Bioinformatics (Oxford, England)最新文献

{"title":"ScopeNovo: a de novo peptide sequencing method using spectrum-spectrum similarity to refine the prediction accuracy.","authors":"Zhi Song, Zejian Ding, Lusheng Wang","doi":"10.1093/bioinformatics/btag755","DOIUrl":"10.1093/bioinformatics/btag755","url":null,"abstract":"<p><strong>Motivation: </strong>De novo peptide sequencing from tandem mass spectra is essential for characterizing unknown proteins, identifying novel post-translational modifications, and analysing samples without reference databases. While recent deep learning models have improved the predicted peptide accuracy, they typically select the predicted peptide sequence based solely on sequence-level decoding scores. Very often, high scores are assigned to peptides without the direct support from the observed mass spectrum.</p><p><strong>Results: </strong>We present \"ScopeNovo\", a de novo peptide sequencing method that improves predicted peptide accuracy by using spectrum-spectrum similarity between the input spectrum and the \"representative\" spectrum corresponding to the predicted peptide sequence. ScopeNovo generates multiple candidate peptides using an existing peptide de novo sequencing learning model and reranks candidates based on spectrum-spectrum similarity. To test our method, we use π-PrimeNovo as the base learning model. Experimental results on the standard nine-species benchmark datasets V1 and V2 show that ScopeNovo can substantially improve the prediction accuracy. We designed two settings, where Setting 1 is for well-annotated species, and Setting 2 is for species that are not well annotated. For Setting 1, ScopeNovo improves the average peptide accuracies of π-PrimeNovo over the nine species by 6.1% and 4.0% on V1 and V2, respectively. For Setting 2, ScopeNovo can still improve the peptide accuracy of π-PrimeNovo over the nine species by 3.4% and 1.9% on the V1 and V2 benchmark datasets, respectively. Compared to the best-known method, RankNovo, the peptide accuracies over the nine species on V1 and V2 of our method for Setting 1 are 0.711 and 0.737, where the peptide accuracies for RankNovo are 0.687 and 0.682. Even for Setting 2, the peptide accuracies of our method are comparable to those of RankNovo.</p><p><strong>Availability and implementation: </strong>ScopeNovo is implemented in Python under an MIT license. The source code is freely available at https://github.com/SoSongzhi/ScopeNovo.git. The test data are available at https://doi.org/10.5281/zenodo.23181841.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13682837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FA-MFMR: a multivariable functional Mendelian randomization method that accounts for correlated longitudinal exposures via factor augmentation.","authors":"Dong Chen, Junrong Deng, Siyuan Shen, Yuyang Zhou, Hanyu Cui, Yu Qiu, Qiming Li, Yue-Qing Hu","doi":"10.1093/bioinformatics/btag725","DOIUrl":"10.1093/bioinformatics/btag725","url":null,"abstract":"<p><strong>Motivation: </strong>Mendelian randomization (MR) is widely used for causal inference using genetic data, yet most existing MR methods treat exposures as static and have limited capacity to analyze longitudinal measurements. In many biomedical studies, multiple exposures evolve over time, are strongly correlated, and share unobserved temporal structure. Ignoring these features can lead to unstable estimation and reduced power.</p><p><strong>Results: </strong>We propose factor-augmented multivariable functional Mendelian randomization (FA-MFMR) for estimating time-varying causal effects of multiple longitudinal exposures. FA-MFMR integrates genetic instruments with functional representations of exposure trajectories and a low-dimensional factor structure to capture shared temporal patterns. By separating common temporal variation from exposure-specific components, it mitigates multicollinearity, improves estimation stability, and accommodates sparse causal structures in which some exposures have no effect throughout the observation period. Simulations demonstrate lower estimation error and more efficient uncertainty quantification than competing approaches, particularly for highly correlated and sparsely observed longitudinal exposures. Applied to longitudinal biomarkers in a Parkinson's disease cohort, FA-MFMR characterizes age-dependent patterns in the estimated trajectory-level coefficient functions while highlighting uncertainty in localized effects.