Latest bioRxiv papers

Category: bioinformatics — Showing 50 items

• Confronting global eradication of TB head on: Uncovering the root of drug resistance and bacterial survival strategies through a comprehensive computational study of first-line TB drug resistant mutations

  Pawar, P.; Samarasinghe, S.

  Abstract

  Tuberculosis (TB) is fast becoming incurable affecting millions globally. Mycobacterium tuberculosis (Mtb), causative agent of TB, has evolved elusive survival strategies through point mutations in the drug targets leading to a daunting scenario of resistance towards first-line TB drugs, exacerbated by global differences in mutation patterns. Drug resistance studies have focussed only on few mutations; however, hundreds of mutations have been reported in the last three decades. WHO goal of global eradication of TB therefore now requires a deep understanding of mechanisms of drug resistance, involving many mutations, addressed in a global context. This study addresses bacterial survival strategies by following bacteria-drug interaction to probe into how bacteria evolve drug resistance mechanisms through mutations. We hypothesise that bacteria favour mutations that protect them from a drug while making the drug ineffective. To test the hypothesis, we quantify the impact of mutations on both bacterial function and drug binding affinity to get to the root of drug resistance revealing how bacteria may evolve an arsenal of mutations towards an optimal survival strategy. This first comprehensive and systematic in-depth study investigates global patterns of mutation and drug resistance mechanisms from mutation data for Mtb reported over the last 30 years. These were collected for 31,073 drug-resistant Mtb isolates from 149 published studies for the four first line drugs isoniazid (INH), pyrazinamide (PZA), rifampicin (RIF), and ethambutol (EMB). We found 821 single frequency non-synonymous mutations for INH (n= 202), RIF (n=120), EMB (n=226) and PZA (n=273). We then investigated the prevalence and diversity of these mutations in the drug targets across the globe. We found S315T in the target katG (60%) to be the most prevalent mutation in INH resistance followed by S450L in rpoB (56%) and M306V in embB (29%) associated with RIF and EMB resistance, respectively; these were also the highly occurring mutations across the six WHO regions, except for the most common mutation Q10P in pncA (1.4%) (PZA resistance; with shorter exposure to drug) showing a variable pattern of occurrence globally. We found the highest mutational burden in the Western Pacific and South-East Asia regions for INH and RIF resistance. Frequent mutations had also undergone frequent amino acid substitutions. Accordingly, we developed a comprehensive atlas of mutation spread across the globe and their evolution over the last 30 years. We then probed into the impact of mutations on TB bacteria and drug binding with a comprehensive bioinformatics analysis for understanding crucial changes caused by mutation at the molecular level affecting function and structural stability of bacteria and the drug binding affinity. We found that the most prevalent mutations occur in non-conserved areas in the drug binding region indicating a choice of a less dramatic level of change in target protein function and stability. All mutations reduced drug binding affinity. For characterising drug resistance mechanisms, we introduced a new concept of ranking drug-resistant TB mutations into lethal, moderate, mild and neutral considering the combined effect on Mtb viability and drug binding. We identified 340 mutations as lethal, 284 as moderate, 185 as mild and 12 as neutral. We observed that frequently occurring mutations occur in non-conserved regions causing a mild effect on target proteins (such as S315T of katG, S450L of rpoB and M306V in embB), while reducing drug binding affinity. With these we uncovered a universal strategy of drug resistance and bacterial survival: Mtb favours less harmful mutations in the drug binding region without compromising conservancy while destabilising the drugs, thus striking a balance between fitness and drug resistance. This ingenious strategy seems successful and reasonable persisting globally over three decades and provides a holistic understanding of drug resistance and a strong foundation for designing efficacious drugs and therapies towards global eradication of TB.

  bioinformatics2026-05-01v1

• Modeling healthy proteomic profiles for anomaly detection using subspace learning based one-class classification

  Sohrab, F.; Kumar, A.; Ahola, V.; Magis, A.; Hautamaki, V.; Heinaniemi, M.; Huang, S.

  Abstract

  High-throughput plasma proteomics provides sensitive and scalable measurements of thousands of systemic protein profiles from minimally invasive blood samples, creating new opportunities for disease detection and population-scale health monitoring. However, robust statistical modeling remains challenging due to high dimensionality, limited availability and high diversity of diseased samples, resulting in class imbalance in clinical cohorts. Here, we present a subspace One-Class Classification (OCC) framework for proteomics-driven anomaly detection that models healthy proteomic profiles as a reference distribution. To address the limitations of conventional hyperparameter tuning in severely imbalanced data settings, we introduce a fully data-driven parameter estimation strategy that infers all model parameters directly from intrinsic properties of the healthy training data, without using any disease labels. Using plasma proteomics data generated with Olink, we evaluate a family of subspace and graph-embedded subspace extensions of Support Vector Data Description, in which all models operate on learned low-dimensional representations rather than the original feature space. Models are trained exclusively on a healthy reference cohort and evaluated on heterogeneous disease conditions, including multiple cancer types and an independent COVID-19 cohort, with all disease samples withheld from training to enable unbiased assessment of cross-disease generalization. Across disease contexts, the evaluated one-class models yield stable and balanced detection performance, demonstrating that learning structured low-dimensional representations of healthy proteomic variation captures intrinsic biological organization that generalizes across disease-specific perturbations. These results establish healthy-profile-based, subspace one-class learning as a robust and disease-agnostic framework for screening in high-dimensional plasma proteomics.

  bioinformatics2026-05-01v1

• From Generalist to Specialist: Evolution of PS2 α-integrins and Implications for Drug Targeting

  Liu, S.; Chen, Y.; Xu, R.-G.; Zhang, H.; Mostafa, F.; Liu, L.

  Abstract

  Integrins are heterodimeric transmembrane receptors that mediate cell-cell and cell-extracellular matrix interactions and play essential roles in development and disease. Within the PS2 alpha-integrin subfamily, four paralogs (alphaIIb, alpha5, alpha8, and alphaV) share a conserved RGD-binding motif yet exhibit diverse functional specializations. Integrins have been widely targeted therapeutically for various clinical conditions, though achieving subtype specificity remains a major challenge. Here, we performed an integrative evolutionary analysis of 114 PS2 alpha-integrin sequences across 28 vertebrate species, combining phylogenetic reconstruction, time calibration, ancestral sequence inference, and structural mapping. Our time-calibrated phylogeny indicates that the PS2 lineage originated ~862 Mya, with diversification of the four paralogs occurring prior to vertebrate radiation. Ancestral state reconstruction reveals that fibronectin and vitronectin binding are ancestral traits, whereas fibrinogen binding and beta3 pairing arose independently in the alphaIIb and alphaV lineage. Evolutionary rate analysis shows domain-specific divergence, with the beta-propeller acting as a hotspot of evolutionary change, likely driven by combined pressures from ligand binding and beta-subunit interaction. These pressures vary across paralogs: alphaIIb exhibits accelerated evolution in ligand-binding regions, while alphaV displays elevated rates in beta-subunit interaction domains. Mapping sequence variation onto structural interfaces identifies lineage-specific substitutions underlying functional divergence, including distinct molecular solutions for fibrinogen binding in alphaIIb and alphaV. These findings collectively demonstrate that PS2 alpha-integrins evolved from a generalist ancestor through neofunctionalization and lineage-specific specialization. This work provides an evolutionary framework for identifying subtype-specific functional sites and highlights the potential of evolution-informed strategies to guide the development of more selective integrin-targeting therapeutics.

  bioinformatics2026-05-01v1

• MorphOTU: A universal image-based framework for delineating biodiversity discovery

  Zhan, Z.; Chen, W.; Liu, X.; Yue, L.; Zhang, F.

  Abstract

  The absence of a scalable system for organizing the vast majority of unidentified species becomes the central obstacle in biodiversity science. Existing molecular and computer-vision methods rely on DNA material or closed-set labels, which hamper biodiversity quantification under the open, incomplete conditions that characterize real ecosystems. Here, we introduce morphOTUs, a general image-based framework that constructs operational units of biodiversity directly from phenotype. Using morphOTU, we derive image based OTUs across five plant and beetle datasets spanning heterogeneous imaging conditions. These units recover species-level boundaries, retain coherent structure when most species are "unseen" during training, and accurately approximate richness and Shannon diversity indices even under sparse labeling or limited sampling. Visual explanations reveal that morphOTU consistently focuses on biologically meaningful traits and captures continuous phenotypic variation. By providing a scalable and open set framework for quantifying phenotypic diversity, morphOTUs enable biodiversity assessment that includes unnamed species and unlock the ecological value of rapidly expanding digital image repositories.

  bioinformatics2026-05-01v1

• Single-cell foundation models reveal context-sensitive cancer programmes under subtype shift

  Wallace, J.; Youssef, G.; Han, N.

  Abstract

  Single-cell foundation models (scFMs) have shown promise as transferable representations of cellular state, but recent zero-shot evaluations suggest that they do not consistently outperform simpler baselines. We asked whether this apparent limitation reflects an intrinsic weakness of scFMs or instead the difficulty of using them without task-specific adaptation. To test this, we fine-tuned two widely used scFMs, Geneformer and scGPT, on common tumour subtypes from renal, lung, and breast cancer, and compared them with a LightGBM baseline on within-domain validation cohorts and on out-of-domain rarer, unseen cancer subtypes. Across all three organs, the models achieved near-perfect within-domain discrimination (AUROC 0.98-1.00), but differences emerged under subtype shift. On chromophobe RCC, scGPT and Geneformer achieved AUROC 0.88 and 0.92 respectively versus 0.64 for LightGBM; on SCLC, Geneformer reached 1.00 versus 0.82 for LightGBM; and on TNBC, scGPT achieved 0.80 versus 0.49 for LightGBM. To determine whether this generalisation reflected meaningful adaptation rather than arbitrary feature drift, we applied Integrated Gradients, an interpretability technique, to the fine-tuned scFMs and SHAP to LightGBM. LightGBM showed highly stable gene-importance rankings across datasets, whereas the foundation models were substantially more context-sensitive. However, this flexibility was not random: all models converged on a shared within-domain core, while scFMs acquired larger rare-subtype-specific gene sets and pathway programmes during transfer. Pathway enrichment further supported the biological relevance of these attributed genes. Together, these results suggest that fine-tuned scFMs can bridge clinically relevant domain shifts in cancer single-cell analysis and that interpretability provides a practical route to distinguishing biologically grounded adaptation from rigid reuse of training-era rules.

  bioinformatics2026-05-01v1

• JUMPlion improves quantitative DIA proteomics through ion-level recovery of missing values

  Fu, Y.; Yuan, Z.-F.; Byrum, S. D.; Wu, L.; Peng, J.; Wang, X.; High, A. A.

