Bioinformatics (Oxford, England)[JOURNAL]
Sun
200 papers
treestructure: an R package to detect population structure in phylogenetic trees.
Nascimento FF, Franceschi VB, Volz EM
Bioinformatics · 2026 May · PMID 42024618 · Full text
MOTIVATION: How population structure can shape genetic diversity is a longstanding problem in population genetics. While the use of geographic locations, when available, can help answer some of these questions, it is sti...
MOTIVATION: How population structure can shape genetic diversity is a longstanding problem in population genetics. While the use of geographic locations, when available, can help answer some of these questions, it is still difficult to determine population structure when such metadata are not available or when the potential population structure is not easily observed. Here, we present an updated version of treestructure, an R package that implements a statistical test based on coalescent theory to detect unobserved population structure in a time-scaled phylogenetic tree. AVAILABILITY: treestructure is available at CRAN at https://cloud.r-project.org/web/packages/treestructure/ and at https://emvolz-phylodynamics.github.io/treestructure/.
Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity.
Zhuang H, Gai X, Zhang AR … +3 more , Hou W, Ji Z, Shi P
Bioinformatics · 2026 May · PMID 42024616 · Full text
MOTIVATION: Dimensionality reduction for single-cell RNA-sequencing (scRNA-seq) data involving multiple biological samples presents a significant analytical challenge. RESULTS: We introduce MUlti-Sample Trajectory-Assist...
MOTIVATION: Dimensionality reduction for single-cell RNA-sequencing (scRNA-seq) data involving multiple biological samples presents a significant analytical challenge. RESULTS: We introduce MUlti-Sample Trajectory-Assisted Reduction of Dimensions (MUSTARD), an innovative trajectory-guided dimensionality reduction method specifically designed for multi-sample, multi-condition scRNA-seq data. By integrating pseudotemporal information, MUSTARD provides a comprehensive unsupervised approach that simultaneously captures major gene expression variation patterns along pseudotime trajectories and across multiple samples, facilitating the discovery of biologically meaningful sample heterogeneity, endotypes, and associated gene markers and modules. In data-driven simulations, MUSTARD outperformed existing methods in distinguishing sample groups, achieving superior out-of-sample prediction accuracy. In two COVID-19 datasets and a tuberculosis dataset, MUSTARD identified components linked to symptom severity, batch effect, and other known biological variations, with notable overlap in immune response genes across the two independent COVID-19 datasets. These results underscore MUSTARD's flexibility and power in identifying biologically relevant sample heterogeneity across diverse datasets. AVAILABILITY AND IMPLEMENTATION: The R package MUSTARD with a detailed user manual is publicly available at https://github.com/haotian-zhuang/MUSTARD and Zenodo (DOI: 10.5281/zenodo.18293392). The source code to reproduce the results in this paper is available at https://github.com/haotian-zhuang/MUSTARD_Paper and Zenodo (DOI: 10.5281/zenodo.18293392).
StrucPTM: a database of structurally validated protein modifications and their conformational variation.
Jeon SG, Yoo J, Joo K … +1 more , Paek E
Bioinformatics · 2026 May · PMID 42024582 · Full text
MOTIVATION: Post-translational modifications (PTMs) alter functional states and interaction specificity largely through the conformational changes they impose on protein structure. However, most existing resources remain...
MOTIVATION: Post-translational modifications (PTMs) alter functional states and interaction specificity largely through the conformational changes they impose on protein structure. However, most existing resources remain sequence-centric and cannot reveal how chemical modifications reshape 3D structures. To address this gap, we propose a structural database that systematically extracts and contextualizes modification sites within experimentally determined protein structures, providing a foundation for future studies of protein structure, function, and regulatory mechanisms. RESULTS: We present StrucPTM, a database that extracts modified residues directly from the Protein Data Bank (PDB) structures using atom-level composition rules, substantially expanding coverage beyond annotation-dependent methods. Each validated PTM modification is mapped onto a UniProt entry. The database further characterizes residues using key structural descriptors-including secondary structure, relative solvent accessibility (RSA), and whether the PTM site lies at an interchain interface. All chains associated with the same UniProt ID are compared and grouped into homolog sets based on sequence identity. This emphasizes structural conservation among homologs, allowing PTM-induced conformational deviations to be distinguished from unrelated sequence divergence. AVAILABILITY AND IMPLEMENTATION: StrucPTM offers searchable access, interactive 3D visualization, and homolog-based structural comparison through its web interface: https://prix.hanyang.ac.kr/strucptm. The source code and datasets are permanently archived on Zenodo (DOI: 10.5281/zenodo.18939125) and are accessible via GitHub (https://github.com/HanyangBISLab/StrucPTM.git).
