Journal of Computational Biology最新文献

{"title":"Preface.","authors":"Mona Singh","doi":"10.1177/15578666251412452","DOIUrl":"https://doi.org/10.1177/15578666251412452","url":null,"abstract":"","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"33 1","pages":"1"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating Transitions of <i>k</i>-mer-Based Objects Across <i>k</i>-mer Sizes.","authors":"Adam Park, David Koslicki","doi":"10.1177/15578666251401600","DOIUrl":"10.1177/15578666251401600","url":null,"abstract":"<p><p>The widespread adoption of <i>k</i>-mers in computational biology has enabled efficient methods for utilizing genomic sequences in a variety of biological tasks. However, understanding the influence of <i>k</i>-mer sizes within these methods remains a persistent challenge. Slicing sequences into a fixed size lacks grounding in biological insight, and complex bioinformatics pipelines obscure the effect of the parameter <i>k</i> due to various noisy factors. The choice of <i>k</i>-mer size is typically arbitrary, with justification omitted in both the literature and method tutorials. Furthermore, the lack of theoretical understanding has caused recent multi-<i>k</i>-mer approaches to face significant computational challenges. Nevertheless, most methods are built on well-defined objects related to <i>k</i>-mers, such as de Bruijn graphs, Jaccard similarity, Bray-Curtis dissimilarity, and <i>k</i>-mer spectra. The role of <i>k</i>-mer sizes within these objects is more intuitive and can be described by numerous quantities and metrics. Therefore, exploring these objects across <i>k</i>-mer sizes opens opportunities for robust analyses and new applications. However, the evolution of <i>k</i>-mer objects with respect to <i>k</i>-mer sizes is surprisingly elusive. We introduce a novel substring index, the Pro<i>k</i>rustean graph, that elucidates the transformation of <i>k</i>-mer sets across <i>k</i>-mer sizes. Our framework built upon this index rapidly computes <i>k</i>-mer-based quantities for all <i>k</i>-mer sizes, with computational complexity independent of the size range and dependent only on maximal repeats. For example, counting unitigs (maximal simple paths) in de Bruijn graphs for <i>k</i> = 1, …, 100 is achieved in seconds using our index on a gigabase-scale dataset. We present a variety of algorithms for applications relevant to pangenomics and metagenomics. The Pro<i>k</i>rustean graph is directly derived from the affix tree and can be constructed space-efficiently from the Burrows-Wheeler transform. This derivation grounded in modern substring indexes that are all theoretically based on longest common prefixes reveals that such extension-based substring representations inherently struggle to explore <i>k</i>-mer objects across different sizes, which motivated our data structure. Our implementation is available at: https://github.com/KoslickiLab/prokrustean.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"107-133"},"PeriodicalIF":1.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13071888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145834045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"StarCDP: Dynamic Programming Algorithms for Fast and Accurate Cell Lineage Tree Reconstruction from CRISPR-Based Lineage Tracing Data.","authors":"Junyan Dai, Erin K Molloy","doi":"10.1177/15578666251386082","DOIUrl":"10.1177/15578666251386082","url":null,"abstract":"<p><p>CRISPR-based lineage tracing, coupled with single-cell RNA sequencing, has emerged as a promising approach for studying development and disease progression at the cellular level. Thus, cell lineage tree (CLT) reconstruction has attracted significant attention in recent years, including the introduction of Star Homoplasy Parsimony (SHP) to model the unique properties of CRISPR-induced mutations, along with the Startle family of methods. However, CLT reconstruction continues to be challenged by technological limitations in producing consistent phylogenetic signals across CLTs. To address these issues, we present Star-CDP, a collection of dynamic programming algorithms that enable researchers to seek, count, sample, and build consensus trees from solutions to SHP within a constrained search space, defined by subsets of cells from which a solution must draw its clades. When using our procedure to construct clade constraints, Star-CDP runs in polynomial time, enabling scalability to larger numbers of cells than Startle-ILP (integer linear programming), the leading method for SHP. In simulations, Star-CDP's strict consensus achieved the same or higher accuracy (f1-score) compared to the leading parsimony methods, with the greatest gains in accuracy occurring when the phylogenetic signal was limited due to the high ratio of cells to mutations. On lineage tracing data from a mouse model of lung adenocarcinoma, Star-CDP's strict consensus achieved the lowest SHP score and comparable numbers of metastatic reseedings compared to PAUP*'s strict consensus and Startle-NNI (nearest neighbor interchange), all benchmarked on a standard data processing pipeline (although our study also revealed that the pipeline can impact relative performance for migrations/reseedings). Star-CDP is available on GitHub: https://github.com/molloy-lab/Star-CDP.