  Abstract

  Incomplete quantification remains a persistent challenge in data-independent acquisition (DIA) mass spectrometry (MS), particularly in low-input and single-cell analyses. In identification-driven workflows, missing protein quantities often arise not from true absence of the corresponding peptides, but from failure to retain low-abundance signals from precursor or product ions for quantification. Here we present JUMPlion (local inference of ion-level missingness), a DIA quantification framework that re-examines MS raw files to recover missing values at the ion level before protein quantification. JUMPlion re-extracts precursor- and product-ion signals directly from raw data, infers ion-level measurements within precursor-specific local quantitative neighborhoods, and combines complementary precursor- and product-ion signals into downstream quantification. Using benchmark datasets acquired on multiple DIA platforms, JUMPlion increased protein-level completeness, improved fold-change accuracy, and enhanced detection of differentially abundant proteins while maintaining low differential-abundance false discovery rates. These gains were most evident in low-input and single-cell DIA datasets. Together, these results show that addressing missingness at the ion level before protein-level summarization can improve DIA quantification in diverse acquisition settings.

  bioinformatics2026-05-01v1

• Rapid-PFP: Accelerating Prefix-Free Parsing with GPU Parallelism

  Ferro, E. A.; Pencinger, T.; Green, O.; Lotfollahi, M.; Boucher, C.

  Abstract

  Prefix-Free Parsing (PFP) is widely used in genomic data processing to construct compressed indexes on massive, highly repetitive datasets. However, existing CPU implementations are constrained by sequential bottlenecks, limiting their ability to scale to large-scale modern pangenomic collections. We introduce RAPID-PFP, a redesigned implementation of the PFP algorithm that takes advantage of the massive parallelism and high memory bandwidth of modern GPUs. RAPID-PFP parallelizes trigger-string detection, phrase parsing, dictionary construction, and parse generation through custom CUDA kernels and GPU-resident data structures built using cuDF, CuPy, and Numba-CUDA. The algorithm operates entirely within GPU memory, minimizes host interaction, and dynamically adapts to available VRAM, enabling efficient processing in a range of hardware configurations. Across E. coli and Human Pangenome (HPRC) datasets, RAPID-PFP produces identical output to established CPU pipelines while delivering an order-of-magnitude acceleration. On 3,682 E. coli assemblies, RAPID-PFP reduces runtime from 552 seconds to 17 seconds compared to PFP-FL (32.1 times) and from 1,078 seconds to 17 seconds compared to PFP-ITL (62.6 times). On the complete 46-sample HPRC dataset, RAPID-PFP achieves a 33.4 time speedup and successfully processes scales that PFP-ITL cannot handle. Performance improves with dataset size, reflecting that PFP maps naturally onto thousands of CUDA cores, yielding sublinear scaling relative to CPU implementations. RAPID-PFP demonstrates that foundational compressed-indexing algorithms can be re-engineered for accelerators, enabling scalable and practical preprocessing for large-scale genomic indexing workflows.

  bioinformatics2026-05-01v1

• A high-quality, chromosome-scale genome assembly of the shade-tolerant wild rice, Oryza granulata

  Zhang, F.; Yang, Y.-h.; Li, W.; Shi, C.; Zhu, X.-g.; Gao, L.-z.

  Abstract

  Oryza granulata Nees et Arn. ex Watt, a diploid wild rice (GG genome), possesses exceptional shade tolerance and is a key genetic resource for rice improvement. However, previous genome assemblies lacked continuity and completeness. Here we present a chromosome-scale reference genome of O. granulata using PacBio SMRT (113*), Hi-C (95*), and Illumina sequencing. The final assembly is ~764.24 Mb, with a scaffold N50 of ~59.32 Mb, and ~96.47% of the sequence anchored to 12 chromosomes. BUSCO completeness is ~98.6%. We annotated ~42,064 protein-coding genes, of which ~95.39% were functionally annotated, along with ~73.46% repetitive elements. The genome assembly and raw sequencing data are available at NGDC (PRJCA061980), NGDC GSA (CRA068332), and NGDC GWH (GWHISVE00000000.1). This high-quality genome will serve as a fundamental resource for evolutionary genomics, conservation biology, and breeding of shade-tolerant rice cultivars.

  bioinformatics2026-05-01v1

• Aiki-GeNano: Multi-Stage Preference Optimization for Generative Design of Developable Nanobodies

  Meda, R. S.; Doshi, J.; Iyer, E.; Shastry, S.; Mysore, V.

  Abstract

  Therapeutic nanobodies must combine target binding with biophysical and chemical properties that determine manufacturability, stability, and clinical viability, collectively termed developability, yet most computational design pipelines still treat developability as a post-hoc filter rather than an integrated training objective. We present Aiki-GeNano, a three-stage language-model alignment pipeline for epitope-conditioned nanobody generation that integrates multiple developability signals directly into training, using only sequence information and previously published predictors. Across 65 target epitopes and relative to the supervised baseline, the combined pipeline raised predicted mean melting temperature by 6.6 C, halved isomerization-motif severity, reduced deamidation, N-glycosylation sequons and CDR methionine-oxidation motifs, and preserved predicted humanness and solubility. On a shared 10-target GPCR benchmark, Aiki-GeNano achieved the highest predicted melting temperature and the lowest isomerization severity among five contemporary VHH generators. Starting from ProtGPT2 and a 1.35-million-pair binder dataset generated on an mRNA-display platform, the pipeline applies supervised fine-tuning, Direct Preference Optimization on 522{,}800 pairs ranked by a composite of selectivity, predicted thermal stability, solubility, and humanness, and Group Reward-Decoupled Policy Optimization against six sequence-based rewards (FR2 hydrophobicity, hydrophobic-patch coverage, chemical-liability motifs, Wilkinson--Harrison expression probability, VHH hallmark residues, scaffold integrity). Generated sequences differ from the nearest training sequence by a mean of 8.1--9.0 amino acids out of 126, and two alternative training trajectories converge to distinct amino-acid-composition strategies with similar liability outcomes but different thermal-stability gains, indicating initialization-dependent convergence of the reward-optimized policy. Predicted humanness was preserved at the level of the camelid VHH scaffold of the training library -- a data-side limitation rather than a methodological one, since the framework was effectively constant across all preference pairs. Applicability to the drug discovery and development pipeline, limitations of predicted-property evaluation, and future work are discussed.

  bioinformatics2026-05-01v1

• Skill-Augmented Frontier Agents Nearly Saturate BixBench-Verified-50

  Zhang, X.

  Abstract

  Large language model (LLM) agents are increasingly used for biological data analysis, but prior benchmark results have given a mixed picture of whether they are ready for routine bioinformatics work. The original BixBench study reported only ~17-21% accuracy for frontier agents on open-answer bioinformatics questions. Subsequent curation of BixBench-Verified-50 removed or revised ambiguous items, revealing much higher performance for modern agents. Here we evaluate three frontier-model configurations on the 50 verified questions using the same local benchmark, prompt structure, answer format, and grading pipeline: GPT-5.4 with Claude Scientific Skills and no web access, Claude Opus 4.7 with Claude Scientific Skills and no web access, and GPT-5.5 with Claude Scientific Skills, bioSkills, and web access. The three configurations achieve 88.0% (44/50), 84.0% (42/50), and 98.0% (49/50) accuracy, respectively. The remaining GPT-5.5 error is not a clear analytical failure: the agent correctly computed Spearman correlations on the distributed CRISPRGeneEffect.csv values and selected CCND1, whereas the reference answer is recovered only after interpreting stronger essentiality as the opposite sign of the raw gene-effect score. Offline errors mainly occurred when agents lacked pathway, organism-annotation, BUSCO, or PhyKIT-related resources. These results show that frontier agents equipped with high-quality scientific skills can nearly saturate a curated bioinformatics benchmark, while also emphasizing that question wording, score sign conventions, and access to current external resources remain decisive for reliable evaluation.

  bioinformatics2026-05-01v1

• Differential peripheral immune dynamics underlie therapeutic response to chemotherapy and chemo-immunotherapy in triple-negative breast cancer

  mesrizadeh, z.; Mukund, K.; Subramaniam, S.