Treemble: a graphical tool to generate Newick strings from phylogenetic tree images.
Allard JB, Kumar S
Bioinformatics · 2026 May · PMID 42018742 · Full text
SUMMARY: Phylogenetic trees are ubiquitous and central to biology, but most published trees are available only as visual diagrams and not in the machine-readable Newick format. There are, thus, thousands of published tre...
SUMMARY: Phylogenetic trees are ubiquitous and central to biology, but most published trees are available only as visual diagrams and not in the machine-readable Newick format. There are, thus, thousands of published trees in the scientific literature that are unavailable for follow-up analyses, comparisons, and supertree construction. Experts can easily read such diagrams, but the manual construction of a Newick string from a diagram is laborious, error-prone, and time-consuming. Previous attempts to semi-automate the reading of tree images relied on image processing techniques. These often encounter difficulties as typical published tree diagrams contain various graphical elements and annotations that overlap the branches, such as error bars on internal nodes. Here we introduce Treemble, a user-friendly desktop application for generating Newick strings from tree images. The user simply clicks to mark node locations, assisted by a deep learning-based node detection tool, and Treemble algorithmically assembles the tree from the node coordinates alone. Treemble also facilitates the automatic reading of tip name labels and can be used for both rectangular and circular trees. AVAILABILITY AND IMPLEMENTATION: Treemble is a native desktop application for macOS and Windows and is freely available, with documentation, at treemble.org. Source code is available at github.com/John-Allard/Treemble. The trained node detection model is available at huggingface.co/John-Allard/treemble-1.
Spark: sparse hierarchical energy minimization for scalable prediction of RNA pseudoknots.
Gray M, Will S, Jabbari H
Bioinformatics · 2026 May · PMID 42015361 · Full text
MOTIVATION: The biological functions of RNAs are tightly connected to their specific RNA structures. As experimental techniques to determine high-accuracy structures are costly and time-consuming, computational predictio...
MOTIVATION: The biological functions of RNAs are tightly connected to their specific RNA structures. As experimental techniques to determine high-accuracy structures are costly and time-consuming, computational prediction approaches became indispensable for biological RNA research; most notably, the prediction of minimum free energy secondary structures. Pseudoknots are prevalent, highly significant structural motifs, yet they are commonly ignored to achieve acceptable efficiency. Existing reliable pseudoknot prediction methods typically have prohibitive complexity. A route to fast scalable pseudoknot prediction was suggested with HFold following the hierarchical folding hypothesis. Recent successful sparsification of the CCJ pseudoknot prediction algorithm in Knotty promises a further boost by introducing this technique to hierarchical folding. RESULTS: We introduce Spark, a sparsified algorithm for predicting pseudoknotted RNA structures. Spark predicts exactly the same minimum-energy structures as its predecessor HFold in the accurate HotKnots 2.0 energy model for pseudoknots. While sparsification maintains exact energy minimization and theoretical complexity, it strongly improves the time and space consumption over HFold. We benchmarked the performance of Spark against HFold and, as a pseudoknot-free baseline, RNAfold. Compared with HFold, Spark substantially reduces both run time and memory usage, while achieving run times close to RNAfold. Across all tested sequence lengths, Spark used the least memory and consistently ran faster than HFold. CONCLUSION: Combining sparsification and hierarchical folding in Spark results in an remarkably fast and memory-efficient tool for the accurate prediction of pseudoknotted RNA structures. Consequently, Spark practically enables pseudoknot prediction in large scale and even for very long RNA sequences. AVAILABILITY: Spark software is available on Github (https://github.com/TheCOBRALab/Spark), with a permanent archive of the software and results deposited on Zenodo (https://doi.org/10.5281/zenodo.19073315).
NAViFluX: a visualization‑centric platform for interactive analysis, refinement and design of genome‑scale metabolic networks.
