.; Tixhon, M.; Hao, T.; Calderwood, M. A.; Mort, M.; Cooper, D. N.; Vidal, M.; Radivojac, P.

  AI Summary

  • MutPred-PPI, a graph attention network, was developed to predict the interaction-specific effects of missense variants on protein-protein interactions using AlphaFold 3-based contact graphs and protein language model embeddings.
  • The model outperformed existing methods with AUCs of 0.85 for seen proteins and 0.72 for unseen proteins, showing strong generalizability.
  • Application to various datasets revealed distinct PPI perturbation patterns, with disease-associated variants showing enrichment for edgetic effects, particularly in cancer and neurodevelopmental disorders.

  Abstract

  Disruption of protein-protein interactions (PPIs) is a major mechanism of a variant's deleterious effect. Computational tools are needed to assess such variants at scale, yet existing predictors rarely consider loss of specific interactions, particularly when variants perturb binding interfaces without significantly affecting protein stability. To address this problem, we present MutPred-PPI, a graph attention network that predicts interaction-specific (edgetic) effects of missense variants by operating on AlphaFold 3-based protein complex contact graphs with protein language model embeddings imposed upon nodes. We systematically evaluated our model with stringent group cross-validation as well as benchmark data recently collected within the IGVF Consortium. MutPred-PPI outperformed all baseline methods across all evaluation criteria, achieving an AUC of 0.85 on seen proteins and 0.72 on previously unseen proteins in cross-validation, demonstrating strong generalizability despite scarce training data. To demonstrate biomedical relevance, we applied MutPred-PPI to variants from ClinVar, HGMD, COSMIC, gnomAD, and two de novo neurodevelopmental disorder-linked datasets. Disease-associated variants from ClinVar and HGMD showed strong enrichment for both quasi-null and edgetic effects, whereas population variants from gnomAD increasingly preserved interactions with higher allele frequencies. Notably, we observed a strong edgetic disruption signature in highly recurrent cancer variants from both the full COSMIC dataset and a subset of variants from oncogenes. Recurrent tumor suppressor gene variants and autism spectrum disorder-associated variants exhibited moderate quasi-null enrichment, whilst neurodevelopmental disorder-linked variants showed a weak edgetic disruption signature. These results indicate distinct PPI perturbation mechanisms across disease types and show that MutPred-PPI captures functionally relevant molecular effects of pathogenic variants.

  bioinformatics2026-02-12v2

• Reading TEA leaves for de novo protein design

  Pantolini, L.; Durairaj, J.

  AI Summary

  • The study explores de novo protein design using a 20-letter structure-inspired alphabet from protein language model embeddings to enhance Monte Carlo sampling efficiency.
  • This approach allows for rapid template-guided and unconditional design of protein sequences that meet in silico designability criteria, without relying on known homologues.
  • The method significantly reduces the time required for protein design, opening new avenues for therapeutic and industrial applications.

  Abstract

  De novo protein design expands the functional protein universe beyond natural evolution, offering vast therapeutic and industrial potential. Monte Carlo sampling in protein design is under-explored due to the typically long simulation times required or prohibitive time requirements of current structure prediction oracles. Here we make use of a 20-letter structure-inspired alphabet derived from protein language model embeddings to score random mutagenesis-based Metropolis sampling of amino acid sequences. This facilitates fast template-guided and unconditional design, generating sequences that satisfy in silico designability criteria without known homologues. Ultimately, this unlocks a new path to fast and de novo protein design.

  bioinformatics2026-02-12v2

• GeneReL: A Large Language Model-Powered Platform for Gene Regulatory Relationship Extraction with Community Curation

  Park, J.-S.; Ha, S.; Lee, Y.; Kang, Y. J.

  AI Summary

  • GeneReL is a platform developed to extract and curate gene regulatory relationships in Arabidopsis thaliana using large language models (LLMs) and community validation.
  • It uses a tiered pipeline with different LLMs for screening, extraction, and verification, and includes a five-step gene normalization process.
  • The platform has curated 13,710 interactions, with 86.8% unique compared to IntAct, and features interactive visualization and community voting for validation.

  Abstract

  Motivation: Gene regulatory networks provide fundamental insights into plant biology, yet extracting structured interaction data from scientific literature remains a significant bottleneck. Traditional manual curation cannot scale to meet the demands of modern research, while automated text mining approaches struggle with the complexity of gene nomenclature and relationship classification. Large language models offer promising capabilities for information extraction, but integrated platforms combining LLM extraction with community validation for plant regulatory databases remain scarce. Results: We developed GeneReL, an integrated platform combining LLM-based extraction with community-driven curation for gene regulatory networks in Arabidopsis thaliana. The system employs a tiered pipeline using Claude Haiku 4.5 for screening, Claude Sonnet 4 for extraction, and Claude Opus 4 for verification, along with a novel five-step gene normalization pipeline incorporating paper-text search and LLM-based disambiguation with UniProt annotations. The database contains 13,710 curated interactions across 51 relationship types, with 90.2% classified as high confidence based on linguistic certainty markers in source text. Comparison with IntAct reveals 86.8% of interactions are unique to our literature-derived database, demonstrating complementary coverage to existing resources. The web platform provides card-based browsing with voting capabilities, interactive network visualization using Cytoscape.js with locus-ID-based node consolidation, and administrative interfaces for curator review of ambiguous gene mappings.

  bioinformatics2026-02-12v2

• Deep learning-based non-invasive profiling of tumor transcriptomes from cell-free DNA for precision oncology

  Patton, R. D.; Netzley, A.; Persse, T. W.; Nair, A.; Galipeau, P. C.; Coleman, I. M.; Itagi, P.; Chandra, P.; Adil, M.; Vashisth, M.; Sayar, E.; Hiatt, J. B.; Dumpit, R.; Kollath, L.; Demirci, R. A.; Ghodsi, A.; Lam, H.-M.; Morrissey, C.; Iravani, A.; Chen, D. L.; Hsieh, A. C.; MacPherson, D.; Haffner, M. C.; Nelson, P. S.; Ha, G.

  AI Summary

  • The study introduces Triton for fragmentomic and nucleosome profiling of cfDNA and Proteus, a deep learning framework for predicting gene expression from standard depth whole genome sequencing of cfDNA.
  • Proteus accurately reproduced gene expression profiles from ctDNA in patient-derived xenografts, similar to RNA-Seq replicates.
  • When applied to patient cohorts, Proteus predicted expression of prognostic markers, phenotype markers, and therapeutic targets, demonstrating its utility in precision oncology.

  Abstract

  Circulating tumor DNA (ctDNA) profiling from liquid biopsies is increasingly adopted as a minimally invasive solution for clinical cancer diagnostic applications. Current methods for inferring gene expression from ctDNA require specialized assays or ultra-deep, targeted sequencing, which preclude transcriptome-wide profiling at single-gene resolution. Herein we jointly introduce Triton, a tool for comprehensive fragmentomic and nucleosome profiling of cell-free DNA (cfDNA), and Proteus, a multi-modal deep learning framework for predicting single gene expression, using standard depth (~30-120x) whole genome sequencing of cfDNA. By synthesizing fragmentation and inferred nucleosome positioning patterns in the promoter and gene body from Triton, Proteus reproduced expression profiles using pure ctDNA from patient-derived xenografts (PDX) with an accuracy similar to RNA-Seq technical replicates. Applying Proteus to cfDNA from four patient cohorts with matched tumor RNA-Seq, we show that the model accurately predicted the expression of specific prognostic and phenotype markers and therapeutic targets. As an analog to RNA-Seq, we further confirmed the immediate applicability of Proteus to existing tools through accurate prediction of gene pathway enrichment scores. Our results demonstrate the potential clinical utility of Triton and Proteus as non-invasive tools for precision oncology applications such as cancer monitoring and therapeutic guidance.

  bioinformatics2026-02-12v1

• Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors

  Hou, S.; Shen, H.; Zhang, Y.