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"48-66"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145421813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Batch Active Learning for Protein Structure Modeling.","authors":"Zexin Xue, Michael Bailey, Abhinav Gupta, Ruijiang Li, Alejandro Corrochano-Navarro, Sizhen Li, Lorenzo Kogler-Anele, Qui Yu, Heidi Rommelaere, Wander Van Breedam, Norbert Furtmann, Joseph Batchelor, Ziv Bar-Joseph, Sven Jager","doi":"10.1177/15578666251405823","DOIUrl":"10.1177/15578666251405823","url":null,"abstract":"<p><p>Molecular structure prediction is essential for understanding therapeutic functions and accelerating pharmaceutical research. While state-of-the-art deep learning models like AlphaFold demonstrate strong performance on general protein backbone prediction, they struggle with critical regions of VHH antibodies, a novel family of molecules underrepresented in current training datasets. Many academic and industry laboratories can generate high-quality VHH structures for novel sequences, presenting an opportunity to improve model performance through iterative fine-tuning with strategically selected new data. However, experimental structure determination requires weeks to months of effort and significant costs per structure, making exhaustive data collection impractical. Randomly curating subset of full collection yields suboptimal improvements, as many structures provide redundant information while key regions remain unexplored. Strategic data selection can identify which structures, once experimentally determined, will maximally improve prediction accuracy, enabling superior model performance with fewer iterations and lower costs. We propose DEWDROP, an active learning selection method that guides VHH structure curation to maximally improve fine-tuned model performance. DEWDROP leverages Monte Carlo dropout to generate prediction ensembles that inform optimal data selection. While we focus on VHH antibodies, underrepresentation issues affect many molecular domains, making DEWDROP broadly applicable as a model-agnostic method for structural biology applications. To demonstrate this effectiveness, we evaluate our approach through retrospective iterative fine-tuning experiments and batch selection analysis on two distinct structural families: VHH antibodies from SAbDab-nano as our target application and primary benchmark and <i>Mycobacterium leprae</i> proteins from the AlphaFold Protein Database to demonstrate broader applicability across different molecular domains. For all analyses, we use a structured prediction model based on coarse-grain molecular representations that operates independently of multiple sequence alignments called Equifold. We demonstrate that DEWDROP (1) improves model training efficiency through optimized batch selection, outperforming baseline methods and (2) selects structurally informative data with high information content.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"33 1","pages":"184-200"},"PeriodicalIF":1.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theory, Algorithms, and Applications for Identification of Undesignable RNA Secondary Structures and Motifs.","authors":"Tianshuo Zhou, Apoorv Malik, Wei Yu Tang, David H Mathews, Liang Huang","doi":"10.1177/15578666251398551","DOIUrl":"10.1177/15578666251398551","url":null,"abstract":"<p><p>RNA design aims to find a sequence that folds into a designated target structure under a specific RNA folding model, also known as the inverse problem of RNA folding. While numerous RNA design methods have been invented to search for sequences capable of folding into a target structure under the default (Turner) RNA folding model, little attention has been given to the identification of undesignable structures. This work bridges the gap between RNA design and undesignability by introducing a series of theorems and algorithms aimed at identifying both undesignable structures and their causative local structural components, which we define as <i>minimal undesignable motifs</i>. We first present theorems that provide sufficient conditions for recognizing undesignability structures and propose efficient, theorem-guided algorithms to verify whether an RNA structure is undesignable. While such global undesignability sheds light on the limits of RNA design, identifying the specific motifs responsible for undesignability is critical for understanding RNA folding models and advancing design methodologies. To this end, we develop a new