Krishnamurthy MB, Harish PS, Subramanian A
Bioinformatics · 2026 May · PMID 42011154 · Full text
MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, arc...
MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, architectural complexity, and limited visualization support impede its adoption by the broader scientific community. Existing tools exclusively specialize in GSMN analyses or visualization while lacking important features such as pathway-specific views, database-integrated refinement, and comprehensive enrichment and perturbation analyses. RESULTS: Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based tool that unifies native pathway/subsystem map generation, interactive model refinement via KEGG/BiGG, pathway merging and modules for flux computations, topology, and functional enrichment all within network views. Using three independent case studies on Escherichia coli, the utility of NAViFluX for characterization of nutrient-specific metabolic adaptations, enhancing gene essentiality predictions and interpretability, and rational design of an optimized carbon-fixing metabolic state is demonstrated. AVAILABILITY AND IMPLEMENTATION: All source code and supplementary files associated with the case studies are publicly available via Zenodo at https://zenodo.org/records/19107831. NAViFluX can be easily installed as a standalone software through https://github.com/bnsb-lab-iith/NAViFluX.
Reconstructing cell-cell interaction network in single-cell spatial transcriptomics via directed heterogeneous graph autoencoder.
Hu JX, Pan X, Yuan Y … +1 more , Shen HB
Bioinformatics · 2026 May · PMID 41999209 · Full text
MOTIVATION: Spatial transcriptome data have both gene expression information and cell spatial location information, offering exceptional prospects for analyzing cell-cell interaction (CCI) network. Most existing statisti...
MOTIVATION: Spatial transcriptome data have both gene expression information and cell spatial location information, offering exceptional prospects for analyzing cell-cell interaction (CCI) network. Most existing statistical and optimal transport-based methods rely only on known ligand-receptor pairs to infer CCI network. Furthermore, most current deep learning frameworks rely on symmetric decoders or undirected graph architectures. RESULTS: Taking advantage of spatial transcriptomic data and graph autoencoders, we present a directed heterogeneous graph autoencoder-based approach DualCellChat to reconstruct a complete and accurate CCI network from incomplete single cell spatial transcriptomics. Benchmarked on five single-cell spatial datasets from four different technologies, we demonstrate that DualCellChat outperforms existing deep learning-based methods and can inherently model the direction of cellular interactions. Furthermore, we introduce downstream analysis to infer signature genes involved in cellular interactions from the reconstructed CCI network and infer significant ligand-receptor pairs for specific cell types. AVAILABILITY AND IMPLEMENTATION: The dataset and code are available in GitHub (https://github.com/JinxianHu/DualCellChat) and Zenodo (DOI: 10.5281/zenodo.18512678).
HySimODE: a hybrid stochastic-deterministic simulation framework for multiscale models of biological systems.
Zamora-Chimal CG, Darlington APS
Bioinformatics · 2026 May · PMID 41999207 · Full text
Hybrid simulation is essential for modeling biochemical systems that mix low-copy stochastic dynamics with high-abundance deterministic processes. We present HySimODE, a Python framework that automates hybrid simulation...
Hybrid simulation is essential for modeling biochemical systems that mix low-copy stochastic dynamics with high-abundance deterministic processes. We present HySimODE, a Python framework that automates hybrid simulation directly from user-defined ordinary differential equation-based models. HySimODE uses a short deterministic pre-simulation and a machine-learning classifier to automatically assign each species to a stochastic or deterministic regime, and then combines a simple stochastic update rule with a stiff ODE solver in a single simulation loop. The classifier was trained and validated on a diverse dataset of biochemical ODE models spanning multiple dynamical regimes, enabling robust stochastic-deterministic partitioning beyond simple abundance thresholds. This design eliminates manual specification of regimes, avoids model reformulation, and enables reproducible, data-driven hybrid simulations of ODE-only biochemical models, including systems with saturable kinetics, effective-rate laws, or macro-energetic variables that lack a consistent reaction-network representation. Benchmarking against deterministic integrators, stochastic simulations, and abundance-threshold hybrid approaches demonstrates that HySimODE provides a practical and scalable framework for hybrid simulation of ODE-defined biochemical systems. We demonstrate its utility on two distinct case studies: a host-circuit interaction model from synthetic biology and a long-term synaptic potentiation model from neurobiology. HySimODE includes a modular adapter system that automatically converts ODE models written in concentrations into molecular counts, enabling universal compatibility across biochemical systems without code modification.