  AI Summary

  • The study addresses biases in existing benchmarks for phase-separating protein (PSP) predictors due to taxonomic and intrinsic-disorder imbalances.
  • A new taxonomy-aware, disorder-matched benchmark was developed, revealing that PSP features vary by taxa but LLPS-associated shifts are conserved.
  • Benchmarking 20 PSP predictors showed taxon-dependent performance variations, with PSPs lacking IDRs being particularly challenging, suggesting the need for disorder-stratified evaluations.

  Abstract

  Background: Biomolecular condensates formed via liquid-liquid phase separation (LLPS) play vital roles in cellular organization and function. Computational prediction of phase-separating proteins (PSPs) is increasingly used to prioritize candidates at proteome scale, making robust, well-designed benchmarks essential for fair evaluation and iterative improvement of PSP predictors. Results: We first show that a recently released PSP benchmark is substantially confounded by the imbalances in taxonomic origin and intrinsic-disorder compositions between positive and negative sets, allowing predictors to achieve high apparent performance by exploiting non-LLPS shortcuts and obscuring their true ability to distinguish PSPs. To minimize these effects, we construct a taxonomy-aware, disorder-matched PSP benchmark. Using this benchmark, we find that absolute sequence and biophysical feature values of PSPs differ markedly across taxa, whereas LLPS-associated feature shifts relative to taxon-specific proteome backgrounds are comparatively conserved. Benchmarking twenty PSP predictors under this framework reveals pronounced taxon-dependent variation in performance. Moreover, PSPs lacking IDRs consistently constitute a more challenging regime across methods, motivating routine disorder-stratified evaluation. Conclusions: Our taxonomy-aware, disorder-matched benchmarking framework reduces shortcut-driven biases, enables more interpretable evaluation of PSP predictors, and provides guidance for developing models that capture transferable LLPS-associated signals rather than dataset- or taxon-specific shortcuts.

  bioinformatics2026-02-12v1

• Spatiotemporal cell type deconvolution leveraging tissue structure

  Lobo, M. M.; Zhang, Z.; Zhang, X.

  AI Summary

  • SpaDecoder is introduced as a method for cell type deconvolution in spatial transcriptomics, utilizing 3D tissue structure through an adaptive Gaussian kernel.
  • It accounts for variability in single-cell reference profiles and batch effects, enhancing the accuracy of cell type distribution estimation.
  • Comparisons and ablation tests demonstrate SpaDecoder's superior performance in leveraging 3D tissue structure for improved deconvolution across various datasets.

  Abstract

  Spot-based spatial transcriptomics (ST) captures aggregated transcriptomic profiles at spatial locations (spots) in tissue slices. Cell type deconvolution methods decode each spot and estimate the proportion of every cell type in the spot, necessary for uncovering spatial cell type distributions for further downstream analyses. Existing methods utilize cell type markers or reference transcriptomic (scRNA-seq) atlases at single cell (sc) resolution, or by aggregating profiles of identified cell types. However, current methods fail to effectively utilize the 3D tissue layout and single cell resolution reference. Some leverage 2D spatial organization assuming proximal spots are similar, which may be violated around boundaries or isolated cell types. We present SpaDecoder, a parallelized matrix factorization-based per-spot deconvolution method for multiple 3D spatial or temporal ST tissue slices effectively leveraging tissue structure with an adaptively inferred 3D neighborhood Gaussian kernel. We additionally account for variability in sc-reference profiles, along with batch effects. The mathematical framework of SpaDecoder allows it to be used for a range of downstream analyses. It can decode anteroposterior variability, impute gene expression, uncover putatively key tissue regions, identify colocalized cell types and predict spatio-temporal scRNA-seq cell locations. Ablation tests along with comparisons against other methods on various metrics, datasets, and scenarios, collectively show that SpaDecoder effectively harnesses 3D tissue structure and sc-reference profiles to improve cell type deconvolution. SpaDecoder is available at https://github.com/ZhangLabGT/spadecoder.

  bioinformatics2026-02-12v1

• Structure-guided analysis and prediction of human E2-E3 ligase pairing specificity

  Jarboe, B.; Dunbrack, R.

  AI Summary

  • This study addresses the specificity of E2-E3 ligase interactions in ubiquitination by analyzing experimental structures from the PDB and using AlphaFold to predict thousands of ubiquitin-E2-E3 ternary complexes.
  • A machine learning model was developed to predict functional E2-E3 pairings, enhancing the understanding of ubiquitination networks.
  • The model predicted E2 partners for 88 E3 ligases, including a novel pairing between UBE2C and RNF214, potentially linking them in hepatocellular carcinoma pathways.

  Abstract

  Protein ubiquitination, directed by specific E3 ligases, constitutes the primary cellular pathway for selective protein degradation. In addition to targeting proteins for degradation, ubiquitination can mediate new protein-protein interactions, and otherwise modulate protein function, thereby regulating key cellular processes such as DNA repair and immune responses. Recently, Proteolysis-Targeting Chimeras (PROTACs), and related proximity-inducing agents, have revealed the significant therapeutic potential of co-opting ubiquitin ligase activity to induce the selective degradation of disease-relevant proteins. Despite the biological and clinical significance of this pathway, fundamental gaps remain in our understanding of ubiquitination networks, particularly regarding the specificity of E2-E3 interactions and their substrate preferences. In this study, we leverage analysis of experimental structures in the Protein Data Bank (PDB) and use AlphaFold to generate structures of thousands of ubiquitin-E2-E3 ternary complexes. Using these predicted structures and complementary analyses, we develop a machine learning model to predict functional E2-E3 pairings, advancing our ability to map ubiquitination networks and providing structural insights into functional ubiquitin-E2-E3 complexes. We demonstrate the utility of our model by predicting E2 partners for 88 putative E3 ligases lacking any previously known E2 interactors. Notably, we identify a predicted pairing between UBE2C and RNF214, two proteins recently implicated in hepatocellular carcinoma separately but through interrelated pathways, suggesting a potential functional link mediated by RNF214-dependent ubiquitination in partnership with UBE2C. Additionally, we present our web resource, UbiqCore, making the E2-E3 pairing predictions and ternary complex structures available to the scientific community (https://dunbrack.fccc.edu/ubiqcore).

  bioinformatics2026-02-12v1

• A hyperparameter benchmark of VAE-based methods for scRNA-seq batch integration

  Kassab, M.; Maniero, L.; Beltrame, E.

  AI Summary

  • The study benchmarks hyperparameters of VAE-based methods (scVI, MrVI, LDVAE) for scRNA-seq batch integration, using 960 trainings across four datasets and two feature regimes.
  • Evaluations with scib metrics showed scVI excels in batch correction, LDVAE preserves biological structure better in some datasets, and MrVI is effective in multi-protocol settings but resource-intensive.
  • Results indicated that training with highly variable genes (HVGs) generally outperformed full-gene training, and higher latent dimensionality (>30) often balanced batch mixing with biological conservation.

  Abstract

  We present the first systematic benchmark of model architecture hyperparameters for variational autoencoder (VAE) methods for single-cell RNA-seq batch integration within scvi-tools, comparing scVI, MrVI, and LDVAE across four heterogeneous datasets under two feature regimes (all genes vs highly variable genes (HVGs)). We investigated 960 trainings (120 configurations) varying latent size and network depth/width, and evaluated with a standardized scib metric suite covering batch removal and biological conservation (Batch ASW, PCR batch, iLISI, graph connectivity, NMI, ARI, label ASW, isolated-label F1/ASW, cLISI, trajectory conservation), plus qualitative UMAP/t-SNE and PCA, random projection, and unintegrated baselines. Results show dataset-dependent trade-offs: scVI performs best overall via stronger batch correction; LDVAE can better preserve biological structure in some datasets; MrVI is stable and excels at batch correction in multi-protocol settings, but is more resource-intensive. HVG-only training generally outperforms full-gene training for all models. Hyperparameter analysis suggests moderate-to-high latent dimensionality (>30) often gives the best balance; sensitivity to latent size tracks dataset heterogeneity (tissues, labs, chemistries, gene coverage), with larger latents improving batch mixing but sometimes reducing biological conservation. We provide model- and dataset-specific guidelines for practical defaults and tuning of VAE-based integration in single-cell studies.

  bioinformatics2026-02-12v1

• Splicer: Phylogenetic Placement in Sub-Linear Time

  Markin, A.; Anderson, T. K.