  Abstract

  Triple-negative breast cancer (TNBC) remains the most aggressive breast cancer subtype, with limited treatment options and variable response to immune checkpoint inhibitors. While tumor-infiltrating lymphocytes have been extensively studied, the integration of system-level peripheral immune dynamics with mechanistic immune regulation underlying therapeutic response and resistance remain poorly defined. Here, we integrate systems-level immune state modeling with pathway-level mechanistic inference to analyze single-cell RNA sequencing of peripheral blood mononuclear cells from advanced TNBC patients treated with paclitaxel alone (chemotherapy) or in combination with anti-PD-L1 antibody atezolizumab (combination). This framework leverages treatment arm, longitudinal sampling, and clinical response to resolve coordinated immune programs across lymphoid and myeloid compartments. Using this approach, we identified distinct treatment- and response-specific immune states in pre- and post-treatment. Chemotherapy responders displayed pre-treatment adaptive immune priming, whereas combination therapy responders exhibited pre-existing effector T cell activity coupled with tumor tissue PD-L1 expression. In contrast, chemotherapy non-responders developed persistent post-treatment immune dysregulation in regulatory and terminal effector programs, while combination therapy non-responders demonstrated maladaptive remodeling of adaptive and innate lymphoid compartments, including dysfunctional NK and metabolically reprogrammed myeloid populations. Across both regimens, pathways involving protein translation, metabolic adaptation, and stress signaling emerged as critical modulators of response. These findings suggest that coordinated adaptive-innate immune dynamics underlie therapeutic efficacy, whereas systemic immune exhaustion and myeloid immunoregulation lead to resistance. Projection of these peripheral immune programs onto independent I-SPY2 showed concordant associations with tumor immune phenotypes and pathological complete response, supporting generalizability of the identified systemic immune states. Our study demonstrates the utility of an integrative systems-level approach for linking peripheral immune state organization with mechanistic insights, informing immune response and resistance in TNBC.

  bioinformatics2026-05-01v1

• Reconstructing True 3D Spatial Omics at Single-Cell Resolution

  Yang, Y.; Luo, Y.; Zhang, K.; Bu, Y.; Xia, Z.; Peng, H.; Yan, R.; Liu, Q.; Chen, Y.; Shen, L.; Chen, E.

  Abstract

  Capturing the three-dimensional (3D) organization of cells is essential for deciphering complex biological processes, yet comprehensive 3D spatial omics is severely hindered by the destructive nature of physical sectioning and the depth limitations of intact tissue imaging. Current computational methods rely on 2.5D stacking of discrete slices, which inherently disrupts tissue topology and fails to resolve continuous depth-dependent molecular gradients. To bridge this gap, we introduce DeepSpatial, an Optimal Transport flow matching framework that models tissue evolution as a continuous dynamic vector field. By solving the underlying probability flow ODEs, DeepSpatial enables the direct extraction of uninterrupted, infinitely resolvable tissue states at arbitrary spatial depths. Using Deep STAR/RIBOmap 3D technologies, we demonstrate that DeepSpatial achieves improved 3D reconstruction fidelity relative to 2.5D approaches, yielding structures that more closely recapitulate native tissue microenvironments in real-world datasets. Across diverse spatial omics modalities, including spatial proteomics using imaging mass cytometry in human breast cancer and spatial transcriptomics using openST in head and neck squamous cell carcinoma metastatic lymph nodes, DeepSpatial produces biologically interpretable and high-fidelity reconstructions across datasets. We evaluated the scalability and robustness of DeepSpatial on a large-scale mouse brain dataset, reconstructing a continuous 3D cellular atlas comprising 39 million cells within 41.6 hours. Systematic downstream characterization validated its ability to recapitulate consistent spatial architectures, cell-type distributions, transcriptomic patterns, and microenvironmental structures across brain regions. Collectively, these results demonstrate DeepSpatial as a generalizable and efficient solution for true 3D spatial reconstruction across scales and modalities.

  bioinformatics2026-05-01v1

• Interpretable sequence-based machine learning consolidates candidate H3N2 hemagglutinin antigenic sites

  Meyer, A. G.; Santillana, M.

  Abstract

  Vaccine strain selection for seasonal influenza A(H3N2) depends on knowing which hemagglutinin (HA) substitutions are most likely to erode neutralizing antibody recognition, yet published antigenic site sets disagree substantially on which positions matter most. We applied interpretable gradient-boosted tree models with SHAP-based site attribution to two complementary hemagglutination inhibition (HI) datasets to produce a more consolidated ranking of candidate antigenic positions. Models trained on a Neher/Bedford benchmark dataset recover the canonical cluster-transition sites established by prior analyses. Moreover, after filtering the WIC dataset for confounding factors, our models recover the majority of positions from four major prior reference sets (Koel, Neher/Bedford, Harvey, and Shah) and improve concordance between rankings derived from the Neher/Bedford and WIC datasets. Rankings from our models also agree more strongly with models trained to predict sampling time or passage identity than with standard evolutionary metrics used to detect diversifying selection. Our results show that interpretable sequence-based models can provide a more integrative ranking of candidate antigenic positions across different data sources and modeling approaches. This work should aid efforts to prioritize H3N2 substitutions for epidemic surveillance.

  bioinformatics2026-05-01v1

• Single nucleus transcriptome analysis of Arabidopsis thaliana roots infected with Phytophthora. capsici

  Alajoleen, R. M.; Chau, T. N.; Shuman, J.; Bargmann, B.; Li, S.

  Abstract

  Understanding how plant roots coordinate immune responses at the cellular level is key to unraveling host-pathogen interactions. Using single-nucleus RNA sequencing (snRNA-seq) of Arabidopsis thaliana roots 24 hours after Phytophthora. capsici (P. capsici) inoculation, we captured the transcriptional landscape of early infection at single-cell resolution. Four libraries (two infected and two mock-treated) were generated with approximately 26,000 high-quality nuclei with consistent sequencing depth and viability. A reference-based pipeline distinguished host and pathogen transcripts, enabling species-resolved mapping and host-focused single-nucleus transcriptomic analysis. Integration and clustering identified 12 transcriptionally distinct root cell types, encompassing major tissues such as the meristem, cortex, endodermis, and vasculature. Cluster-specific marker analysis confirmed cell-type identities, while differential expression and Gene Ontology enrichment revealed a global transcriptional shift from metabolic and translational processes in mock samples to defense, stress, and pathogen-response pathways upon infection. Hormone-related enrichment indicated broad salicylic acid activation across root tissues, spatially confined ethylene signaling in vascular-associated clusters, and localized jasmonic acid responses in cortex and phloem. Together, these results provide a high-resolution view of Arabidopsis root immunity, highlighting a coordinated yet tissue-specific defense architecture in which salicylic acid underpins systemic protection, ethylene modulates vascular defense, and jasmonic acid contributes targeted reinforcement during early P. capsici infection. Keywords: single-nucleus RNA sequencing, root immunity, cell type specific defense. hormone signaling, salicylic acid, jasmonic acid, ethylene signaling

  bioinformatics2026-05-01v1

• OmniAge: a compendium of aging omic biomarkers links mitotic clocks to clonal hematopoiesis and causality

  Du, Z.; Ling, Y.; Tong, H.; Guo, X.; Teschendorff, A. E.

  Abstract

  Interest in aging 'omic' biomarkers has grown due to their ability to quantify biological age. Most of these biomarkers have been derived in blood and fall into many diverse categories, yet relatively little is known about their correlative patterns, especially between biomarkers from different categories. Here we present the OmniAge R and Python package, a collection of 413 aging omic biomarkers representing 12 different categories, including traditional epigenetic clocks, epigenetic mitotic clocks, DNA methylation-based proxies for clonal hematopoiesis and inflammaging, causal clocks, cell-type specific epigenetic clocks and single-cell transcriptomic clocks. By studying their inter-class correlations across large blood datasets, we reveal associations of mitotic age with clonal hematopoiesis subtypes and causal clocks, which are predictive of cancer risk. Using proxies of serum protein levels, we further dissect associations with mitotic clocks, clonal hematopoiesis and causal clocks into distinct biological processes mapping to key aging pathways. Applying OmniAge to multi-modal data of sorted immune cell-types reveals that age-acceleration derived from transcriptomic and epigenetic clocks correlate, but that this is driven by underlying cell-type heterogeneity. In summary, the OmniAge package is an exploratory tool for evaluating large numbers of aging omic biomarkers, and to aid discovery and generate new hypotheses.

  bioinformatics2026-05-01v1

• Calibrated analysis framework for nanopore direct RNA sequencing uncovers cell-specific m⁶A stoichiometry at conserved sites

  Ohnezeit, D.; Loliashvili, E.; Putzel, G.; Verstraten, R.; Liu, J.; Nicholson, L. S.; Pironti, A.; Jaffrey, S. R.; Depledge, D. P.; Wilson, A. C.

  Abstract

  Nanopore direct RNA sequencing (DRS) coupled with Dorado modification-aware basecalling enables mapping of epitranscriptomic modifications including N6-methyladenosine (m6A) at the level of individual RNAs. However, a lack of systematic benchmarking continues to raise questions regarding the sensitivity, specificity, and reproducibility of this method. To address this and to establish a best-practice workflow, we evaluated multiple Dorado versions using in vitro transcribed RNA and an m6A methyltransferase inhibitor as specificity controls. We established that stringent filtering is necessary to reduce false-positive calls and found strong concordance at high-stoichiometry sites when compared to an orthogonal m6A mapping method (GLORI). Further, by applying DRS to primary human fibroblasts and HD10.6 neurons, we uncovered cell type-specific differences in m6A stoichiometry, indicating a finely tuned epitranscriptomic regulation. Our study thus presents the first systematic comparison of Dorado and GLORI from the same input RNA and expands characterization of the m6A epitranscriptome to fibroblasts and neurons.

  bioinformatics2026-04-30v4

• Praxis-BGM: Clustering of Omics Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference

  Jia, Q.; Goodrich, J. A.; Conti, D. V.