BABAPPAlign: a multiple sequence alignment engine with a learned residue-level scoring function.
Sinha K
Bioinformatics · 2026 May · PMID 41992497 · Full text
MOTIVATION: Multiple sequence alignment (MSA) remains a core problem in bioinformatics, yet most widely used alignment methods still rely on static amino acid substitution matrices that cannot adapt to sequence-specific...
MOTIVATION: Multiple sequence alignment (MSA) remains a core problem in bioinformatics, yet most widely used alignment methods still rely on static amino acid substitution matrices that cannot adapt to sequence-specific context. RESULTS: BABAPPAlign is a progressive MSA engine that replaces static substitution scoring with a trained residue-level scorer operating on fixed protein-language-model embeddings, while retaining exact affine-gap dynamic programming. It also provides an integrated codon-aware alignment mode. Using BAliBASE as the primary inferential benchmark, with supporting external validation on deterministic subsets of PREFAB and HOMSTRAD, the learned backend outperformed matched in-engine EBA-style cosine and BLOSUM62 controls, and also exceeded MAFFT. AVAILABILITY AND IMPLEMENTATION: BABAPPAlign is implemented in Python and distributed as an open-source command-line package through PyPI; the source code is available at https://github.com/sinhakrishnendu/BABAPPAlign, the archived software release is available at https://doi.org/10.5281/zenodo.17934124, and the pretrained model weights are available at https://doi.org/10.5281/zenodo.18053200. SUPPLEMENTARY MATERIAL: Supplementary material is available at Bioinformatics online.
A large-scale neutral comparison study of survival models on low-dimensional data.
Burk L, Zobolas J, Bischl B … +3 more , Bender A, Wright MN, Sonabend R
Bioinformatics · 2026 May · PMID 41992491 · Full text
MOTIVATION: This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to...
MOTIVATION: This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to scientifically compare new and existing model classes through proper empirical evaluation. Existing benchmarks in the survival literature are smaller in scale regarding the number of used datasets and extent of empirical evaluation. They often lack appropriate tuning or evaluation procedures, while other comparison studies focus on qualitative reviews rather than quantitative comparisons. This comprehensive study aims to fill the gap by neutrally evaluating a broad range of methods and providing generalizable guidelines for practitioners. RESULTS: We benchmark 21 models, ranging from classical statistical approaches to many common machine learning methods, on 34 publicly available datasets. The benchmark tunes models using both a discrimination measure (Harrell's C-index) and a scoring rule (Integrated Survival Brier Score), and evaluates them across six metrics covering discrimination, calibration, and overall predictive performance. Despite superior average ranks in overall predictive performance from individual learners like oblique random survival forests and likelihood-based boosting, and better discrimination rankings from multiple boosting- and tree-based methods as well as parametric survival models, no method statistically significantly outperforms the commonly used Cox proportional hazards model for either tuning measure. We conclude that while the Cox Proportional Hazards model remains a robust default for low-dimensional, right-censored survival data, more flexible methods may be preferable for specific dataset characteristics. AVAILABILITY AND IMPLEMENTATION: All code, data, and results are publicly available on GitHub https://github.com/slds-lmu/paper_2023_survival_benchmark and archived on Zenodo https://doi.org/10.5281/zenodo.19075310.
From atoms to fragments: a coarse representation for efficient and functional protein design.
Castorina LV, Wood CW, Subr K
Bioinformatics · 2026 May · PMID 41987573 · Full text
MOTIVATION: Although deep learning has accelerated protein design, current protein representations such as sequences or full-atom structures scale non-linearly with protein length. We propose a sparse and interpretable r...