  AI Summary

  • Splicer is developed to perform phylogenetic placement in sub-linear time, specifically O(√n), addressing the scalability issues of existing methods like pplacer and EPA-ng.
  • It decomposes the reference tree into "blobs" and constructs a scaffold tree, then places query sequences first on the scaffold and then within blobs for precision.
  • Splicer demonstrated high accuracy on an influenza A dataset and was applied to over 12 million SARS-CoV-2 genomes, scaling maximum-likelihood placement to large datasets.

  Abstract

  Motivation: Phylogenetic placement is an established approach for rapidly classifying new genetic sequences and updating a phylogeny without fully recomputing it. Popular maximum- likelihood placement methods, such as pplacer and EPA-ng, tend to struggle computationally when the size of the reference tree increases to tens or hundreds of thousands of sequences. As a more scalable alternative, distance-based and parsimony-based placement methods were introduced such as UShER. These methods, in principle, scale linearly as the size of the reference tree grows. However, as the scale of genetic and genomic sequences continues to grow nearly exponentially, developing algorithms that can perform placement in sub-linear time while maintaining accuracy becomes more crucial. Results: Here, we develop Splicer, the first such algorithm that can perform placement in guaranteed O({surd}n) time. To achieve this performance, Splicer first decomposes the original reference tree into "blobs" and constructs a phylogenetic scaffold tree linking representatives from different blobs. Every blob in such decomposition has at most c{surd}n taxa, and the scaffold tree has at most 4/c*{surd}n leaves, where c is any constant. Then, given the query sequences for placement, they are first placed onto a scaffold tree using pplacer or EPA-ng, and then placed more precisely within the respective blobs. We demonstrate the high accuracy of Splicer on an empirical influenza A virus dataset that has sparse coverage due to limited genomic surveillance. We also show that Splicer can, for the first time, apply maximum-likelihood placement to COVID-19 pandemic-scale data using a dataset with over 12 million SARS-CoV-2 reference genomes. Splicer scales the highly accurate maximum-likelihood approaches implemented in pplacer and EPA-ng to trees with millions of taxa and eliminates the necessity to curate and subsample genomic datasets for real-time classifications. Availability and implementation: Splicer tool and source code are freely available at https://github.com/flu-crew/splicer.

  bioinformatics2026-02-12v1

• tensorOmics: Data integration for longitudinal omics data using tensor factorisation

  Kodikara, S.; Lu, B.; Wang, S.; Le Cao, K.-A.

  AI Summary

  • The study introduces tensorOmics, a framework using tensor factorization for integrating longitudinal multi-omics data, addressing the limitations of traditional matrix-based methods.
  • tensorOmics includes both supervised and unsupervised methods for single and multi-omic analyses, preserving temporal structures and integrating phenotypic responses.
  • Validation through case studies showed tensorOmics effectively differentiates treatment groups, captures time-dependent molecular signatures, and reveals coordinated responses across omics layers.

  Abstract

  Multi-omics studies capture comprehensive molecular profiles across biological layers to understand complex biological processes. A central challenge is integrating information across heterogeneous data types to identify coordinated molecular responses, particularly when measurements are collected longitudinally. Traditional integration methods can be broadly classified as unsupervised (exploring patterns without phenotypic information) or supervised (discriminating between groups or predicting outcomes). These approaches rely predominantly on matrix-based techniques that concatenate or project data into lower-dimensional spaces. However, matrix methods struggle with longitudinal data, as flattening multi-dimensional structures obscures temporal trajectories and violates independence assumptions. Tensor-based methods preserve the natural multi-way structure of longitudinal data but existing approaches are predominantly unsupervised, cannot incorporate phenotypic responses for discriminant analysis, and lack frameworks for integrating multiple omics layers. We present tensorOmics, a comprehensive framework for longitudinal omics analysis using tensor factorisation. The framework encompasses supervised and unsupervised methods for both single-omic (tensor PCA, tensor PLS discriminant analysis) and multi-omic settings (tensor PLS, block tensor PLS, block tensor PLS discriminant analysis). This unified approach captures coordinated responses across biological layers while preserving temporal structure. We validated tensorOmics through three case studies: antibiotic perturbation experiments, anaerobic digestion systems, and fecal microbiota transplantation. These applications demonstrate tensorOmics differentiates treatment groups, captures time-dependent molecular signatures, and reveals multi-layer coordinated responses that cross-sectional methods miss.

  bioinformatics2026-02-12v1

• Investigating Enzyme Function by Geometric Matching of Catalytic Motifs

  Hackett, R. E.; Riziotis, I. G.; Larralde, M.; Ribeiro, A. J. M.; Zeller, G.; Thornton, J.

  AI Summary

  • Developed a method using geometric matching to detect catalytic features in protein structures, utilizing a library of 6780 3D coordinate sets from 762 enzyme mechanisms.
  • The approach was validated on 3751 high-quality experimental enzyme structures and predicted human proteome structures, showing higher sensitivity in identifying enzyme homology than sequence or 3D-structure-based methods.
  • This method identifies structural similarities in catalytic sites of divergent enzymes, offering insights into enzyme function evolution, and is available as the Python module Enzyme Motif Miner.

  Abstract

  The rapidly growing universe of predicted protein structures offers opportunities for data driven exploration but requires computationally scalable and interpretable tools. We developed a method to detect catalytic features in protein structures, providing insights into enzyme function and mechanism. A library of 6780 3D coordinate sets describing enzyme catalytic sites, referred to as templates, has been collected from manually curated examples of 762 enzyme catalytic mechanisms described in the Mechanism and Catalytic Site Atlas. For template searching we optimised the geometric-matching algorithm Jess. We implemented RMSD and residue orientation filters to differentiate catalytically informative matches from spurious ones. We validated this approach on a non-redundant set of high quality experimental (n=3751, <40% amino acid identity) enzyme structures with well annotated catalytic sites as well as predicted structures of the human proteome. We show matching catalytic templates solely on structure is more sensitive than sequence- and 3D-structure-based approaches in identifying homology between distantly related enzymes. Since geometric matching does not depend on conserved sequence motifs or even common evolutionary history, we are able to identify examples of structural active site similarity in highly divergent and possibly convergent enzymes. Such examples make interesting case studies into the evolution of enzyme function. Though not intended for characterizing substrate-specific binding pockets, the speed and knowledge-driven interpretability of our method make it well suited for expanding enzyme active-site annotation across large predicted proteomes. We provide the method and template library as a Python module, Enzyme Motif Miner, at https://github.com/rayhackett/enzymm.

  bioinformatics2026-02-12v1

• MOSAIC: A Spectral Framework for Integrative Phenotypic Characterization Using Population-Level Single-Cell Multi-Omics

  Lu, C.; Kluger, Y.; Ma, R.

  AI Summary

  • MOSAIC is a spectral framework designed to analyze population-scale single-cell multi-omics data by learning a joint feature x sample embedding, addressing limitations of existing cell-centric or feature-centric methods.

  • It enables Differential Connectivity (DC) analysis, revealing regulatory network changes like the rewiring of proliferation programs in activated T cells post-vaccination, despite unchanged gene expression.

  • Applied to an HIV+ cohort, MOSAIC identified a novel stress-driven neuronal subtype with increased protein synthesis, highlighting its utility in discovering biologically significant sample subgroups.