  Abstract

  High-dimensional omics data are typically measured on limited sample sizes, which challenges model-based clustering methods such as Gaussian mixture models, often leading to instability and poor generalization under complex mixture structures. To address these limitations, we developed Praxis-BGM, a natural-gradient variational inference framework for Gaussian mixture models that enables semi-supervised transfer learning by incorporating an informative prior Gaussian mixture model derived from large-scale reference data with robust cluster structures. This prior can encode cluster-specific means, covariance structures, and structural connectivity patterns, and is updated using the target data with variational inference to improve clustering in small-sample settings. We derived natural-gradient updates for standard parameters and assess feature-level contributions to posterior clustering via Bayes Factors. Implemented in Python library JAX for accelerator-oriented computation, Praxis-BGM is computationally efficient and scalable. Across extensive simulations and two real-world applications-breast cancer bulk transcriptomics for subtype recovery and single-cell transcriptomics for cross-platform label transfer-Praxis-BGM improves posterior clustering performance, stability, and biological interpretability, even when priors are partially mismatched.

  bioinformatics2026-04-30v3

• Systematic evaluation and benchmarking of text summarization methods for biomedical literature: From word-frequency methods to language models

  Baumgärtel, F.; Bono, E.; Fillinger, L.; Galou, L.; Keska-Izworska, K.; Walter, S.; Andorfer, P.; Kratochwill, K.; Perco, P.; Ley, M.

  Abstract

  The rapid expansion of biomedical literature demands automated summarization tools that can reliably condense research articles into concise, accurate overviews. We benchmarked 62 text summarization methods - ranging from frequency-based and TextRank extractors to modern encoder-decoder models (EDMs) and large language models (LLMs) - on a set of 1,000 biomedical abstracts for which author-generated highlights sections were available as reference summaries. Models were evaluated using a composite suite of metrics covering lexical overlap (ROUGE-1/2/L, BLEU, METEOR), embedding-based semantic similarity (RoBERTa, DeBERTa, all-mpnet-base-v2), and factual consistency (AlignScore). Our results indicate that general-purpose language models (LMs) achieve the highest overall scores across both lexical and semantic metrics, outperforming both reasoning-oriented and domain-specific models. Within the general-purpose group, medium-sized models, typically runnable on a single node, often outperform frontier-scale counterparts, suggesting an optimal balance between model capacity and computational efficiency. Statistical extractive methods lag behind all neural approaches. These findings provide a systematic reference for selecting summarization tools in biomedical research and highlight that broad pretraining remains more effective than narrow domain adaptation for generating high-quality scientific summaries.

  bioinformatics2026-04-30v3

• SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data

  An, S.; Keller, M.; Gehlenborg, N.; Hemberg, M.

  Abstract

  Spatially resolved single-cell technologies enable profiling of cells in situ, yet computational approaches that jointly discover multicellular spatial patterns and characterize their molecular programs remain limited. Here we introduce SpatialQuery, a framework that can both identify cellular motifs, i.e. recurrent multicellular co-localization patterns, and perform molecular analyses focused on the motifs. It uncovers genes modulated by spatial contexts through differential expression analysis, and detects coordinated expression changes through covariation analysis. SpatialQuery can identify functional tissue units, and goes beyond pairwise analyses to characterize multicellular interactions. Applications to both spatial transcriptomics and proteomics data uncover cross-germ-layer signaling in gut tube patterning, disease-specific fibrotic and immunosuppressive niches in kidney and colon, and regional determinants of motif-associated transcriptional programs in a mouse brain atlas. SpatialQuery is available as a Python package, and we demonstrate how its light computational footprint enables integration into web-based cell atlas portals for interactive visualization and exploration.

  bioinformatics2026-04-30v3

• On the use of variational autoencoders for biomedical data integration

  Pielies Avelli, M.; Hernandez Medina, R.; Webel, H. E.; Rasmussen, S.

  Abstract

  Variational Autoencoders (VAEs) are a widely used framework to integrate diverse biomedical data modalities, create representations that capture the underlying structure of the datasets, and obtain insights about the relations between variables. Here we describe how this is achieved from an empirical point of view in our previously developed VAE-based framework MOVE, providing an intuitive perspective on the inner workings of multimodal VAEs in biomedical contexts. We explore how the models' emerging dynamics shape their performance and how in silico perturbations can be leveraged to identify potential associations between variables. To do that, we extend our framework to handle perturbations of continuous variables, introduce a new approach to better capture associations between them, and create synthetic datasets to benchmark the proposed methods against well-defined ground truth associations. We finally showcase our findings in real biomedical scenarios, namely a multimodal dataset of inflammatory bowel disease and a dataset containing genetic knockdowns in K562 and RPE1 cells.

  bioinformatics2026-04-30v2

• Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Features to Speculative Multimer Assembly

  Nithin, C.; Pilla, S. P.; Kmiecik, S.

  Abstract

  CASP16 provided a community-wide benchmark for assessing RNA structure prediction, including the first large-scale blind assessment of RNA-RNA multimer prediction. The results showed that achieving high atomic precision remains a major challenge across the field. In this work, we use the performance of our group (LCBio) as a diagnostic case study to examine the current limits of RNA structure prediction. Our workflow ranked first in the RNA multimer category and remained competitive for monomers. We combine hierarchical analysis with representative case studies to identify a pattern of predictive breakdown, in which modeling fidelity degrades from reliable local features to increasingly speculative global architectures. Multi-helix junctions appear to mark a major transition boundary where 2D topological success often fails to translate into 3D geometric realism, leading to cascading errors in global architecture. This hierarchical breakdown is especially pronounced in RNA multimers, where limitations in the recovery of junction geometry and tertiary interactions propagate directly into errors in higher-order assembly, making multimer prediction increasingly speculative. By placing benchmark performance in a direct structural context, this case study helps define the current limits of RNA structure prediction and highlights priorities for improving predictive accuracy.

  bioinformatics2026-04-30v2

• Spartan: activation-aware framework for spatial domain and variable gene discovery

  Faiz, M. F. I.; Jokl, E.; Jennings, R.; Piper Hanley, K.; Sharrocks, A.; Iqbal, M.; Baker, S. M.

  Abstract

  Spatial transcriptomics is rapidly advancing toward single-cell-level resolution, revealing complex tissue architectures organized across continuous anatomical gradients. However, accurate identification of spatial domains remains a central computational challenge, as many existing clustering approaches blur anatomical boundaries, merge transitional zones, or fail to resolve localized microstructures. Here we introduce Spartan, an activation-aware multiplex graph framework for high-resolution domain discovery. Spartan integrates spatial topology and Local Spatial Activation (LSA), a neighborhood deviation signal that captures localized transcriptional heterogeneity often attenuated by similarity-based clustering. By jointly modeling cohesion within domains and localized activation structure, Spartan recovers anatomically aligned partitions across spatially resolved transcriptomics technologies including Visium HD, MERFISH, Stereo-seq, and STARmap. We further demonstrate its utility in a high-resolution Visium HD section of developing human esophagus and stomach, where activation-aware graph integration enables precise delineation of complex transitional regions such as the gastroesophageal junction and supports stable multi-scale domain recovery without fragile hyperparameter tuning. Beyond domain identification, Spartan leverages activation-aware structure to detect spatially variable genes associated with localized tissue remodeling. Spartan scales near-linearly with dataset size, providing a robust and interpretable framework for spatial systems-level analysis.

  bioinformatics2026-04-30v2

• A gene program dictionary of human cells

  Xu, Y.; Wang, Y.; Geng, Z.; Qin, Y.; Ma, S.

  Abstract

  Defining all human cell types and their roles in health and disease is a central goal of biology. Single-cell RNA sequencing has enabled the construction of organ-specific cell atlases, but building a comprehensive organism-wide atlas spanning multiple organs remains challenging due to batch effects, study biases, and inter-organ complexity. Here, we present Gene Program Dictionary (GPD), a framework that leverages robust gene co-expression programs-rather than direct cell integration-to overcome these barriers. Using SpacGPA, a partial correlation-based network method, we analyzed 466 scRNA-seq datasets, generating 1,975 independent networks and 90,701 gene co-expression modules, which were consolidated into 1,534 consensus gene programs representing a wide range of human tissues and cell types. Each program serves as a composite marker, capturing both cell-type-specific and shared biological processes. We demonstrate their utility by mapping endothelial cell subtypes across tissues to reveal their heterogeneity-including tumor-specific programs-annotating colorectal cancer spatial transcriptomes, and linking programs and their corresponding cell types to disease loci, revealing hotspots such as neuronal programs in psychiatric disorders and a proximal tubule program in kidney diseases. GPD provides an organism-wide reference for studying cellular diversity and disease mechanisms.

  bioinformatics2026-04-30v2

• Survey of the human proteostasis network: the ubiquitin-proteasome system

  Elsasser, S.; Powers, E.; Stoeger, T.; Sui, X.; Kurtzbard, R. D.; Martinez-Botia, P.; Wangaline, M. A.; Gama, A. R.; Huttlin, E. L.; Elia, L. P.; Kelly, J. W.; Gestwicki, J. E.; Frydman, J. E.; Finkbeiner, S.; Clerico, E. M.; Morimoto, R.; Prado, M. A.; Vertegaal, A. C. O.; Hofmann, K.; Finley, D.

  Abstract

  Modification by ubiquitination governs the half-lives of thousands of proteins that are fated for elimination by either the proteasome or autophagy pathways, depending on the intricate architectures of ubiquitin modification. This system mediates quality control for individual proteins, protein complexes, and organelles, as well as myriad purely regulatory functions. Here we provide a comprehensive survey of the ubiquitin-proteasome system (UPS), the scope of which is at present poorly defined. The UPS, with the inclusion of pathways involving ubiquitin-like modifiers, comprises in our estimate over 1400 distinct proteins in humans, a vast set of activities whose collective impact on the biology of the cell is pervasive. The UPS is an integral component of the proteostasis network (PN), the remainder of which we have also surveyed in recent studies. With the addition of molecular chaperones, proteins from autophagy-lysosome pathway, and related activities, the PN includes in total over 3100 components by our estimates. Comprehensive and systematic definition of these pathways should support a range of ongoing investigations in the areas of genomics, proteomics, biochemistry, cell biology, and disease research.

  bioinformatics2026-04-30v2

• Harnessing AI to Build Virtual Cells

  Cheng, X.; Li, P.; Guo, H.; Liang, Y.; Gong, J.; de Vazelhes, W.; Gou, C.; Xie, P.; Song, L.; Xing, E. P.