theoretical framework for motif undesignability and propose scalable and interpretable algorithms to identify minimal undesignable motifs within a given RNA secondary structure. Our approach establishes motif undesignability by searching for rival motifs, rather than exhaustively enumerating all (partial) sequences that could potentially fold into the motif. Furthermore, we exploit rotational invariance in RNA structures to detect, group, and reuse equivalent motifs and to construct a database of unique minimal undesignable motifs. To achieve that, we propose a loop-pair graph representation for motifs and a recursive graph isomorphism algorithm for motif equivalence. Our algorithms successfully identified 24 unique minimal undesignable motifs among 18 undesignable puzzles from the Eterna100 benchmark. Surprisingly, we also find over 350 unique minimal undesignable motifs and 663 undesignable native structures in the ArchiveII dataset, drawn from a diverse set of RNA families. Last but not least, we demonstrate that our theory and algorithms can handle motifs with external loops-a critical advancement given the substantial impact of external loops on the quantity, diversity, and designability of RNA structure motifs.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"33 1","pages":"134-167"},"PeriodicalIF":1.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mcRigor: A Statistical Software Package for Evaluating and Optimizing Metacell Partitioning in Single-Cell Data Analysis.","authors":"Pan Liu, Jingyi Jessica Li","doi":"10.1177/15578666251383561","DOIUrl":"10.1177/15578666251383561","url":null,"abstract":"<p><p>Metacell partitioning is a common preprocessing step in single-cell data analysis, used to reduce sparsity by aggregating similar cells. However, existing metacell partitioning algorithms may inadvertently group heterogeneous cells, potentially biasing downstream analyses. The resulting metacell partitions can vary substantially with different hyperparameter settings, leaving users uncertain about which result to trust. The mcRigor R package offers a statistical method for evaluating and optimizing metacell partitioning in single-cell data analysis. This article provides instructions for installing and using mcRigor to support more rigorous and interpretable metacell-based workflows.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"43-47"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12872216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Dynamic Data Structure for Haplotype Matching and Compression on Biobank-Scale Data.","authors":"Pramesh Shakya, Ahsan Sanaullah, Degui Zhi, Shaojie Zhang","doi":"10.1177/15578666251385570","DOIUrl":"10.1177/15578666251385570","url":null,"abstract":"<p><p>Advanced genotyping technology has made it feasible for large numbers of individuals to be genotyped resulting in many biobanks across the world. These biobanks are an excellent resource to study haplotype matching at a large scale. Durbin's positional Burrows-Wheeler transform (PBWT) supports efficient haplotype matching and queries given a panel of haplotypes and scales well with large data. It has been widely used for statistical phasing, imputation, and identity-by-descent detection. However, the original PBWT panel does not support updates when haplotypes need to be added or deleted from the panel. Dynamic-PBWT (d-PBWT) solved this problem but is not memory efficient. While the memory constraint problem of the PBWT has been tackled by Syllable-PBWT and <math><mi>μ</mi></math>-PBWT, these are static data structures that do not allow updates. In addition, Syllable-PBWT only supports long-match query and <math><mi>μ</mi></math>-PBWT only supports set-maximal match query, limiting their functionality in the compressed form. In this article, we present Dynamic <math><mi>μ</mi></math>-PBWT (which can also be seen as compressed d-PBWT) that is memory efficient and supports dynamic updates. We run-length compress PBWT to achieve a better compression rate and store the runs in self-balancing trees to enable dynamic updates. We show that the number of updates per insertion or deletion in the tree at each site is constant regardless of the number of haplotypes in the panel and the updates can be made without decompressing the index. Moreover, we use orders of magnitude less memory than d-PBWT. We provide set maximal match and long match query algorithms on Dynamic <math><mi>μ</mi></math>-PBWT. The long match query algorithm can easily be extended back to the original <math><mi>μ</mi></math>-PBWT. We benchmark all algorithms on the UK Biobank and 1000 Genomes Project dataset. Overall, the flexibility and space-efficiency of Dynamic <math><mi>μ</mi></math>-PBWT make it a potential index data structure for biobank-scale genetic data maintenance and analysis.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"67-89"},"PeriodicalIF":1.