  Abstract

  Population-scale single-cell multi-omics offers unprecedented opportunities to link molecular variation to human health and disease. However, existing methods for single-cell multi-omics analysis are either cell-centric, prioritizing batch-corrected cell embeddings that neglect feature relationships, or feature-centric, imposing global feature representations that overlook inter-sample heterogeneity. To address these limitations, we present MOSAIC, a spectral framework that learns a high-resolution feature x sample joint embedding from population-scale single-cell multi-omics data. For each individual, MOSAIC constructs a sample-specific coupling matrix capturing complete intra- and cross-modality feature interactions, then projects these into a shared latent space via spectral decomposition. The joint feature x sample embedding defines each feature's connectivity profile per sample, enabling two key downstream applications. First, MOSAIC introduces Differential Connectivity (DC) analysis, which identifies features exhibiting regulatory network rewiring across conditions even when their expression or abundance remains unchanged. Applied to a CITE-seq vaccination cohort, MOSAIC revealed rewiring of proliferation programs in activated T cells, highlighting a functional shift in STAT5B despite stable expression. Second, MOSAIC enables identification of biologically meaningful sample subgroups by isolating coherent multimodal feature modules. Applied to an HIV+ prefrontal cortex cohort, MOSAIC uncovered a novel stress-driven neuronal subtype within HIV+ samples characterized by elevated protein synthesis without chromatin accessibility changes. MOSAIC provides a general-purpose framework for systems-level phenotypic characterization, offering novel biological insights from population-scale multi-omic studies.

  bioinformatics2026-02-12v1

• SLECA: a single-cell atlas of systemic lupus erythematosus enabling rare cell discovery using graph transformer

  Duan, M.; Shi, Y.; Tian, H.; Wu, Q.; Wang, X.; Liu, B.

  AI Summary

  • The study introduces SLECA, a large-scale single-cell atlas for systemic lupus erythematosus (SLE), using a graph-transformer framework to identify rare immune cell populations.
  • SLECA integrates 366 samples, identifying 54 cell types, including disease-relevant rare populations like double-negative T cells (DNTs), which correlate with clinical severity.
  • In silico perturbation showed that transcription factors JUN and EGR1 can reprogram DNTs, suggesting potential therapeutic targets in SLE.

  Abstract

  Systemic lupus erythematosus (SLE) is a highly heterogeneous autoimmune disease with complex immune and molecular dysregulation. While rare immune cell populations are increasingly recognized as critical drivers of disease pathogenesis and progression, the lack of sufficiently powered, comprehensive single-cell atlases has limited their systematic identification and characterization. To address this gap, we present SLECA, the first large-scale single-cell atlas of SLE, enabled by a novel graph-transformer framework for the interpretable discovery and analysis of disease-relevant rare cell populations. SLECA integrates 366 single-cell samples with standardized clinical and biological metadata, providing a comprehensive and analytically unified atlas of systemic lupus erythematosus. By enabling scalable integration and interpretable analysis, SLECA resolves 54 distinct cell types, including rare populations with critical disease relevance. Notably, we identify double-negative T cells (DNTs) as a disease-expanded population whose abundance correlates with clinical severity. Through in silico perturbation, we demonstrate that key transcription factors, specifically JUN and EGR1, can reprogram DNT cells toward conventional T-cell phenotypes, highlighting actionable regulatory vulnerabilities in SLE.

  bioinformatics2026-02-12v1

• Decoding the Molecular Language of Proteins with Evolla

  Zhou, X.; Han, C.; Zhang, Y.; Du, H.; Tian, J.; Su, J.; Liu, R.; Zhuang, K.; Jiang, S.; Gitter, A.; Liu, L.; Li, H.; Wu, M.; You, S.; Yuan, Z.; Ju, F.; Zhang, H.; Zheng, W.; Dai, F.; Zhou, Y.; Tao, Y.; Wu, D.; Shao, Z.; Liu, Y.; Lu, H.; Yuan, F.

  AI Summary

  • Evolla is an interactive protein-language model trained on 546 million protein-text pairs, designed to interpret protein function through natural language queries.
  • It outperforms general large language models in functional inference and matches state-of-the-art supervised models in zero-shot performance.
  • Applications include identifying eukaryotic signature proteins in Asgard archaea and discovering a novel PET hydrolase, PsPETase, validated for plastic degradation.

  Abstract

  Proteins, nature's intricate molecular machines, are the products of billions of years of evolution and play fundamental roles in sustaining life. Yet, deciphering their molecular language - understanding how sequences and structures encode biological functions - remains a cornerstone challenge. Here, we introduce Evolla, an interactive protein-language model designed to transcend static classification by interpreting protein function through natural language queries. Trained on 546 million protein-text pairs and refined via Direct Preference Optimization, Evolla couples high-dimensional molecular representations with generative semantic decoding. Benchmarking establishes Evolla's superiority over general large language models in functional inference, demonstrates zero-shot performance parity with the state-of-the-art supervised model, and exposes remote functional relationships invisible to conventional alignment. We validate Evolla through two distinct applications: identifying candidate eukaryotic signature proteins in Asgard archaea, with functional Vps4 homologs validated via yeast complementation; and interactively discovering a novel deep-sea polyethylene terephthalate (PET) hydrolase, PsPETase, confirmed to degrade plastic films. These results position Evolla not merely as a predictor, but as a generative engine capable of complex hypothesis formulation, shifting the paradigm from static annotation to interactive, actionable discovery. The Evolla online service is available at <a href="http://www.chat-protein.com/">http://www.chat-protein.com/</a>.

  bioinformatics2026-02-11v4

• scPRINT-2: Towards the next-generation of cell foundation models and benchmarks

  Kalfon, J.; Peyre, G.; Cantini, L.

  AI Summary

  • The study introduces scPRINT-2, a single-cell Foundation Model pre-trained on 350 million cells from 16 organisms, aiming to enhance performance in cell biology tasks.
  • scPRINT-2 was developed using an additive benchmark across various tasks, leading to state-of-the-art results in expression denoising, cell embedding, and cell type prediction.
  • The model's capabilities include generative functions like expression imputation and counterfactual reasoning, with demonstrated generalization to new modalities and organisms.

  Abstract

  Cell biology has been booming with foundation models trained on large single-cell RNA-seq databases, but benchmarks and capabilities remain unclear. We propose an additive benchmark across a gymnasium of tasks to discover which features improve performance. From these findings, we present scPRINT-2, a single-cell Foundation Model pre-trained across 350 million cells and 16 organisms. Our contributions in pre-training tasks, tokenization, and losses made scPRINT-2 state-of-the-art in expression denoising, cell embedding, and cell type prediction. Furthermore, with our cell-level architecture, scPRINT-2 becomes generative, as demonstrated by our expression imputation and counterfactual reasoning results. Finally, thanks to our pre-training database, we uncover generalization to unseen modalities and organisms. These studies, together with improved abilities in gene embeddings and gene network inference, place scPRINT-2 as a next-generation cell foundation model.

  bioinformatics2026-02-11v3

• Verifying LLM-extracted text with token alignment

  Booeshaghi, A. S.; Streets, A. M.

  AI Summary

  • This study investigates improving the verification of text extracted by large language models (LLMs) by aligning extracted text with the original source, focusing on discontiguous phrases.
  • Using LLM-specific tokenizers and ordered alignment algorithms, the approach improved alignment accuracy by about 50% compared to traditional word-level tokenization.
  • The study introduced the BOAT and BIO-BOAT datasets for testing, demonstrating that ordered alignment is the most practical method for this task.