</p><p><strong>Availability and implementation: </strong>The developed R package and code to reproduce all the results are available at https://github.com/YQHuFD/FA-MFMR.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13658255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CRISPR-HAWK: haplotype- and variant-aware guide design toolkit for CRISPR-Cas.","authors":"Alisa Kumbara, Manuel Tognon, Giulia Carone, Arianna Fontanesi, Nicola Bombieri, Rosalba Giugno, Luca Pinello","doi":"10.1093/bioinformatics/btag708","DOIUrl":"10.1093/bioinformatics/btag708","url":null,"abstract":"<p><strong>Motivation: </strong>Current CRISPR guide RNA design tools rely on reference genomes, overlooking how genetic variation impacts editing outcomes. As genome editing advances toward clinical applications, incorporating population diversity becomes essential for ensuring therapeutic efficacy across diverse populations.</p><p><strong>Results: </strong>We present CRISPR-HAWK, a framework integrating individual- and population-scale variants and haplotypes into gRNA design. Analyzing therapeutic targets across 79 648 genomes reveals that genetic variants substantially alter guide performance. For the clinically approved sickle cell disease therapeutic guide targeting BCL11A, we identify haplotypes that completely abolish predicted cutting activity. Across seven therapeutic loci, 82.5% of guides contain variants modifying on-target activity. Variants also create novel protospacer adjacent motif sites generating individual-specific guides invisible to reference-based design. These findings demonstrate that variant-aware selection is critical for equitable genome editing.</p><p><strong>Availability: </strong>CRISPR-HAWK is available at https://github.com/pinellolab/CRISPR-HAWK and https://github.com/InfOmics/CRISPR-HAWK.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic graph prior-guided latent adaptation for generalizable single-cell perturbation response prediction.","authors":"Yunlong Bie, Peishan Fan, Qijin Yin, Li Xiao","doi":"10.1093/bioinformatics/btag747","DOIUrl":"10.1093/bioinformatics/btag747","url":null,"abstract":"<p><strong>Motivation: </strong>Predicting transcriptional responses to drug perturbations in unseen cellular contexts is a central challenge in single-cell perturbation modeling. This challenge creates a need for models that combine flexible state transitions with structured biological prior knowledge. Existing data-driven models often struggle to generalize across unseen cell types, while many prior-based models rely on static biological graphs without explicitly conditioning prior influence on perturbation-specific target support.</p><p><strong>Results: </strong>We propose DGP-LA, a dynamic graph-prior latent adaptation framework that refines biological priors and injects them as gated latent corrections within a cell-state-conditioned transition model. DGP-LA couples a transition backbone with perturbation-conditioned sparse graph refinement. It propagates gene-aligned target support over the refined graph and selectively injects the resulting topology-aware signal into the hidden representation. On Tahoe and NeurIPS, DGP-LA improves Pearson Delta relative to State by 8.7% and 35.8% for generalization across unseen cell types. In zero-shot drug perturbation response prediction on unseen cell types, it improves Pearson Delta relative to CRISP by 79.1% and 67.6%. The model also improves DE-gene recovery, response discrimination, and Hallmark pathway recovery, supporting adaptive latent integration of biological priors for more generalizable and biologically interpretable perturbation modeling.</p><p><strong>Availability: </strong>DGP-LA's source code and reproducibility materials are available at https://github.com/bieyl/DGP-LA and archived on Zenodo at https://doi.org/10.5281/zenodo.22155398.