  Abstract

  A virtual cell is a world model of a cell: a computational system that predicts, simulates and programs cellular processes across modalities and scales. An important path toward this goal is to model how genetic and chemical perturbations give rise to transcriptional responses, a core capability for disease understanding and drug discovery. However, current approaches remain expert-intensive, relying on iterative manual model design, training and debugging over months. Here we present VCHarness, an autonomous AI system that constructs perturbation-response models by combining an AI coding agent with multimodal biological foundation models. The system explores large spaces of architectures and training pipelines with minimal human intervention, iteratively generating, evaluating and refining candidate models. Across multiple perturbation-response benchmarks, VCHarness identifies architectures that outperform expert-designed approaches while reducing development time from months to days. It further uncovers non-obvious architectural patterns associated with improved performance, indicating that automated search can extend beyond conventional design strategies. These results suggest a shift from manually engineered models toward autonomous systems for constructing components of virtual cell world models, enabling scalable and data-driven exploration of cellular systems.

  bioinformatics2026-04-30v2

• PanVariants: Best Practice for Pangenome-based Variant Calling Pipeline and Framework

  Yi, H.; Wang, L.; Chen, X.; Ding, Y.; Carroll, A.; Chang, P.-C.; Shafin, K.; Xu, L.; Zeng, X.; Zhao, X.; Gong, M.; Wei, X.; Hou, Y.; Ni, M.

  Abstract

  Background: Although pangenome references offer richer population diversity compared to linear references, current mainstream pangenome-based variant callers are limited to detecting only known variants stored in the graph. To address this limitation, we developed PanVariants, a novel pipeline designed to improve the detection of both known and novel variants accurately. We systematically evaluated its performance against the traditional linear alignment solution (BWA+GATK/Manta) and the existing pangenome-aware solution (DRAGEN/PanGenie) in three contexts: small variants (SNVs/indels) and structural variants (SVs) accuracy in Genome in a Bottle samples, clinical detection on positive samples, and application in cohort-based joint calling. Results: By integrating k-mer-based and mapping-based methods, PanVariants significantly reduced variant errors (FPs + FNs), achieving a 73% reduction compared to BWA+GATK and a 45% reduction compared to DRAGEN for SNVs. Retraining the DeepVariant model with high-quality DNBSEQ data further decreased errors by 15%. For SVs detection, PanVariants attained an F1-score of 89.39%, markedly outperforming DRAGEN (68.18%) and BWA+Manta (58.33%), approaching long-read sequencing performance (95.22%). In validation using clinical positive samples, PanVariants successfully detected all expected pathogenic variants while PanGenie failed. In the cohort joint-calling analysis, PanVariants detected more variants, made fewer Mendelian inheritance errors, and gave better per-sample accuracy than GATK. Conclusions: PanVariants establishes a robust framework and best-practice pipeline for pangenome-based variant detection, achieving both sensitive novel variant discovery and high accuracy for SNVs, indels and SVs. Our systematic evaluation of optional processing steps and input variables offers practical guidance for users. Validated across diagnostic and population-based applications, our findings strongly support the transition from linear to pangenome references in future genomics.

  bioinformatics2026-04-30v2

• Overcoming systematic data biases enables accurate prediction of enzyme kcat fold-changes for computational protein design

  Rousset, Y.; Kroll, A.; Lercher, M.

  Abstract

  Machine learning is increasingly used to guide protein engineering by predicting how mutations affect desired properties. Recent models for the turnover number (kcat) of enzymes report high accuracy, suggesting that mutation effects can be inferred directly from protein sequence. However, these approaches are typically evaluated on heterogeneous datasets of enzyme variants, where closely related sequences and systematic reporting patterns may confound model performance. A central challenge is therefore to determine whether current models truly capture mutation-specific effects or instead exploit statistical regularities in the data. Here we show that much of the reported accuracy in mutant kcat prediction arises from two pervasive biases: variants of the same enzyme occupy a narrow activity range, and mutations within a group often share a common direction of change. Simple baselines that exploit these biases match or exceed the performance of existing models, indicating that high apparent accuracy does not imply mechanistic understanding. To address this limitation, we introduce a bias-aware framework that reformulates prediction as a pairwise fold-change task and evaluates performance on unseen mutant-mutant pairs, thereby isolating mutation-specific signal. A proof-of-principle implementation explains approximately one-third of the variance under these conditions and outperforms existing models on leakage-controlled benchmarks. More broadly, this work establishes a general strategy for evaluating and modeling mutation effects in biochemical datasets, with implications for protein engineering and related fields.

  bioinformatics2026-04-30v2

• Distilling Direct Effects via Conditional Differential Gene Expression Analysis

  Gu, J.; Skelton, A.; Staley, J.; Popson, P. O.; Peng, L.; Song, X.; Knowles, J. K.; He, Z.

  Abstract

  Differential gene expression (DGE) analysis is foundational for interpreting RNA sequencing data, but it conflates direct biological effects with correlations propagated through gene co-expression. Across three RNA sequencing datasets (including a genome-scale perturb-seq experiment), we find that only a small fraction of differentially expressed genes have direct effects on the trait of interest, while the majority are undirected or passengers whose associations are mediated through other genes. To distinguish direct effect genes, we introduce conditional differential gene expression (CDGE) analysis, a framework that tests for conditional rather than marginal association between each gene and the trait of interest. Implemented via the GhostKnockoff procedure with lasso regression, CDGE delivers false discovery rate control, operates on summary statistics from existing DGE pipelines, and accommodates batch effects. The genes identified by CDGE mediate the effects of most other differentially expressed genes and show stronger enrichment for known protein-protein interactions and biological pathways than DGE-identified genes. These results suggest that the field has been systematically over-interpreting DGE outputs, and that distinguishing direct from mediated effects is essential for prioritizing genes for functional follow-up and therapeutic development.

  bioinformatics2026-04-30v2

• Comprehensive top-down mass spectral repository enables pan-dataset analysis and top-down spectral prediction

  Li, K.; Liu, K.; Fulcher, J. M.; Kaulich, P. T.; Tang, H.; Liu, X.

  Abstract

  Mass spectral libraries have become essential resources for training deep learning (DL) models for spectral prediction and de novo sequencing in bottom-up mass spectrometry (BU-MS). Compared with BU-MS, top-down MS (TD-MS) offers unique advantages for characterizing intact proteoforms by analyzing proteoforms without enzymatic digestion. Despite these advantages, large-scale spectral libraries for TD-MS are currently lacking. Here we present TopRepo, the first comprehensive and publicly available repository of TD-MS spectra, comprising more than 12 million spectra acquired from 12 species across seven types of mass spectrometers. Using TopRepo, we constructed a large-scale top-down spectral library containing over 5 million spectra with proteoform and fragment-ion annotations. We demonstrate that TopRepo enables pan-dataset analyses of N-terminal processing, mass shifts, and other proteoform characteristics identified by TD-MS. Furthermore, we show that the TopRepo spectral library substantially improves proteoform identification through spectral library searching and supports the training of DL models for high-accuracy top-down spectral prediction.

  bioinformatics2026-04-30v2

• On the consistency of duplication, loss, and deep coalescence gene tree parsimony costs under the multispecies coalescent

  Sapoval, N.; Nakhleh, L.

  Abstract

  Gene tree parsimony (GTP) is a common approach for efficient reconciliation of multiple discordant gene tree phylogenies for the inference of a single species tree. However, despite the popularity of GTP methods due to their low computational costs, prior work has shown that some commonly employed parsimony costs are statistically inconsistent under the multispecies coalescent process. Furthermore, a fine-grained analysis of the inconsistency has indicated potentially complimentary behavior of duplication and deep coalescence costs for symmetric and asymmetric species trees. In this work, we prove inconsistency of GTP estimators for all linear combinations of duplication, loss and deep coalescence scores. We also explore empirical implications of this result evaluating inference results of several GTP cost schemes under varying levels of incomplete lineage sorting.

  bioinformatics2026-04-30v2

• Cross-Species Adaptation of RETFound for Rodent OCT Age Estimation Reveals Strong CNN Baselines in Data-Scarce Space Biology

  Hayati, A.; Gong, J.; Nagesh, V.; Avci, P.; Ong, A. Y.; Masalkhi, M.; Engelmann, J.; Karouia, F.; Keane, P. A.; Costes, S. V.; Sanders, L. M.

  Abstract

  Space-biology imaging studies are often constrained by severe data scarcity, limiting the development of robust machine-learning biomarkers. Rodent spaceflight and space-analog datasets provide an important preclinical setting for testing transfer-learning strategies, but the extent to which human retinal foundation models can generalize to rodent optical coherence tomography (OCT) remains unclear. Here, we benchmark cross-species adaptation of RETFound, a human retinal Vision Transformer pretrained on 1.6 million retinal images, for chronological age prediction from Brown Norway rat OCT B-scans in the NASA Open Science Data Repository dataset OSD-679. We adapted RETFound using Low-Rank Adaptation (LoRA) and evaluated performance on control animals under matched 3-fold rat-level cross-validation. We compared RETFound+LoRA with a strong ImageNet-pretrained Xception baseline under matched protocols and included a scratch/random ViT as a negative-control architecture check. Metrics included mean absolute error (MAE), R2, and inter-eye mean absolute difference (MAD). RETFound+LoRA achieved MAE = 26.20 +/- 5.03 days with R2 = 0.744 +/- 0.049. However, Xception performed better in the primary benchmark (MAE = 19.01 +/- 7.67 days, R2 = 0.853 +/- 0.082), and the matched-fold comparison favored Xception, although this result should be interpreted cautiously given the small number of folds. Inter-eye consistency was maintained across the matched control evaluation, and saliency maps localized model attention to anatomically plausible inner retinal regions. Together, these results show that human retinal foundation models can transfer to rodent OCT in a scientifically useful way, but also that strong CNN baselines may outperform transformer-based models in small-sample cross-species settings. This preprint provides a reproducible benchmark and baseline framework for future retinal biomarker development in space biology.

  bioinformatics2026-04-30v2

• Unlocking Multi-Sample Differential Expression for Spatial Transcriptomics Data with TESSERA

  Constantine, F.; Laszik, Z.; Dudoit, S.; Purdom, E.