  Abstract

  Large language models excel at text extraction, but they sometimes hallucinate. A simple way to avoid hallucinations is to remove any extracted text that does not appear in the original source. This is easy when the extracted text is contiguous (findable with exact string matching), but much harder when it is discontiguous. Techniques for finding discontiguous phrases depend heavily on how the text is split-i.e., how it is tokenized. In this study, we show that splitting text along subword boundaries, with LLM-specific tokenizers, and aligning extracted text with ordered alignment algorithms, improves alignment by about 50% compared to word-level tokenization. To demonstrate this, we introduce the Berkeley Ordered Alignment of Text (BOAT) dataset, a modification of the Stanford Question Answering Dataset (SQuAD) that includes non-contiguous phrases, and BIO-BOAT a biomedical variant built from 51 bioRxiv preprints. We show that text-alignment methods form a partially ordered set, and that ordered alignment is the most practical choice for verifying LLM-extracted text. We implement this approach in taln, which enumerates ordinal subword alignments.

  bioinformatics2026-02-11v2

• Multi-compartment spatiotemporal metabolic modeling of the chicken gut guides the design of dietary interventions

  Utkina, I.; Alizadeh, M.; Sharif, S.; Parkinson, J.

  AI Summary

  • The study developed a multi-compartment, spatiotemporally resolved metabolic model of the chicken gut to understand how diet influences microbial metabolism.
  • The model identified cellulose, starch, and L-threonine as effective dietary supplements for enhancing short-chain fatty acid production, particularly butyrate, through in silico screening.
  • Validation through a feeding trial confirmed model predictions, highlighting the importance of microbial community composition in metabolic outcomes.

  Abstract

  Understanding how diet shapes microbial metabolism along the gastrointestinal tract is essential for improving poultry gut health and reducing reliance on antibiotic growth promoters. Yet dietary interventions often yield inconsistent outcomes because their efficacy depends on baseline conditions, including diet composition and microbiota structure. To address this, we developed the first multi-compartment, spatiotemporally resolved metabolic model of the chicken gastrointestinal tract. Our six-compartment framework integrates avian-specific physiological features including bidirectional flow, feeding-fasting cycles, and compartment-specific environmental parameters. The model captured distinct metabolic specialization along the gut, with upper compartments enriched for biosynthetic pathways and lower compartments specialized for fermentation. Systematic in silico screening of 34 dietary supplements revealed context-dependent metabolic responses and identified cellulose, starch, and L-threonine as robust enhancers of short-chain fatty acid production. A controlled feeding trial validated key predictions, particularly for butyrate, and integrating trial-specific microbial community data substantially improved prediction accuracy for several metabolites. Our findings demonstrate that community composition is a major driver of metabolic outcomes and underscore the need for context-specific modeling. Our framework provides a mechanistic platform for rational dietary intervention design and is broadly adaptable to other animal or human gastrointestinal systems.

  bioinformatics2026-02-11v2

• Prediction of Antibody Non-Specificity using Protein Language Models and Biophysical Parameters

  Sakhnini, L. I.; Beltrame, L.; Fulle, S.; Sormanni, P.; Henriksen, A.; Lorenzen, N.; Vendruscolo, M.; Granata, D.

  AI Summary

  • This study predicts antibody non-specificity using protein language models (PLMs) and biophysical descriptors, focusing on human and mouse antibody data.
  • The best prediction model, ESM 1v LogisticReg, achieved 71% accuracy in 10-fold cross-validation, highlighting the heavy variable domain's importance.
  • Biophysical analysis revealed the isoelectric point as a significant factor in non-specificity, with implications for developing therapeutic antibodies and nanobodies.

  Abstract

  The development of therapeutic antibodies requires optimizing target binding affinity and pharmacodynamics, while ensuring high developability potential, including minimizing non-specific binding. In this study, we address this problem by predicting antibody non-specificity by two complementary approaches: (i) antibody sequence embeddings by protein language models (PLMs), and (ii) a comprehensive set of sequence-based biophysical descriptors. These models were trained on human and mouse antibody data from Boughter et al. (2020) and tested on three public datasets: Jain et al. (2017), Shehata et al. (2019) and Harvey et al. (2022). We show that non-specificity is best predicted from the heavy variable domain and heavy-chain complementary variable regions (CDRs). The top performing PLM, a heavy variable domain-based ESM 1v LogisticReg model, resulted in 10-fold cross-validation accuracy of up to 71%. Our biophysical descriptor-based analysis identified the isoelectric point as a key driver of non-specificity. Our findings underscore the importance of biophysical properties in predicting antibody non-specificity and highlight the potential of protein language models for the development of antibody-based therapeutics. To illustrate the use of our approach in the development of lead candidates with high developability potential, we show that it can be extended to therapeutic antibodies and nanobodies.

  bioinformatics2026-02-11v2

• A multi-component power-law penalty corrects distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions

  Schlegel, L.; Gomez-Cano, F.; Marand, A. P.; Johannes, F.

  AI Summary

  • The study addresses the overestimation of long-range interactions in single-cell co-accessibility and deep learning predictions by introducing a distance-based penalty function.
  • Using Hi-C data from maize, rice, and soybean, the researchers developed tissue-specific and global consensus penalties based on multi-regime power-law exponents.
  • Applying these corrections to scATAC-seq data reduced long-range false positives by 73% with tissue-specific penalties and 66% with global consensus, aligning predictions more closely with Hi-C data.

  Abstract

  Scalable proxies for 3D genome contacts - such as single-cell co-accessibility and deep learning predictions - have emerged as powerful alternatives to chromatin capture-based methods, but predictions systematically overestimate long-range interactions. Here we show how to correct this bias using distance-based penalty functions informed by Gaussian mixture modeling and polymer-physics scaling. Using Hi-C datasets from maize, rice, and soybean, we derive tissue-specific and global consensus penalties parameterized by multi-regime power-law exponents. Applying these corrections to scATAC-seq co-accessibility scores improves their distance profiles in concordance with Hi-C and reduces long-range false positives by an average of 73% with tissue-specific penalties and 66% with the global consensus. We provide open-source code and fitted parameters to support adoption in maize, rice, and soybean.

  bioinformatics2026-02-11v2

• ModSeqR: An R package for efficient analysis of modified nucleotide data

  Zimmerman, H. E.; Moore, J.; Miller, R. H.; Stirland, I.; Jenkins, A.; Saito, E.; Jenkins, T.; Hill, J. T.

  AI Summary

  • The study addresses the computational challenges in analyzing large datasets from long-read technologies for DNA methylation.
  • They introduce the CH3 file format, reducing file sizes by over 95%, and the ModSeqR R package, which uses this format and a database backend for efficient epigenetic analyses.
  • These tools facilitate high-throughput methylation analysis with reduced computational demands.

  Abstract

  DNA methylation regulates a wide range of biological processes, including gene expression, disease progression, and cell identity. Long-read technologies now enable more comprehensive and accurate methylome analyses than ever before, but they are hindered by the computational resources needed to analyze the massive datasets. Here, we present the CH3 file format, which aids data storage and transfer by reducing file sizes by more than 95%, and the ModSeqR R package, which builds on the CH3 format and a database backend to enable a broad range of epigenetic analyses. Together, these tools enable high-throughput methylation analysis while minimizing computational resource requirements.

  bioinformatics2026-02-11v2

• Augmented prediction of multi-species protein--RNA interactions using evolutionary conservation of RNA-binding proteins

  He, J.; Zhou, T.; Hu, L.-F.; Jiao, Y.; Wang, J.; Yan, S.; Jia, S.; Chen, Q.; Zhu, W.; Zhang, J.; Jia, M.; Li, Y.; Wang, X.; Wang, Y.; Yang, Y. T.; Sun, L.

  AI Summary

  • The study introduces MuSIC, a deep learning framework to predict multi-species RBP--RNA interactions by using evolutionary conservation across 11 species.
  • MuSIC outperforms existing methods, accurately predicting RBP-binding peaks with higher confidence in closely related species.
  • The framework also quantifies the impact of genetic variants on RBP binding, validated experimentally, revealing disruptions in ubiquitination pathways.