MOTIVATION: Although deep learning has accelerated protein design, current protein representations such as sequences or full-atom structures scale non-linearly with protein length. We propose a sparse and interpretable representation for proteins, based on evolutionarily conserved fragments. Specifically, we use a curated set of 40 functional and evolutionarily conserved fragments as an alphabet to build Fragment Graphs and Fragment Sets. These fragment-based representations are both lightweight and functionally informative, capturing up to 55% more variance using fewer than 13 of the dimensions required by traditional methods. RESULTS: On a dataset of 215 functionally diverse proteins, our approach creates more coherent functional clusters than traditional sequence- and structure-based methods, even among proteins with ≤30% sequence identity. Fragment-based searches of protein databases achieve accuracies comparable to traditional methods, while using 90% fewer tokens per protein. These searches execute ∼68.7× faster than RMSD-based structural methods and ∼1.64× faster than sequence-based methods, even including fragment pre-processing overhead. Additionally, we show that our representation effectively guides RFDiffusion for protein backbone generation with functional recovery rates higher than 40%. In summary, our fragment-based representation offers a scalable and interpretable alternative for the next generation of protein design tools for backbone design, sequence design, and functional similarity searches within protein structure databases. AVAILABILITY: https://github.com/wells-wood-research/tessera.
QCatch: a framework for quality control assessment and analysis of single-cell sequencing data.
Gao Y, He D, Patro R
Bioinformatics · 2026 May · PMID 41987571 · Full text
MOTIVATION: Single-cell sequencing data analysis requires robust quality control (QC) to mitigate technical artifacts and ensure reliable downstream results. While tools like alevin-fry and simpleaf (and augmented execut...
MOTIVATION: Single-cell sequencing data analysis requires robust quality control (QC) to mitigate technical artifacts and ensure reliable downstream results. While tools like alevin-fry and simpleaf (and augmented execution context for the alevin-fry), offer flexibility and computational efficiency to process single-cell data, this ecosystem will further benefit from a standardized QC reporting tailored for its outputs. RESULTS: We introduce QCatch, a Python-based command-line tool that generates comprehensive and interactive HTML QC reports designed specifically for single-cell quantification results. Taking the output directory of alevin-fry or simpleaf as the input, QCatch is able to perform essential processing steps, like cell calling, and generate detailed QC reports that contain informative visualizations and statistics, including unique molecular identifier (UMI) count distributions, sequencing saturation estimates, and splicing status information, for QC assurance. Built for seamless integration into downstream analysis workflows, QCatch exports the processed results in a richly-annotated H5AD format file, a widely used data format common among many downstream single-cell data analysis tools. AVAILABILITY AND IMPLEMENTATION: The source code and documentation of QCatch are available on GitHub at https://github.com/COMBINE-lab/QCatch. QCatch can be installed via both Bioconda and PyPI.
Fast prediction of protein flexibility.
Pražnikar J
Bioinformatics · 2026 Apr · PMID 41984823 · Full text
MOTIVATION: Advances in hardware have made molecular dynamics (MD) simulations of protein structures faster and more accessible to the scientific community. However, accurately estimating protein flexibility using MD rem...
MOTIVATION: Advances in hardware have made molecular dynamics (MD) simulations of protein structures faster and more accessible to the scientific community. However, accurately estimating protein flexibility using MD remains computationally demanding, especially for large systems and long time scales. Several MD-based resources-including MdMD, the DynamD database, and more recently ATLAS and mdCATH-now provide MD trajectories for thousands of proteins, enabling the development of predictive models. RESULTS: Here, the Graphlet Degree Vector (GDV) is introduced as a lightweight, fast, and easy-to-implement linear model for predicting protein flexibility directly from atom coordinates. GDV is a 15-dimensional feature vector that captures local packing and the spatial connectivity of each atom with its nearby neighbors. Trained on a subset of globular-like proteins from the ATLAS database, the GDV model achieves a Spearman correlation of 0.828 compared to MD data. The model trained on ATLAS dataset was further evaluated on independent Nuclear Magnetic Resonance and cryo-electron microscopy datasets, demonstrating the robustness and generalizability of the GDV-based approach. A key advantage of the GDV model is that it requires no additional external or experimental data and can be applied in near real time (on the order of 10 seconds) even for large proteins with 20 000 atoms on a standard desktop or laptop. Overall, the results show that a lightweight, fast, and purely coordinate-based model can provide accurate and generalizable predictions of protein flexibility across diverse folds and sizes. AVAILABILITY: The source code is available in the GitHub repository https://github.com/jure-praznikar/FastProtFlex. The data required for model training are available at https://doi.org/10.5281/zenodo.17771418. CONTACT: jure.praznikar@upr.si.
MAISNet: a multi-species integrated graph neural network for acetylcholinesterase inhibitor screening.