  Abstract

  Spatial transcriptomics allows the unprecedented examination of gene expression levels at the resolution of spatially-situated single cells in a high-throughput manner. As the technology is adopted more broadly, studies frequently collect data from multiple tissue samples, which leads to unique challenges that traditional spatial statistical methods are not equipped to handle. In particular, factors that differ across samples, such as different coordinate systems, different numbers and types of cells, different underlying tissue architectures, among others, preclude the application of traditional methods to our new setting. In this work, we propose a novel method, TESSERA, based on a spatial generalized linear model, for analyzing multi-sample spatial transcriptomics count data. Importantly, we provide a mathematical and computational framework for efficient and scalable model fitting and statistical inference to accompany the specification of our model. Our method for fitting the model enables the estimation of a common set of fixed effects across samples. This allows us to address a variety of differential expression questions, such as identification of which genes are differentially expressed between conditions (e.g., diseases, treatments), while accounting for spatial correlation between cells within a sample. We benchmark our proposed method on simulated data and apply it to a spatial transcriptomics dataset of human kidney samples. We find that our method provides a hitherto nonexistent extension to the multi-sample setting while remaining competitive with or outperforming existing algorithms in the single-sample setting.

  bioinformatics2026-04-30v1

• Species-specific transformer models of bacterial gene order and content for genomic surveillance tasks

  Horsfield, S. T.; Wiatrak, M.; McInerney, J. O.; Bentley, S. D.; Colijn, C.; Lees, J. A.

  Abstract

  Transformer models enable functionally meaningful representation of complex biological data, such as nucleotide or protein sequences. Existing foundation transformer models are trained on large multi-domain corpuses of unlabelled DNA or protein data, showing unmatched task generalisation. However, these foundation models are often outperformed on domain-specific tasks by models trained on taxonomically-constrained data, such as gene classification in prokaryotes. By extension, species-specific transformer models hold promise for targeted analyses, given sufficient training data are available. Epidemiological analysis of bacterial pathogens exemplifies the use-case of species-specific transformers, due to the wealth of genome data available, coupled with pathogen-specific analyses carried out during routine and outbreak surveillance. Here, we trained a transformer model, PanBART, on the gene content and gene order of two important and biologically distinct bacterial pathogens, Escherichia coli and Streptococcus pneumoniae, benchmarking against state-of-the-art non-transformer approaches for genomic epidemiology. We show PanBART learns representations of population structure in an unsupervised manner, and can be used to accurately assign genomes to biologically-meaningful sequence clusters. PanBART is also able to identify emergent lineages, differentiating them from pre-existing lineages, and can accurately predict genomes likely to uptake genes involved in antibiotic resistance before a transfer event has occurred. Finally, PanBART can be used to conduct co-selection analysis to identify pairs of genes likely to be found together. Our work demonstrates that species-specific transformer models can be employed in many critical public health scenarios. We lay the groundwork for wider application of such models in epidemiological analysis, and provide scenarios where such models excel.

  bioinformatics2026-04-30v1

• CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis

  Moore, N.; Sargeant, C. J.; Wakefield, M. J.; Popp, N. A.; Fowler, D. M.; Rubin, A. F.

  Abstract

  Deep Mutational Scanning (DMS) experiments generate large volumes of sequencing data that must be processed through multi-step computational pipelines to yield interpretable variant scores. At least twelve dedicated tools have been published for this purpose, yet the diversity of experimental designs, scoring strategies, and software implementations has produced a fragmented landscape in which no single tool accommodates the full range of workflows encountered in practice. Here we present CountESS (Count-based Experiment Scoring and Statistics), an open-source pipeline tool that provides a modular, graphical interface for constructing flexible DMS analysis workflows. CountESS supports a wide range of input formats, barcode translation, HGVS variant calling, and user-defined scoring functions, enabling it to accommodate diverse experimental designs including selection assays, time-series experiments, and bin-based assays such as VAMP-seq. Implemented in Python with DuckDB as a computational backend, the software provides high-performance, memory-efficient processing suitable for large datasets. CountESS is freely available at https://github.com/CountESS-Project/CountESS under the 3-Clause BSD Licence. Supplementary data, including demonstration pipelines and example datasets, are available at https://github.com/CountESS-Project/countess-demo.

  bioinformatics2026-04-30v1

• EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

  Mu, X.; Yang, Y.; Wang, Q.; Chen, Z.; Luo, B.; Huang, Z.; Lin, X.; Xu, L.; Li, X.; Qu, Y.; Xiao, J.; Wang, Z.; Shi, B.; Ou, Q.; Yao, B.; Yan, J.; Zhuang, Y.; Zhang, Y.; Shi, R.; Xu, Y.

  Abstract

  Enzyme kinetic parameters underpin mechanistic biology but remain sparse in physiological context. We present EnzCast, a multi-modal framework jointly predicting Km, kcat, kcat/Km, and Ki from protein sequence, 3D structure, substrate chemistry, and experimental conditions, paired with IsoKin, an isoform-resolved Bayesian framework converting EnzCast priors into patient-specific in vivo kinetics. Trained on KinBench, the largest curated kinetics database, task-adaptive EnzCast achieved R2 = 0.413, 0.455, 0.227, and 0.105 for Km, Ki, kcat, and kcat/Km, surpassing all baselines on catalytic tasks. Systematic condition scans recovered compartment-specific pH direction inversion and pathway-level temperature responses. In a 20-patient colorectal cancer single-cell cohort, IsoKin reduced posterior uncertainty by 73.3% and 77.3%, revealing cell-type-specific rewiring. Orthogonal validation--scFEA flux, DepMap essentiality (permutation P = 0.0008) and TCGA survival--provided mixed but directionally consistent support. Together, EnzCast and IsoKin bridge in vitro prediction, condition-aware biochemical interrogation and patient-resolved in vivo inference.

  bioinformatics2026-04-30v1

• Advances in protein function prediction from the fifth CAFA challenge

  De Paolis Kaluza, M. C.; Ramola, R.; Joshi, P.; Piovesan, D.; Reade, W.; Orchard, S.; Martin, M. J.; Ignatchenko, A.; Kaggle Competition Participants, ; Rost, B.; Orengo, C. A.; Robinson-Rechavi, M.; Durand, D.; Brenner, S. E.; Greene, C. S.; Mooney, S. D.; Friedberg, I.; Radivojac, P.

  Abstract

  The Critical Assessment of Function Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight well-performing methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA 5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements.

  bioinformatics2026-04-30v1

• A Conditional Variational Autoencoder with QSAR-Guided Surrogate-Weighted Fine-Tuning and Cross-Entropy Optimization for Targeted Antimicrobial Peptide Generation

  Castanon, I.; Wan, F.; de la Fuente, C.; Pini, A.; Falciani, C.

  Abstract

  Machine Learning frameworks have emerged as a promising tool for antimicrobial peptide design; however, generative models remain limited by two persistent problems: the limited availability of experimentally validated peptides and the circular dependency of the models. In this work we present a conditional variational autoencoder pipeline that addresses both limitations through a modular architecture that combines both binary and quantitative experimental data and implements a multimodal approach to externally guide the generation. A transformer-based encoder successfully generated a discriminative 64-dimensional latent space (test AUROC 0.968, F1 0.919) separating antimicrobial from non-antimicrobial sequences. This latent representation conditions a species-specific LoRA fine-tuned ProtGPT2 decoder through a scalar gating function, which generates balanced antimicrobial peptides through two different modes; prior and perturb, depending on their generation starting points. We introduced a Surrogate Weighted Fine-Tuning (SWF) ensemble to eliminate the circular dependency and a Cross-Entropy Method to explore and exploit the latent space, leading to successful antimicrobial peptide generation. The best candidates exhibited competitive physicochemical characteristics, a mean helical fraction of 0.874 (mean pLDDT 83.7), and externally predicted efficacy evaluated by APEX.

  bioinformatics2026-04-30v1

• Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease

  Ball, R. L.; Klein, A.; Gerring, M. W.; Berger-Liedtka, A. K.; Kim, M. J.; Berry, M. A.; Gargano, M. A.; Mukherjee, G.; Fisher, H. S.; Nichols-Meade, T.; Castellanos, F.; Smith, C. L.; Karlebach, G.; Murray, S. A.; Bult, C. J.; Robinson, P. N.; Chesler, E. J.

  Abstract

  Choosing an appropriate mouse genetic background is a persistent challenge for successful translation of preclinical disease modeling. We present Strain Recommender, a genomic framework that prioritizes inbred mouse strains as relatively vulnerable or resilient to a disease state using disease-associated gene signatures and strain-specific transcriptome predictions. The method represents disease states as weighted gene scores, ranks 657 strains based on resemblance to the disease state, and estimates uncertainty via a permutation-derived false positive rate (FPR). In a prospective validation of connective tissue disorder predictions, vulnerable and resilient Collaborative Cross strains showed significantly different cardiovascular abnormalities. In a global retrospective validation predicting previously reported strain background effects, Strain Recommender achieved [≥] 90% sensitivity for 86.6% of diseases with 94.4% mean sensitivity (95% CI: 94.0-94.8%) across 5,890 diseases, including 92.3% (95% CI: 91.6-93.0%) for 2,598 rare diseases, demonstrating its potential to improve the validity of mouse models of human disease.

  bioinformatics2026-04-30v1

• Understanding the bias of compositional microbiome differential abundance estimation

  Calle, M. L.; Pujolassos, M.; Susin, A.