  Abstract

  RNA-binding proteins (RBPs) play critical roles in gene expression regulation. Recent studies have begun to detail the RNA recognition mechanisms of diverse RBPs. However, given the array of RBPs studied so far, it is implausible to experimentally profile RBP-binding peaks for hundreds of RBPs in multiple non-model organisms. Here, we introduce MuSIC (Multi-Species RBP--RNA Interactions using Conservation), a deep learning-based framework for predicting cross-species RBP--RNA interactions by leveraging label smoothing and evolutionary conservation of RBPs across 11 diverse species ranging from human to yeast. MuSIC outperforms state-of-the-art computational methods, and provides predicted RBP-binding peaks across species with high accuracy. The prediction confidence is higher in the closely related species, partially due to the RBP conservation patterns. Finally, the effects of homologous genetic variants on RBP binding can be computationally quantified across species, followed by experimental validations. The target transcripts with disrupted binding events are enriched with the ubiquitination-associated pathways. To summarize, MuSIC provides a useful computational framework for predicting RBP--RNA interactions cross-species and quantifying the effects of genetic variants on RBP binding, offering novel insights into the RBP-mediated regulatory mechanisms implicated in human diseases.

  bioinformatics2026-02-11v2

• Large-scale quantum computing framework enhances drug discovery in multiple stages

  Wen, K.; Zha, J.; Chen, S.; Zhong, J.; Yuan, L.; Cui, Y.; Shi, X.; Qin, W.; Lan, X.; Liu, Y.; Yang, X.; Qin, H.; Li, M.; Guo, P.; Xiao, Q.; Wu, T.; Zhou, Y.; Cao, C.; Ning, S.; Wu, C.; Gao, Q.; He, H.; Ma, Y.; An, Z.; Liu, X.; Chen, Y.; Zheng, Z.; Wei, H.; Ma, Y.; Zhang, J.

  AI Summary

  • The study improved the stability of a 2000-node Coherent Ising Machine (CIM), named QBoson-CPQC-3Gen, through enhanced vibration isolation and temperature control, allowing stable solutions for over an hour.
  • A CIM-based framework was developed for computer-aided drug discovery (CADD), incorporating graph-based encoding for tasks like allosteric site detection and protein-peptide docking.
  • This framework outperformed heuristic algorithms in speed and accuracy, identifying 2 novel druggable sites and bioactive compounds for 6 targets, validated through in vitro, in-cell, and crystallographic methods.

  Abstract

  Coherent Ising machines (CIMs) excel at solving large-scale combinational optimization problems (COPs), but their insufficient long-term stability has hindered their applications in compute-intensive tasks like computer-aided drug discovery (CADD). By improving fiber vibration isolation and temperature control system, we have implemented a 2000-node CIM named QBoson-CPQC-3Gen achieving stable solutions over one hour on large-scale COPs. Graph-based encoding schemes were further introduced to realize a CIM-based CADD workflow including allosteric site detection, protein-peptide docking and intermolecular similarity calculation. CIM-based methods demonstrated superior speed and accuracy than heuristic algorithms. Especially, QBoson-CPQC-3Gen identified 2 novel druggable sites and bioactive compounds for 6 targets, which were further validated in vitro, in-cell and by crystal structures. Our contributions established a quantum-computing framework for multi-stage drug discovery, representing a significant advancement in both quantum computing applications and pharmaceutical research.

  bioinformatics2026-02-11v1

• VC-RDAgent: An efficient rare disease diagnosis agent via virtual case construction informed by hybrid statistical-metric and hyperbolic-semantic prioritization

  Liu, Y.; Li, H.; Jiang, P.; Wu, L.; Xie, Z.; Ning, C.; Kong, X.; Wang, Y.; Zhang, X.; Huang, Z.

  AI Summary

  • VC-RDAgent addresses the challenge of rare disease diagnosis by creating virtual standardized cases, avoiding the need for real-world patient data due to its scarcity and privacy issues.
  • The system uses VC-Ranker, which combines statistical-metric measures with hyperbolic-semantic embeddings to generate high-fidelity virtual references from knowledge bases.
  • Testing on four datasets showed VC-RDAgent improved Top-1 hit rates by 8.7% to 85.9%, with VC-Ranker achieving a Top-10 hit rate of 0.819, surpassing previous methods by 6%.

  Abstract

  While Large Language Models (LLMs) have shown promise in clinical decision support, current Retrieval-Augmented Generation (RAG) paradigms face a fundamental bottleneck in rare disease diagnosis: the scarcity, privacy restrictions, and extreme heterogeneity of real-world patient records. This reliance on sparse or inaccessible data leads to a severe "retrieval mismatch," where the lack of high-quality reference cases causes diagnostic performance to degrade sharply. To break this deadlock, we propose VC-RDAgent, a privacy-preserving and offline-capable framework that decouples diagnostic reasoning from sensitive real-world records by synthesizing virtual standardized cases. The system is powered by VC-Ranker, a multi-dimensional engine that integrates statistical-metric measures with hyperbolic-semantic embeddings to capture deep hierarchical ontology relationships. This approach allows for the dynamic generation of high-fidelity virtual references directly from authoritative knowledge bases. Extensive benchmarking across four diverse datasets demonstrates that VC-RDAgent effectively functions as a "performance equalizer." It boosts average Top-1 hit rates by 8.7% to 85.9% over zero-case baselines, enabling lightweight open-source models to rival frontier commercial systems. Notably, VC-Ranker alone achieved an aggregate Top-10 hit rate of 0.819, outperforming prior state-of-the-art methods by 6%. By eliminating the dependency on real-time web retrieval and private case sharing, VC-RDAgent provides a scalable, robust, and clinically deployable solution to shorten the diagnostic odyssey, which is made accessible through an intuitive, chat-based web application https://rarellm.service.bio-it.tech/rdagent/.

  bioinformatics2026-02-11v1

• A global survey of System Biology-based predictions of gene-rare disease associations to enhance new diagnoses

  Benitez, Y.; Uria-Regojo, G.; Minguez, P.

  AI Summary

  • The study aimed to enhance rare disease diagnosis by predicting gene-disease associations using a global, network-based approach.
  • By analyzing functional neighborhoods of known disease genes, the research identified 192 genes linked to single diseases and 251 genes associated with specific disease classes.
  • These findings were used to develop a gene-disease specificity score to improve variant prioritization in genetic diagnostics.

  Abstract

  In rare disease diagnosis, described genotype-phenotype associations are evaluated first. In the absence of strong evidence, WES and WGS provide hundred to million other genetic variants, most poorly annotated, that need to be prioritized. While several in silico approaches leverage existing gene-disease knowledge to predict novel associations, doing so in isolation can hide how different genes are represented across other predictions. We hypothesize that a global perspective, accounting for differences in the knowledge accumulated in the gene collections, can refine predictions. Using a network-based algorithm, we explored functional neighborhoods of known disease-associated genes to predict novel candidates for over 200 rare diseases. A global analysis of gene and protein family behavior across predictions identified genes and functions broadly associated with multiple conditions, 192 genes linked to a single disease and 251 genes functionally associated with specific classes of rare diseases. These findings are integrated into a gene-disease specificity score, aimed at enhancing variant prioritization and guiding geneticists in advancing candidate genes toward functional validation.

  bioinformatics2026-02-11v1

• SIPdb: A stable isotope probing database and analytical dashboard for linking amplicon sequences to microbial activity using a reverse ecology approach

  Trentin, A. B.; Simpson, A.; Kimbrel, J. A.; Blazewicz, S. J.; Wilhelm, R. C.

  AI Summary

  • SIPdb is introduced as a SQLite database and RShiny dashboard for integrating stable isotope probing (SIP) data with microbial sequence data, standardizing 22 studies across 21 isotopolog substrates.
  • The database uses a standardized pipeline to analyze SIP data, identifying over 42,000 unique amplicon sequence variants as isotope incorporators across 62 phyla, with ALDEx2 showing the highest specificity in performance.
  • Validation showed SIPdb recovered 70.1% of reported incorporator taxa, and reanalysis of a non-SIP study identified additional candidate taxa for 1,4-dioxane degradation, enhancing ecological interpretation in microbiome research.