Shao D, Liang S, Xiong Y … +1 more , Liang G
Bioinformatics · 2026 Apr · PMID 41984821 · Full text
MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as Alzheimer's disease (AD). Although existing drugs can temporarily alleviate symptoms, none have been proven to delay...
MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as Alzheimer's disease (AD). Although existing drugs can temporarily alleviate symptoms, none have been proven to delay or prevent disease progression. Acetylcholinesterase inhibitors (AChEIs) have been shown to mitigate AD symptoms, yet traditional AChEI screening approaches remain time-consuming and inefficient. RESULTS: To address this limitation, we developed multi-species AChEI screening network (MAISNet), an AChEI screening framework based on acetylcholinesterase (AChE) data from six species. In MAISNet, inhibitor molecules were represented as SMILES-derived molecular graphs, whereas AChE protein structures were encoded as residue contact maps. Multi-scale molecular and protein features were extracted using the sample and aggregate (GraphSAGE) network and the graph attention network, respectively, and were subsequently fused through a bidirectional cross-attention mechanism. The integrated representations were then processed by a multilayer perceptron (MLP) to inhibitor classification. On both internal and external validation sets, MAISNet consistently outperformed five baseline models. Furthermore, we applied MAISNet to screen existing small molecules, and Methyl 2-[(3S)-3-(1, 2, 3, 4, 5, 6, 7, 8-octahydro-2-naphthyl)-2-(methoxycarbonyl)-1H-pyrrol-1-yl]acetate subsequently emerged as the top-ranked candidate. Overall, MAISNet significantly improves the accuracy and generalization capability of AChEI screening, providing an efficient and reliable computational tool for accelerating therapeutic discovery for AD. AVAILABILITY AND IMPLEMENTATION: Code that supports the reported results can be found at: https://github.com/liangshengjie111/MAISNet. The archival version of the code is preserved on Zenodo at https://doi.org/10.5281/zenodo.18721665.
Bidirectional Mamba-2 boosts EEG super-resolution via regression and diffusion.
Lomoio U, Lió P, Guzzi PH … +1 more , Veltri P
Bioinformatics · 2026 May · PMID 41984820 · Full text
MOTIVATIONS: Electroencephalography (EEG) is a non-invasive method that records brain electrical activity from scalp electrodes, offering millisecond temporal resolution but limited spatial detail due to sparse sensor la...
MOTIVATIONS: Electroencephalography (EEG) is a non-invasive method that records brain electrical activity from scalp electrodes, offering millisecond temporal resolution but limited spatial detail due to sparse sensor layouts. RESULTS: We present DiBiMa-EEGSR, a bidirectional Mamba-2 diffusion framework for spatio-temporal EEG super-resolution that reconstructs high-resolution signals from standard low-density recordings without additional hardware. The method formulates super-resolution as conditional generative inference and integrates a diffusion process with a bidirectional state-space backbone to model long-range temporal dependencies with linear complexity. Conditioning on low-resolution inputs, electrode positions and task labels enables anatomically coherent and context-aware reconstruction. A one-step sampling strategy substantially reduces inference time while preserving fidelity. Across two public benchmarks, the approach improves reconstruction accuracy, spatial coherence and spectral preservation over convolutional, transformer-based and prior diffusion models in both spatial and temporal upsampling tasks, providing a scalable pathway toward high-resolution electrophysiological imaging. AVAILABILITY AND IMPLEMENTATION: Code to reproduce ablation experiments, training and evaluation of the proposed BiMa and DiBiMa EEGSR models are available at https://github.com/UgoLomoio/DiBiMa-EEGSR.git. Model weights are available at https://huggingface.co/Ugo96/DiBiMa-EEGSR while an interactive demo for EEG spatial super-resolution using our models can be found at https://huggingface.co/spaces/Ugo96/DiBiMa-EEGSR-Demo.
Singletrack: an algorithm for improving memory consumption and performance of gap-affine sequence alignment.
López-Villellas L, Iñiguez C, Jiménez-Blanco A … +5 more , Aguado-Puig Q, Moretó M, Alastruey-Benedé J, Ibáñez P, Marco-Sola S
Bioinformatics · 2026 May · PMID 41981735 · Full text
MOTIVATION: Advances in DNA sequencing have outpaced advances in computation, making sequence alignment a major bottleneck in genome data analyses. Classical dynamic programming (DP) algorithms are particularly memory-in...