  Abstract

  One of the most relevant objectives in microbiome studies is the identification of microbial species that are differentially abundant across conditions. However, the compositional nature of microbiome data complicates this task. Interdependence among components leads to spurious associations when the abundances of each component are analyzed separately. Due to the growing awareness of the challenges of compositional data analysis (CoDA), log-ratio transformations, such as the additive log-ratio (alr) or the centered log-ratio (clr) transformations, have become increasingly popular in microbiome studies. Several studies have compared the performance of compositional and non-compositional methods through simulations. However, the debate between these two frameworks remains unresolved, creating confusion among researchers. Rather than relying on simulation-based results, this work provides theoretical results that enable a more rigorous and conclusive analysis of the problem, contributing to a better understanding of differential abundance estimation. We provide theoretical expressions of the bias of differential abundance estimation related to the use of proportions (total sum scaling) and log-ratio transformations (alr and clr) when estimates are interpreted as absolute rather than relative to a reference. The factors that most strongly influence the bias are the magnitude and direction of the effects, the dimension of the composition, the proportion of differentially abundant variables, and the distribution of relative abundances. The findings of this work strongly support the use of CoDA transformations; however, they also highlight that even when log-ratio transformations are applied, interpreting the results outside of a CoDA framework can still lead to biased conclusions. Among CoDA transformations, alr has several advantages over clr: its reference is more explicit, which reduces the risk of interpreting estimates as absolute rather than relative, and it facilitates the replication of results in independent studies, as it only requires assessing changes relative to the same reference rather than reconstructing the full composition. In this work, we propose a heuristic method for selecting a suitable alr reference component, which will enable a more widespread use of this transformation.

  bioinformatics2026-04-30v1

• linearPOA: A parallel, memory-efficient framework for Partial Order Alignment with linear space complexity

  Wei, Y.; Huang, Z.; Zhang, P.; Tian, Q.; Li, Y.; Zou, Q.; Yu, L.

  Abstract

  Multiple sequence alignment (MSA) is a fundamental problem in computational bioinformatics, playing a critical role in genome biology, especially in long read sequencing and assembly. One solution for representing and solving MSA is Partial Order Alignment (POA), which employs Directed Acyclic Graphs (DAGs) to represent sequence relationships. However, when facing the ultra-long, error-prone reads (e.g., >100 kbps), existing POA algorithms with quadratic space complexity become impractical due to excessive memory consumption. This paper introduces the linearPOA, which based on divide-and-conquer strategy to solve the POA, aimed at saving memory compared to quadratic space complexity algorithms like SPOA, abPOA and TSTA. Particularly notable is its capability to save up to 102.74 times memory usage when aligning sequences with 100 kbp reads, compared to the abPOA method using non-heuristic methods. The algorithm was implemented within the linearPOA library, providing functionality for POA and foundational support for sequencing analysis, like error correction for reads. The linearPOA algorithm provides memory-efficient algorithms for long-read sequencing, especially in directly assembling long reads like 100 kbp reads.

  bioinformatics2026-04-30v1

• TxConformal: Controlling False Discoveries in AI-Driven Therapeutic Discovery

  Jin, Y.; Huang, K.; Diamant, N.; Buchholz, K. R.; Rutherford, S. T.; Skelton, N.; Biancalani, T.; Scalia, G.; Leskovec, J.; Candes, E. J.

  Abstract

  Artificial Intelligence (AI) is transforming therapeutic discovery by scoring a large set of promising candidates and prioritizing a shortlist for further investigation. Quantifying the reliability of AI scores and preventing false positives among selected candidates is key to the efficiency of the discovery process. Conformal prediction (CP) has emerged as a popular tool for guiding such prioritization, especially via the conformal selection framework to control false discovery rates (FDR) in selecting top-ranked candidates under distributional shift. However, deploying these advances in real-world therapeutic discovery remains challenging: distribution shifts are difficult to quantify and correct in high-dimensional biomedical data, and practical workflows often require flexible error metrics. Here, we present TxConformal, a general framework for trustworthy decision making when building shortlists using AI scores. TxConformal adjusts for distribution shift by balancing the hidden representations in AI models and then provides confidence measures for true discoveries of target biological properties. These confidence measures, interpretable as p-values, can be used in conjunction with statistical multiple testing procedures to derive selection decisions with limited false positives or to estimate the errors in given selection decisions. TxConformal controls the false positive rate in six real-world tasks spanning various therapeutic discovery stages, modalities, and AI models with realistic data splits. When selecting promising combinatorial genetic perturbations, TxConformal nearly halves false-positive selections compared to baseline methods, substantially reducing unnecessary experimental costs by tens of thousands of dollars. When selecting stable protein structures under mutant shifts, TxConformal identifies about 10 times more proteins than baseline methods at stringent thresholds when running at a target FDR level of 10\%, recovering over 90\% of valuable candidates that baseline methods miss due to unaccounted distribution shifts. Furthermore, we demonstrate that TxConformal robustly supports various alternative error metrics suitable for resource-constrained settings. Finally, in a prospective fixed-budget virtual screening campaign for novel antibiotic discovery, TxConformal predicted false positives in close agreement with experimental outcomes, with substantial improvements over simple baselines.

  bioinformatics2026-04-30v1

• Systems Pharmacology Reveals Type I Interferon and Myeloid-Like B Cell Reprogramming as Druggable Axes in Antiphospholipid Syndrome

  Sun, B.; Lu, Y.; Liu, W.; Wang, C.

  Abstract

  Antiphospholipid syndrome (APS) lacks targeted therapies beyond anticoagulation, and its molecular heterogeneity remains poorly characterized. We employed an integrative systems pharmacology approach combining weighted gene co-expression network analysis (WGCNA), single-cell RNA sequencing, Connectivity Map (CMap) screening, and molecular docking to identify druggable targets in APS. WGCNA of bulk RNA-seq data from neutrophils (n = 18) and whole blood (n = 88) identified two disease-associated modules: ME10 (176 genes, r = 0.77, interferon-I signaling) and ME2 (3409 genes, r = 0.79, degranulation/innate activation). Single-cell analysis of 26,936 B cells revealed transitional B cells with elevated ME2 scores and aberrant SPI1 expression, suggesting myeloid-like transcriptional reprogramming. CMap analysis ranked chloroquine, a first-line APS therapy, among top ME2 candidates (NCS = -2.07), validating the computational approach. DrugBank mapping identified 14 FDA-approved drugs targeting module genes, and a 3-gene machine learning signature (CORO1A, ANKRD22, IFITM1) achieved cross-tissue validation AUC of 0.802. External validation confirmed ME2 pathway modulation by NAPc2 intervention and cross-tissue module conservation in platelets. Patient-level ME10 x ME2 stratification revealed four molecular subtypes with distinct pathway activation profiles. This framework nominates druggable targets across both IFN-I and degranulation pathways, providing a foundation for pathway-guided precision medicine in APS.

  bioinformatics2026-04-30v1

• Molecular Translators as a Computational Primitive for Biomarker Discovery: Learnability Gains Under Conserved Information Ceilings

  Saisan, P. A.; Patel, S. P.

  Abstract

  Virtual molecular mapping systems such as MISO and GigaTIME introduce a potentially transformative primitive in computational pathology: translation of H\&E whole-slide images into biologically structured molecular representations, learned on paired cohorts and deployed as an inference-time map. Despite sustained progress in machine learning, H\&E-to-molecular-biomarker (e.g., gene mutation) prediction continues to exhibit recurrent field-level performance plateaus whose drivers remain poorly resolved. It remains unclear whether continued optimization targets a removable methodological limitation or instead presses against an intrinsic ceiling imposed by morphology. We develop a formal framework characterizing what deterministic translators can and cannot change. Histology-based biomarker modeling is governed by two constraints: method-limited gaps (finite labels, weak supervision, structured nuisance) and modality-limited ceilings (intrinsic slide-specific information in morphology). Because deterministic translation introduces no new slide-level measurements at inference, H\&E information ceilings are conserved; however, translation can still improve finite-sample learnability, yielding an apparent information--performance paradox that we formalize as learnability gains under conserved information ceilings. We derive falsifiable signatures distinguishing these regimes and characterize them in controlled analytical experiments anchored to representative systems, including MISO and GigaTIME. We introduce an open-source toolkit comprising learning regime diagnosis, information-ceiling estimations, phase analyses, fidelity perturbation tests, and shortcut-confounding stress tests as an operational rubric for identifying and overcoming removable performance plateaus in translator-assisted molecular biomarker discovery and computational pathology.

  bioinformatics2026-04-30v1

• CAPHEINE, or everything and the kitchen sink: a workflow for automating selection analyses using HyPhy

  Verdonk, H. E.; Callan, D.; Kosakovsky Pond, S. L.

  Abstract

  Here we present CAPHEINE, a computational workflow that starts with a set of unaligned pathogen sequences and a reference genome and performs a comprehensive exploratory evolutionary analysis of the input data. CAPHEINE pairs nicely with studies of site-level selection dynamics, gene-level positive selection, and lineage-specific shifts in selective pressure. Our workflow is portable across Mac OS, Windows, and Linux, allowing researchers to focus on results. CAPHEINE is freely available at https://github.com/veg/CAPHEINE, along with a set of usage instructions.

  bioinformatics2026-04-29v3

• Robust metabolomics data normalization across scales and experimental designs

  Vynck, M.; Vangeenderhuysen, P.; De Paepe, E.; Nawrot, T.; Plekhova, V.; Vanhaecke, L.