  Abstract

  Stable isotope probing (SIP) provides a powerful means to connect microbial sequence data with diverse metabolic activities, but the lack of a framework for SIP-derived data has limited its integration into broader strategies for ecological inference. Here, we introduce the SIPdb, an extensible SQLite database of curated nucleic acid SIP experiments (also in phyloseq format) paired with an interactive RShiny dashboard for analysis and visualization. The initial release compiles 22 studies covering 21 isotopolog substrates across diverse environments, with data standardized using the MISIP metadata standard. In creating the SIPdb, we have provided a standardized pipeline that accommodates the three most common SIP gradient fractionation strategies (binary, multi-fraction, and density-resolved), two isotope incorporator designation strategies (fixed- and sliding-window), and four complementary differential abundance methods (DESeq2, edgeR, limma-voom, and ALDEx2). Using our pipeline, we identified more than 42,000 unique amplicon sequence variants as isotope incorporators across 62 phyla. Benchmarking with synthetic datasets demonstrated consistent performance across incorporator designation strategies, with ALDEx2 providing the highest specificity. Validation against original publications showed that, on average, SIPdb recovered 70.1% of author-reported incorporator taxa, with discrepancies arising from differences in phylotyping or classification approaches. Finally, our reanalysis of a non-SIP study of 1,4-dioxane degradation showed how SIPdb can both validate known degraders and uncover additional candidate taxa involved in community metabolism. The SIPdb establishes a scalable platform for reverse ecology, enabling hypothesis generation, cross-study meta-analysis, and linking taxa to metabolic processes, while serving as an open, extensible resource to accelerate ecological interpretation in microbiome research.

  bioinformatics2026-02-11v1

• DIA-CLIP: a universal representation learning framework for zero-shot DIA proteomics

  Liao, Y.; Wen, H.; E, W.; Zhang, W.

  AI Summary

  • The study introduces DIA-CLIP, a framework for zero-shot DIA proteomics that uses universal cross-modal representation learning to overcome the limitations of semi-supervised, run-specific training in DIA-MS analysis.
  • DIA-CLIP employs a dual-encoder contrastive learning approach to align peptide sequences with spectral features, enabling high-precision peptide-spectrum match inference without run-specific retraining.
  • Evaluations show DIA-CLIP increases protein identification by up to 45% and reduces false discovery rates by 17%, demonstrating superior performance over existing tools in diverse proteomic applications.

  Abstract

  Data-independent acquisition mass spectrometry (DIA-MS) has established itself as a cornerstone of proteomic profiling and large-scale systems biology, offering unparalleled depth and reproducibility.has emerged as an indispensable cornerstone of quantitative proteomics[3.1].[4.1] However, Ccurrent DIA analysis frameworks, however,identification [5.1]pipelines require semi-supervised training within each run rely on semi-supervised, run-specific training[6.1] for peptide-spectrum match (PSM) re-scoring. This approach is prone to often leads to[7.1] overfitting and lacks generalizability across diverse heterogeneous species and experimental conditionss[8.1][9.1]. Here, we present DIA-CLIP, a pre-trained modelfoundation-model-inspired[10.1] framework that shiftings the DIA analysis paradigm from semi-supervised trainingrun-specificper-file refinement to universal cross-modal representation learning.. BBy integrating dual-encoder contrastive learning framework with encoder-decoder architecture[11.1], DIA-CLIP establishes a unified cross-modal representation for peptides and corresponding spectral features, achievingemploying supervised contrastive learning on large-scale PSM datasets, DIA-CLIP aligns peptide sequences with spectral signals within a shared latent space. This approach enables high-precision, zero-shot PSM inference, eliminating the requirement for run-specific re-training or fine-tuning.[12.1] Extensive evaluations across diverse benchmarks demonstrate that DIA-CLIP consistently outperforms state-of-the-art tools, yielding up to a 45% increase in protein identification while achieving a 12% reduction in entrapment identifications.DIA-CLIP is validated to We demonstrate that DIA-CLIP consistently outperforms state-of-the-art tools across diverse benchmarks, increasing proteome coverage without compromising by 45% for single cell proteomics and reducing false discovery rates by 17% under challenging entrapment experimentfalse discovery rates under entrapment experiment[13.1]. Moreover, DIA-CLIP holds immense potential for diverse practical applications, such as single-cell and spatial proteomics, where its enhanced identification depth facilitates the discovery of novel biomarkers and the elucidates of intricate cellular mechanisms.

  bioinformatics2026-02-11v1

• BRIDGE: Biological Antimicrobial Resistance Inference viaDomain-Knowledge Graph Embeddings

  Iyer, A.; Kazeem, Y.; Kafaie, S.; Rajabi, E.

  AI Summary

  • The study introduces BRIDGE, a knowledge graph-based framework to enhance the prediction of antimicrobial resistance genes (ARGs) by integrating gene neighbourhood information and protein-protein interactions.
  • Focused on Klebsiella pneumoniae and Escherichia coli, BRIDGE uses data from CARD, STRING, and DrugBank to construct a knowledge graph.
  • Applying graph embedding models and deep neural networks, BRIDGE achieved a classification accuracy of up to 97% in predicting novel AMR links, demonstrating improved predictive accuracy and interpretability.

  Abstract

  Antimicrobial resistance (AMR) is a growing global health crisis, responsible for an estimated 1.27 million deaths in 2019 alone. traditional approaches to identifying antibiotic resistance genes (ARGs) are often labour-intensive and limited in their ability to detect novel resistance mechanisms. In this study, we propose BRIDGE, a knowledge graph-based framework, to improve AMR gene prediction by integrating gene neighbourhood information and protein-protein interaction networks. Focusing on Klebsiella pneumoniae and Escherichia coli, we construct a comprehensive and biologically grounded knowledge graph using curated data from CARD, STRING, and DrugBank. We apply knowledge graph embedding models which are fed into deep neural networks to infer novel AMR links, achieving classification accuracy of up to 97%. Our results demonstrate that incorporating biologically meaningful relationships, such as gene neighbourhood information and protein interactions, enhances the predictive accuracy and interpretability of AMR link predictions. This work contributes to the development of scalable and data-integrated approaches for advancing antimicrobial resistance surveillance and drug discovery.

  bioinformatics2026-02-11v1

• Siderophore identification in microorganisms associated with marine sponges by LC-HRMS and a data analytic approach in R.

  Rios, A. G.; Kato, M. J.; Yamaguchi, L. F.; Esposito, B. P.; Arenas, A. F.

  AI Summary

  • The study aimed to identify siderophores in the microbiomes of three marine sponge species using LC-HRMS and an R-based analytical workflow.
  • A total of 59 potential siderophores were annotated, with 41 confirmed through chromatographic profiling and rigorous validation criteria.
  • The approach revealed a diverse set of iron-chelating metabolites, including Ferricrocin, Aeruginic acid, and Madurastatin, without significant impact from iron supplementation during extraction.

  Abstract

  Siderophores are pivotal iron-acquisition biomolecules integral to microbial survival, pathogenicity, and ecology. Elucidating these compounds offers critical insights into the microbial dynamics of marine holobionts and potential therapeutic applications. In this study, we present a culture-independent, data-centric strategy to identify siderophores from the microbiome of three marine sponge species: Dragmacidon reticulatum, Aplysina fulva, and Amphimedon viridis. Utilizing Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) coupled with a custom R-based analytical workflow (XCMS and MetaboAnnotation), we successfully annotated 59 potential siderophores, 41 of which were confirmed via chromatographic profiling. We employed a rigorous validation pipeline, utilizing multiple iron-adduct calculations [M-2H+Fe]+, [M-H+Fe]2+, [2M-2H+Fe]+, high mass accuracy thresholds (<3 ppm), and retention time precision (CV < 2%). Notably, iron supplementation during extraction did not significantly alter siderophore detection, suggesting constitutive production or environmental saturation. This workflow bypasses the limitations of traditional cultivation, revealing a diverse landscape of iron-chelating metabolites--including Ferricrocin, Aeruginic acid, and Madurastatin--directly within the sponge holobiont.

  bioinformatics2026-02-11v1

• Adaptive and Spandrel-like Constraints at Functional Sites in Protein Folds

  Poley-Gil, M.; Fernandez-Martin, M.; Banka, A.; Heinzinger, M.; Rost, B.; Valencia, A.; Parra, R. G.