MOTIVATION: Advances in DNA sequencing have outpaced advances in computation, making sequence alignment a major bottleneck in genome data analyses. Classical dynamic programming (DP) algorithms are particularly memory-intensive, especially when computing gap-affine and dual gap-affine alignments. Existing strategies to reduce memory consumption often sacrifice speed or alignment accuracy. RESULTS: We present Singletrack, an efficient algorithm for backtrace gap-affine and dual gap-affine alignments that requires storing a single DP matrix while preserving optimal alignment results. Compared to classical DP algorithms, Singletrack removes the need to store additional matrices (i.e. 2 for gap-affine and 4 for dual gap-affine), significantly reducing memory consumption and, in turn, reducing pressure on the memory hierarchy and improving overall performance. Most importantly, Singletrack is a general backtrace method compatible with state-of-the-art DP-based algorithms and heuristics, such as the Suzuki-Kasahara (SK) and the Wavefront Alignment (WFA) algorithms. We demonstrate that Singletrack reduces memory consumption for both SK and WFA algorithms, lowering SK usage by 2× and 4× and WFA usage by 3× and 5× for gap-affine and dual gap-affine alignments, respectively. Moreover, replacing KSW2's memory-reduction technique with Singletrack accelerates its SK implementation by up to 1.4× at the cost of doubling memory consumption, while Singletrack increases the performance of the WFA implementation in WFA2-lib by 1.2-2.1×. Compared to the efficient linear-memory BiWFA algorithm, the Singletrack-accelerated version of WFA trades a practical increase in memory usage for up to 5.2× higher performance. AVAILABILITY AND IMPLEMENTATION: The Singletrack implementations presented in this work are available on Zenodo (DOI: 10.5281/zenodo.18770585) and GitHub (https://github.com/LorienLV/singletrack).
Molecular fingerprints are strong models for peptide function prediction.
Adamczyk J, Ludynia P, Czech W
Bioinformatics · 2026 May · PMID 41981726 · Full text
MOTIVATION: Understanding peptide properties is often assumed to require modeling long-range molecular interactions, motivating complex graph neural networks and pretrained transformers. Whether such long-range dependenc...
MOTIVATION: Understanding peptide properties is often assumed to require modeling long-range molecular interactions, motivating complex graph neural networks and pretrained transformers. Whether such long-range dependencies are essential remains unclear. We investigate if simple, domain-specific molecular fingerprints can capture peptide function without these assumptions. Atomic-level representations aim to provide richer information than purely sequence-based models and better efficiency than structural ones. RESULTS: Across 132 datasets, including LRGB and five additional peptide benchmarks, models using count-based ECFP, Topological Torsion, and RDKit fingerprints with LightGBM achieve state-of-the-art accuracy. Despite encoding only short-range molecular features, these models outperform GNNs and transformer-based approaches. Control experiments confirm that fingerprints, though inherently local, suffice for robust peptide property prediction. Our results challenge the presumed necessity of long-range interaction modeling and highlight molecular fingerprints as efficient, interpretable, and lightweight alternatives. SUPPLEMENTARY INFORMATION: All code and data are available on GitHub and Zenodo: https://github.com/scikit-fingerprints/peptides_molecular_fingerprints_classification https://doi.org/10.5281/zenodo.19388783.
Harvesting more reads from single-cell combinatorial barcoding data with scarecrow.
Wragg D, Kang E, Morgan MD
Bioinformatics · 2026 Apr · PMID 41967853 · Full text
SUMMARY: Combinatorial barcoding technologies for single-cell nucleotide sequencing, such as split-pool ligation protocols, involve sequential rounds of cell barcoding to uniquely tag individual cells. The rapid adoption...