  Abstract

  Metabolomics studies employing liquid chromatography-mass spectrometry are affected by signal drift and batch effects, introducing technical variance that impedes biological knowledge discovery. Quality control (QC) sample-based normalization strategies are widely implemented but remain vulnerable to outliers, thereby reducing normalization performance. We introduce rLOESS, rGAM and tGAM, three robust normalization methods that improve resistance to outliers by downweighting or accommodating them. Leveraging additive models, the rGAM and tGAM methods allow flexible non-linear modeling, differential sample weighting, and data-driven QC representativeness evaluation. Implementations of these methods are gathered in the Metanorm R package, integrating robust normalization with visualization for performance verification, while supporting efficient parallel processing. In in silico and/or experimental datasets, the robust methods, relative to several popular existing strategies, improved replicate concordance, and reduced drift and batch effects. The robust methods, with improved recovery of the underlying signal demonstrated in simulation, produced distinct differential abundance results, highlighting the impact of normalization on downstream statistical inference. Overall, tGAM-based normalization suggested the best performance across scenarios and is proposed as default choice. Metanorm is versatile, supporting normalization in metabolomics studies across scales and experimental setups.

  bioinformatics2026-04-29v2

• Learning the All-Atom Equilibrium Distribution of Biomolecular Interactions at Scale

  Wang, Y.; Xu, Y.; Li, W.; Yu, H.; Tan, W.; Li, S.; Huang, Q.; Chen, N.; Wu, X.; Wu, Q.; Liu, K.

  Abstract

  Biomolecular functions are governed by dynamic conformational ensembles rather than static structures. While models like AlphaFold have revolutionized static structure prediction, accurately capturing the equilibrium distribution of all-atom biomolecular interactions remains a significant challenge due to the high computational cost of molecular dynamics (MD). We present AnewSampling, a transferable generative foundation framework designed for the high-fidelity sampling of all-atom equilibrium distributions, which is the first model to faithfully reproduce MD at the all-atom level. It uses a quotient-space generative framework to ensure mathematical consistency and leverages the largest self-curated database of protein-ligand trajectories to date, with over 15 million conformations. Statistically, AnewSampling consistently outperforms all prior generative methods on the ATLAS monomer benchmark, and the all-atom capabilities of AnewSampling enable close statistical alignment with ground-truth MD for evaluating atomic biomolecular interactions in protein-ligand dynamics. Furthermore, AnewSampling successfully recovers coupled ligand and side-chain motions in CDK2 systems, overcoming a major sampling hurdle inherent to conventional MD. AnewSampling enables rapid exploration of conformational landscapes prior to intensive simulations, elucidating fundamental biophysical mechanisms and accelerating the broader design of functional biomolecules.

  bioinformatics2026-04-29v2

• Multi-Modal Deep Learning Integrates Spatial Topologies and Sequential Motifs to Identify Class I HDAC Inhibitors as Pan-Cancer Therapeutics

  Tong, S.; Zhang, W.; Ji, S.

  Abstract

  The molecular characterization of human solid growths has introduced immense genomic complexity and intra-tumoral diversification. Converting these detailed, multi-omic profiles right into workable, broad-spectrum therapeutics continues to be an awesome traffic jam in accuracy oncology. Traditional computational drug repurposing strategies largely rely on single-modality chemical descriptors, which frequently fail to capture the systemic transcriptomic interactions within the highly dynamic tumor microenvironment. Here, this study presents a robust multi-modal deep learning framework that synergistically integrates two-dimensional (2D) molecular graphs via Graph Neural Networks (GNNs) and chemical functional group patterns via self-attention Transformers. By mapping this dual-stream chemical feature space to the perturbational transcriptomic signatures (LINCS L1000) of 22 distinct cancer types from The Cancer Genome Atlas (TCGA), a vast library of over 28,000 small-molecule compounds was computationally screened. of over 28,000 small-molecule compounds. The developed multi-modal architecture achieved state-of-the-art predictive accuracy, significantly outperforming traditional single-modality baseline models. Strikingly, our comprehensive pan-cancer transcriptomic reversal landscape identified a persistent convergence of non-oncology drugs exhibiting potent broad-spectrum anti-tumor potential. Specifically, Class I Histone Deacetylase (HDAC) inhibitorsmost notably TC-H-106, RG2833, and Tianeptinaline, agents originally developed to penetrate the blood-brain barrier for neurodegenerative and psychiatric disorderse-merged as top therapeutic candidates across lung adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA), and rectum adenocarcinoma (READ). Subsequent high-dimensional network pharmacology and functional enrichment analyses confirmed that these agents robustly suppress essential oncogenic pathways, specifically collapsing the G1/S phase transition and DNA damage repair machineries. Furthermore, structural validation via molecular docking and force-field thermodynamics confirmed the highly stable physical binding affinity (Vina score: -7.0 kcal/mol, MMFF94 Energy: 64.76 kcal/mol) of TC-H-106 to the HDAC1 catalytic pocket. Kaplan-Meier survival analysis based on TCGA gene expression stratification underscored the significant prognostic benefit of targeting this epigenetic axis. Collectively, these findings introduce a powerful multi-modal AI framework for systems-level drug repurposing and highlight brain-penetrant Class I HDAC inhibitors as highly promising candidates for pan-cancer epigenetic therapy.

  bioinformatics2026-04-29v2

• Using AI to Build AI: AIDO.Builder Enables Autonomous Machine Learning Model Building for Biomedicine

  Guo, H.; Liang, Y.; Cheng, X.; Ellington, C.; Xie, P.; Song, L.; Xing, E.

  Abstract

  Machine learning accelerates biomedical discovery, but creating effective predictive models requires specialized human expertise and demanding manual effort. Researchers must iteratively design pipelines, select architectures, and debug code. This challenge is particularly severe in biomedicine because of the heterogeneous datasets, sparse annotations, and complex evaluation protocols that are common in the domain. We present AIDO.Builder, an agentic artificial intelligence system that fully automates the entire life-cycle of biomedical model development. Provided only with a natural language task description and a target metric, AIDO.Builder autonomously constructs executable training and evaluation pipelines. The system selects suitable modeling strategies, executes experiments, and uses automated feedback-loop to iteratively revise its own code, configurations, and training procedures. It flexibly adapts to new tasks by training specialized models de novo or by using pretrained foundation models to build predictive models through task-appropriate adaptation. We show that across diverse biomedical benchmarks, AIDO.Builder produces highly competitive solutions against human alternatives, while eliminating the manual iteration previously required for robust model development. By automating the translation of raw data into reliable AI models,AIDO.Builder demonstrates how AI itself can be used to accelerate AI for biomedical research.

  bioinformatics2026-04-29v2

• Fast and haplotype-aware assembly of high-fidelity reads based on MSR sketching: the Alice assembler

  Faure, R.; Hilaire, B.; Flot, J.-F.; Lavenier, D.

  Abstract

  Background: Long-read metagenomic assembly is becoming a critical bottleneck in microbiome analysis, as deep sequencing generates massive datasets that existing methods struggle to assemble while maintaining strain resolution. Results: We present Alice, a lightweight long-read assembler that achieves orders-of-magnitude speedups through a new sequence sketching technique, MSR sketching, compatible with classical assembly methods. Alice assembles a 235 Gbp soil metagenome in 5 hours using only 84 GB RAM - a task that causes most competing methods to exhaust our computational resources (500 GB RAM and 7 days runtime). Across diverse benchmarks, Alice delivered strain-resolved assemblies an order of magnitude faster than state-of-the-art approaches, while producing the most complete assemblies in some cases. Conclusions: MSR sketching overcomes computational barriers in metagenomic assembly, enabling fast, memory-efficient strain-resolved analysis of massive datasets. While Alice's assemblies were more fragmented than with other assemblers, this approach establishes a promising paradigm for scalable metagenomic analysis.

  bioinformatics2026-04-29v2

• Identification of different intrinsic sequence patterns between HIV-1 DNA and RNA across subtypes using the k-mer-based approach

  Chen, H.-C.; Wisniewski, J.; Serwin, K.; Parczewski, M.; Kula-Pacurar, A.; Skums, P.; Kirpich, A.; Yakovlev, S.

  Abstract

  Advanced analytical tools that enable mining of the masked features hidden in intricate datasets and strengthening the biological interpretation of multigenomic outputs hold paramount importance. At present, HIV-1 subtyping remains a challenging task in a great part due to analytical tool discordance. To tackle this issue, in this study, we present an updated version of a k-mer-based approach, PORT-EK-v2, a streamlined bioinformatic pipeline, allowing for a comparison of multiple genomic datasets and identification of over-represented genomic regions, k-mers, related to specific origins of datasets. Using PORT-EK-v2, we exemplified that intrinsic sequence patterns between HIV-1 DNA and RNA are distinct across group M HIV-1 subtypes. Furthermore, we showcased that "isolate k-mer count", a predictive variable computed in this work, could serve as a default choice in classifying the HIV-1 DNA versus RNA sequences across subtypes. Lastly, results based on network-based analyses and Markov chain Monte Carlo modeling unveiled a clear discontinuation of a random walk throughout the network properties corresponding to each tested group of HIV-1 subtypes, confirming the specificity of enriched k-mer retrieved by PORT-EK-v2 and the genomic diversity across group M HIV-1 subtypes. Source code for PORT-EK-v2 is at https://github.com/Quantitative-Virology-Research-Group/PORT-EK-version-2 and is freely available.

  bioinformatics2026-04-29v2