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: From atoms to fragments: a coarse representation for efficient and functional protein design.","authors":"","doi":"10.1093/bioinformatics/btag734","DOIUrl":"10.1093/bioinformatics/btag734","url":null,"abstract":"","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":"42 10","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13651502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAIDR-Desi: interactive software for feature analysis and design of intrinsically disordered regions.","authors":"Tian Hao Huang, Robert Strome, Julie D Forman-Kay, Alan M Moses","doi":"10.1093/bioinformatics/btag715","DOIUrl":"10.1093/bioinformatics/btag715","url":null,"abstract":"<p><strong>Summary: </strong>FAIDR-Desi is a non-technical user interface for design of intrinsically disordered regions (IDRs) based on greedy optimization of sequence features (simple sequence statistics correlated with IDR function, including amino acid composition, residue patterning and short linear interaction motifs), using more features (127) than other IDR design approaches. FAIDR-Desi can be used to design synthetic IDR mimics to establish feature sufficiency for function, or to knock out features to show necessity for function, exemplified with experiments on the yeast Cox15 mitochondrial targeting signal.</p><p><strong>Availability and implementation: </strong>Software is freely available on the web at https://moses-lab.github.io/faidr_desi/ and an archival version of the source code is available on Zenodo (https://doi.org/10.5281/zenodo.22036189).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AcrSeek: metric learning with hybrid negative mining for anti-CRISPR protein detection under extreme class imbalance.","authors":"Chan-Seok Jeong","doi":"10.1093/bioinformatics/btag729","DOIUrl":"10.1093/bioinformatics/btag729","url":null,"abstract":"<p><strong>Motivation: </strong>Anti-CRISPR (Acr) proteins inhibit CRISPR-Cas immunity and are key targets for precise control of CRISPR-based genome editing and phage-host coevolution research. Their experimental identification is costly and low-throughput, so computational predictors are used to prioritise candidates for testing. In this setting validated positives number in the hundreds while phage-derived putative negatives reach tens of thousands, producing extreme class imbalance. Yet existing classification-based predictors train and evaluate on 1:1 balanced datasets and allow high-similarity sequences between training and test, overestimating real-world performance.</p><p><strong>Results: </strong>We present AcrSeek, a metric-learning framework that trains a projection head over a frozen protein language model (PLM) encoder with hybrid negative mining (offline global plus online semi-hard) under a joint triplet-focal objective. Under nested 5-fold cross-validation with leakage controlled by 40% identity clustering, AcrSeek improved AUPRC on the imbalanced (1:383) test set from 0.017 to 0.355 over AcrNET (matched PLM backbone), reaching 0.508 with ESM-2 3B. AcrSeek therefore remains structurally robust under extreme class imbalance and may also serve other protein-function detection problems with scarce positives.</p><p><strong>Availability and implementation: </strong>Source code at https://github.com/jeongchans/acrseek, archived at Zenodo (DOI: 10.5281/zenodo.21946803); data and model checkpoints at Zenodo (DOI: 10.5281/zenodo.20115442).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRISM-GEP: Viewing single-cell expression through the lens of topic modeling.","authors":"Yanir Buznah, Tal Ishon, Uri Shaham","doi":"10.1093/bioinformatics/btag728","DOIUrl":"10.1093/bioinformatics/btag728","url":null,"abstract":"<p><strong>Motivation: </strong>Gene-expression programs (GEPs) are the co-regulated gene modules whose coordinated activity encodes biological processes. Recovering them from single-cell RNA-seq is hard because regulation is many-to-many. A cell can run several programs at once, and a gene can take part in several. Methods that resolve programs along a trajectory break this structure by assigning each gene to a single program.</p><p><strong>Results: </strong>PRISM-GEP discovers overlapping GEPs with Latent Dirichlet Allocation, giving it a prior estimated from gene co-expression in place of the flat default it normally assumes. No reference dataset or pathway database is required. Across 15 human and mouse datasets, PRISM-GEP matches specialized factorizations such as cNMF and scHPF on Gene Ontology Biological Process metrics. Over the 43 rankable dataset-by-metric entries, its mean rank is level with the best, and unlike every specialized method it never ranks last. The same co-expression geometry orders the genes within each program, recovering developmental cascades on par with GeneTrajectory.