SUMMARY: Combinatorial barcoding technologies for single-cell nucleotide sequencing, such as split-pool ligation protocols, involve sequential rounds of cell barcoding to uniquely tag individual cells. The rapid adoption of combinatorial barcoding in recent years is due in part to its scalability across cells and samples. However, small shifts in barcode positions within sequencing reads caused by technical artifacts, e.g. during barcode incorporation or synthesis, can impact the accurate assignment of reads to cell barcodes. Existing processing tools typically assume barcodes contain fixed-length nucleotide sequences located at fixed positions within reads, overlooking any positional variability. Consequently, reads containing truncated or mispositioned barcodes are discarded during initial data processing steps leading to significant data loss. To solve this limitation and maximize the retention of sequencing reads from single-cell combinatorial barcoding experiments, we introduce scarecrow. This tool screens a subsample of reads to generate position-specific barcode profiles, which are then used to flexibly identify barcode sequences in each read whilst accounting for positional errors, a phenomenon we refer to as "jitter". Barcode matches are then prioritized to minimize nucleotide mismatches and the degree of jitter. These initial profiles are subsequently used to extract and error correct barcode combinations in high throughput sequencing libraries. By incorporating jitter into barcode error correction, scarecrow enables greater data recovery and improved downstream single-cell analyses. Scarecrow is fully open access, implemented in Python, and generates output files using standardized sequence file formats for maximal interoperability. A detailed explanation of the scarecrow workflow can be found in the supplementary materials. AVAILABILITY AND IMPLEMENTATION: Scarecrow is freely available on GitHub https://github.com/MorganResearchLab/scarecrow and Zenodo https://doi.org/10.5281/zenodo.18621784.
DrugBLIP: exploring the protein-molecule interaction mechanisms with a multi-task learning graph transformer.
Wang R, Gao X, Zhao P
Bioinformatics · 2026 Apr · PMID 41967848 · Full text
MOTIVATION: Traditional drug discovery methods are costly and inefficient, while existing deep learning approaches remain limited by task specificity and practical applicability. Accurately modeling protein-molecule inte...
MOTIVATION: Traditional drug discovery methods are costly and inefficient, while existing deep learning approaches remain limited by task specificity and practical applicability. Accurately modeling protein-molecule interactions is critical for advancing virtual screening, docking, and drug design. RESULTS: We propose DrugBLIP, a multi-task graph transformer model based on SE(3)-equivariant architectures, to unify protein-molecule interaction learning. By integrating contrastive learning, matching tasks, and docking optimization, DrugBLIP captures 3D spatial relationships through a hybrid graph transformer framework. Evaluations demonstrate state-of-the-art performance: DrugBLIP achieves an AUROC of 0.8217 and BEDROC of 0.5743 on virtual screening, outperforming traditional and deep learning baselines by 10%-127% across metrics. It also attains 91.2% top-1 docking success on CASF-2016 and 41.8% target fishing accuracy, showcasing robustness in diverse scenarios. Additionally, DrugBLIP reduces computational time by 700× compared to traditional docking tools. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/Wolkenwandler/DrugBLIP and archived at Zenodo with DOI: 10.5281/zenodo.16990700.
DTH: a nonparametric test for homogeneity of multivariate dispersions.
Roy A, Lu J, Satten GA … +1 more , Zhao N
Bioinformatics · 2026 May · PMID 41967847 · Full text
MOTIVATION: Testing for differences in within-group dispersion is a fundamental problem in multivariate data analysis, with direct implications for interpreting group structure and validating statistical assumptions of o...
MOTIVATION: Testing for differences in within-group dispersion is a fundamental problem in multivariate data analysis, with direct implications for interpreting group structure and validating statistical assumptions of other analysis such as ANOVA. Existing methods typically construct test statistics either based on the distance of each observation from the group center or on the mean of pairwise dissimilarities among observations within a group. Both approaches can fail when the mean within-group distance is similar across groups but the distributions of the within-group distances differ. This issue is particularly relevant in high-dimensional microbiome data, where outliers and overdispersion can distort the performance of mean-dissimilarity-based tests. RESULTS: We introduce the non-parametric Distance-based Test for Homogeneity (DTH), which measures dispersion of a group by computing within-group dissimilarity. Difference in dispersion across groups is tested by comparing the distributions of the within-group dissimilarity across different groups. A combination of Kolmogorov-Smirnov and Wasserstein distances are used to construct the difference between the distributions. For more than two groups, pairwise group tests are combined using a permutation-based p-value. Through simulations, we show that our method has higher power than existing tests for homogeneity in certain situations and comparable power in others. For continuous covariates, we offer an heuristic extension of DTH that showed good performance in simulations. AVAILABILITY AND IMPLEMENTATION: The DTH package, along with the code for reproducing all simulations, analyses, and an accompanying vignette, is available at https://github.com/asmita112358/DTH.
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