  AI Summary

  • The study investigates how amino acid sequences contribute to protein structure and function, focusing on the role of evolutionary and physical constraints.
  • Using reverse folding and structure prediction, researchers found that some evolutionary conserved frustration in proteins cannot be removed, suggesting these are spandrels from physical constraints.
  • These findings suggest that functional specificity in proteins might evolve from these constraints, providing insight into the interplay between evolution, structure, and biophysics.

  Abstract

  Understanding the relationships among amino acid sequences, structures and functions in proteins and how they evolve, remains a central challenge in molecular biology. It is still unclear which sequence elements differentially contribute to structural integrity or molecular function. Even more, there are ongoing debates on whether protein folds emerge as a result of evolution or as a consequence of physical laws. The energy landscapes theory states that proteins are minimally frustrated systems, i.e. they fold by minimising their energetic conflicts. However, some local frustration, believed to be selected for functional reasons, remains in the native state of proteins. Here, we combine reverse folding and structure prediction methods with sequence and local frustration analysis to address the aforementioned ideas. We found that reverse folding techniques are unable to erase evolutionary conserved frustration from certain residues, even when detrimental for structural integrity. We propose that certain frustration hotspots behave like architectural spandrels, not directly shaped by selection but emerging from physical constraints in protein folds which evolution can later co-opt for function. Our results provide a new perspective revealing how sequence variation and functional specificity could evolve from evolutionary, structural and biophysical constraints.

  bioinformatics2026-02-11v1

• BiOS: An Open-Source Framework for the Integration of Heterogeneous Biodiversity Data

  Roldan, A.; Duran, T. G.; Far, A. J.; Capa, M.; Arboleda, E.; Cancellario, T.

  AI Summary

  • The study addresses the challenge of integrating heterogeneous biodiversity data by introducing BiOS, an open-source framework designed to harmonize datasets from taxonomy, genetics, to species distribution.
  • BiOS features a modular architecture with a decoupled back-end for data management and a user-friendly front-end, offering both an API for developers and a web interface for general users.
  • Key findings include BiOS's adherence to FAIR principles, enabling seamless data integration, and enhancing collaborative conservation efforts by overcoming data fragmentation.

  Abstract

  The era of Big Data has revolutionised biodiversity research, yet the potential of this information is frequently constrained by data heterogeneity, incompatible schemas, and the fragmentation of resources. Whilst standards such as Darwin Core have improved interoperability, significant barriers persist in harmonising multi-typology datasets ranging from taxonomy and genetics to species distribution. Here, we present the Biodiversity Observatory System (BiOS), a comprehensive, open-source software stack designed to address these impediments through a modular, community-driven architecture. BiOS departs from monolithic database designs by decoupling the back-end data management from the front-end presentation layer. This architectural separation supports a dual-access model tailored to diverse stakeholder needs. For researchers and developers, the system offers a comprehensive Application Programming Interface (API) that exposes all back-end functionalities, enabling seamless programmatic access, automated data retrieval, and integration with external analytical workflows. Simultaneously, the platform features a user-centric web interface designed to lower the technical barrier to entry. This interface facilitates intuitive data exploration through agile taxonomic navigation, advanced geospatial map viewers for species occurrence filtering, and dedicated dashboards for visualising genetic markers and legislative status. Strictly adhering to the FAIR principles (Findable, Accessible, Interoperable, Reusable), BiOS acts as a relational engine capable of integrating heterogeneous data streams. By providing a flexible, interoperable core that supports the "seven shortfalls" framework of biodiversity knowledge, BiOS offers a turnkey solution to overcome data fragmentation and enhance collaborative conservation efforts.

  bioinformatics2026-02-11v1

• Cigarette smoke induces colon cancer by regulating the gut microbiota and related metabolites

  Li, W.; Bao, Y.-n.; Zhao, Q.; Yang, X.; Gong, Y.; Gan, B.

  AI Summary

  • This study investigated the link between cigarette smoke and colorectal cancer (CRC) using a mouse model, finding that smoke exposure increases CRC incidence by altering gut microbiota and related metabolites.
  • Smoke exposure decreased beneficial bacteria like Lactobacillus, increased harmful bacteria like Firmicutes and Clostridium, and altered metabolites, while also downregulating tumor suppressor genes PARG, CPT2, and ALDH1A1.
  • Functional assays confirmed that reduced CPT2 expression in CRC cells enhanced malignancy, and clinical data showed these genes were downregulated in smoking-related CRC patients, offering diagnostic potential.

  Abstract

  The causal relationship between smoking and colorectal cancer (CRC) remains unclear. In this study, a cigarette smoke-exposed mouse model demonstrated that smoking significantly increased CRC incidence by inducing gut microbiota dysbiosis and altering related metabolites. Smoke exposure reduced beneficial bacteria (e.g., Lactobacillus), increased harmful bacteria (e.g., Firmicutes and Clostridium), elevated metabolites such as histamine, and suppressed the tumor suppressor genes PARG, CPT2, and ALDH1A1, thereby promoting tumor development. Functional assays in CRC cell lines further confirmed that CPT2 knockdown enhanced malignant phenotypes, including proliferation, migration, and invasion. Clinical analysis showed that these genes were markedly downregulated in smoking-related CRC patients, with strong diagnostic value (AUC > 0.8).

  bioinformatics2026-02-11v1

• PlantMDCS: A code-free, modular toolkit for rapid deployment of plant multi-omics databases

  Chen, C.; Liu, Y.; Wang, L.; Sai, J.; Wang, Y.; Yue, W.; Sun, J.; Li, Z.; Wang, F.; Tian, J.; Xu, D.; Fang, Y.

  AI Summary

  • PlantMDCS is a user-friendly, code-free toolkit designed for rapid deployment of plant multi-omics databases, addressing the challenge of managing and analyzing diverse omics data.
  • It features a decoupled front-end/back-end architecture where the back end manages data storage, preprocessing, and integration, while the front end supports the entire research workflow from data import to visualization without programming.
  • Benchmarking showed that PlantMDCS can construct databases in minutes across various plant species, enhancing efficiency, reproducibility, and data security through local deployment and controlled access.

  Abstract

  With the rapid accumulation of diverse omics datasets, achieving efficient management and integrative analysis of plant multi-omics data remains a major challenge. Conventional solutions rely on constructing web-based databases, which often demand substantial programming expertise and long-term financial support. To address these limitations, we developed the Plant Multi-omics Database Construction System (PlantMDCS)-a locally deployable, user-friendly, and collaborative platform that unifies database construction and downstream multi-omics analysis within a graphical environment. PlantMDCS adopts a decoupled front-end/back-end architecture. The back end serves as the core engine for data management and computation, and is responsible for the storage, preprocessing, integration, and hierarchical association of multi-omics data. Once initialized, the front end supports the complete research workflow, including data import, querying, integrative analysis and visualization. All operations can be performed without programming, while local resource usage is dominated by disk storage required for user-provided datasets rather than sustained computational overhead. Benchmarking across plant species ranging from Arabidopsis to hexaploid wheat demonstrated that database construction can be completed within minutes, independent of genome size or data complexity. PlantMDCS is designed for local deployment to ensure data security, while allowing multi-user collaboration within local networks and supporting controlled remote access for teams distributed across different regions. Overall, PlantMDCS offers a secure and sustainable framework that integrates data management and analysis within a unified system. This design shifts multi-omics research away from fragmented file-based processing toward persistent, database-driven exploration, thereby enhancing analytical efficiency and reproducibility.

  bioinformatics2026-02-11v1