</p><p><strong>Availability and implementation: </strong>The implementation of our methods, including all code, datasets, and experimental workflows, is available in Python at https://github.com/shaham-lab/PRISM-GEP. The version used in this paper is archived at https://doi.org/10.5281/zenodo.22659056.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149083224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective-conditioned-return-blocks decision transformer: a novel approach for multi-objective de novo drug design.","authors":"Eyal Mazuz, Or Gindes, Lior Rokach, Bracha Shapira","doi":"10.1093/bioinformatics/btag731","DOIUrl":"10.1093/bioinformatics/btag731","url":null,"abstract":"<p><strong>Motivation: </strong>Designing novel molecules that simultaneously satisfy several, often conflicting, properties is a long-standing challenge in drug design. While recent deep learning advances optimize single targets effectively, navigating the tradeoffs required for multiple objectives remains a difficult problem.</p><p><strong>Results: </strong>We introduce the Multi-Objective-Conditioned-Return-Blocks Decision Transformer (M-GoRT), a novel architecture for multi-target de-novo drug design incorporating return-to-go blocks conditioned on goal embeddings. Trained without exploration, reward weighting or online fine-tuning, M-GoRT efficiently optimizes for multiple targets. M-GoRT achieves competitive multi-objective success rates against state-of-the-art baselines while maintaining superior structural diversity and novelty, even in high-dimensional tasks incorporating biological targets (DRD2, JNK3). In these high-dimensional tasks, M-GoRT maintains superior structural diversity and novelty establishing it as a practical tool for multi-property molecular generation.</p><p><strong>Availability and implementation: </strong>M-GoRT and the data used are freely available as an open-source software under the MIT license at GitHub https://github.com/eyalmazuz/MolGen/tree/GCRB-DT.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13674356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MAJEC: unified gene, isoform, and locus-level transposable element quantification from RNA-seq.","authors":"Tian-Yeh Lim, Ari J Firestone","doi":"10.1093/bioinformatics/btag737","DOIUrl":"10.1093/bioinformatics/btag737","url":null,"abstract":"<p><strong>Motivation: </strong>The study of transposable elements (TEs) has become increasingly central to fields such as cancer biology, immunology, and aging. Accurately quantifying disease- or laboratory-mediated perturbations in these elements is critical to support this expanding research, yet current RNA-seq pipelines struggle with the pervasive overlap between TEs and protein-coding genes. Existing tools either aggregate to the subfamily level with no locus resolution (TEtranscripts), or provide locus-level quantification without modeling gene overlap (Telescope), with the latter attributing over 40% of TE signal to the 1.1% of loci that overlap gene exons.</p><p><strong>Results: </strong>We present Momentum Accelerated Junction Enhanced Counting (MAJEC), a unified Expectation-Maximization (EM) framework that jointly quantifies genes, transcript isoforms, and individual TE loci from BAM alignments in a single pass. Splice junction evidence informs transcript-level priors, enabling MAJEC to probabilistically distinguish genic from TE-derived reads. This approach was independently validated against Salmon and RSEM on isoform quantification benchmarks. The joint feature space reduces exon-overlap contamination of locus-level TE estimates from 43% of total signal (Telescope) to 5% (MAJEC), while preserving subfamily-level accuracy (differential expression r = 0.987 versus TEtranscripts). Using paired biological vignettes, we demonstrate that MAJEC correctly resolves both the false TE reactivation artifacts endemic to TE-only models, and the false gene upregulation artifacts that occur when heuristic rules misassign genuine intragenic TE transcription. MAJEC simultaneously produces the isoform and locus-level resolution that TEtranscripts lacks, with greater accuracy than Telescope, and runs faster than either.</p><p><strong>Availability and implementation: </strong>MAJEC is available at https://github.com/calico/majec and via PyPI/Bioconda; analysis code and data are deposited at https://doi.org/10.5281/zenodo.19224157.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13676384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}