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13159979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145409143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HyperDiffuseNet: A Deep Hyperbolic Manifold Learning Method for Dimensionality Reduction in Spatial Transcriptomics.","authors":"Jing Qi, Wen Shuai, Lv Yanqi, Yang Mingyu, Shuilin Jin","doi":"10.1177/15578666251377097","DOIUrl":"10.1177/15578666251377097","url":null,"abstract":"<p><p>Spatial transcriptomics (ST) reveals tissue organization but presents analytical challenges due to high dimensionality and complex spatial-hierarchical structures, which are often distorted by Euclidean-based dimensionality reduction methods. Here, we introduce HyperDiffuseNet, a deep geometric learning framework designed for ST data representation. HyperDiffuseNet utilizes a variational autoencoder with a hyperbolic latent space to effectively capture hierarchical relationships. It integrates spatial context by first employing graph convolutional networks on the spatial graph to learn multi-scale dependencies, which inform the computation of a diffusion matrix. This graph-derived diffusion information is then efficiently incorporated into the hyperbolic embeddings via linear mixing in the ambient Minkowski space. The model uses negative binomial reconstruction loss and is optimized with a composite objective function balancing reconstruction fidelity, Kullback-Leibler divergence regularization, attention-weighted spatial regularization, diffusion consistency, and local structure preservation. Empirical evaluations on multiple ST datasets demonstrate that HyperDiffuseNet achieves competitive clustering performance. The hyperbolic embedding approach shows notable improvements in Silhouette coefficient and adjusted rand index metrics across most tested datasets, while maintaining comparable performance in structure preservation.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1101-1120"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145113360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Counting RNA Loop Interaction Networks of Homology Group Rank Zero.","authors":"Zi-Wei Bai, Ricky X F Chen, Michael Fuchs","doi":"10.1177/15578666251370765","DOIUrl":"https://doi.org/10.1177/15578666251370765","url":null,"abstract":"<p><p>An enumerative study of RNA secondary structures according to various characteristics is a topic of key importance in computational biology. RNA secondary structure pairs have also been studied in various contexts. Recently, the homology groups of the simplicial complices induced by pairs of secondary structures have been studied by Bura, He, and Reidys, providing a new way for characterizing these structure pairs. In particular, the homology group <math><mrow><mrow><msub><mrow><mi>H</mi></mrow><mn>2</mn></msub></mrow></mrow></math> corresponding to any pair has been shown to be a free group. In this article, we provide enumerative results, both exactly and asymptotically, for those pairs giving a free group of rank zero. The asymptotic number of these structure pairs of length <i>n</i> is shown to be (0.2774624151…)(4.8105752536…)^<i>n</i><i>n</i>^-3/2. We also prove that the distribution of the number of base pairs in those pairs of secondary structures is asymptotically normal.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"32 12","pages":"1147-1159"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145604350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph Data Augmentation for Graph Convolutional Networks Learning in Robust Mental Disorder Prediction with Limited and Noisy Labels.","authors":"Jiacheng Pan, Yihong Dong, Daogen Jiang, Longyang Wang","doi":"10.1177/15578666251371079","DOIUrl":"10.1177/15578666251371079","url":null,"abstract":"<p><p>Graph neural networks have shown impressive performance in a variety of biomedical application tasks due to their powerful graph representation capabilities. Although GNN has achieved great success, the data noise and data scarcity problems commonly faced in real psychiatric disease prediction scenarios may affect the training and prediction of graph learning models. At present, there is no relevant work to obtain a reasonable solution. Data augmentation, which allows limited data to produce value equivalent to more data without substantially increasing the data, is considered a practical approach to addressing the problem of noisy data and data scarcity. In this work, we propose a method based on graph data augmentation for solving the problem of noisy data and data scarcity in mental illness prediction. To mitigate the negative effects of label noise, we use edge predictors to optimize the graph topology, enhance links to nodes with high similarity, remove erroneous noisy edges, and enhance the model robustness by adding adversarial perturbations in the feature space. In addition, a confident self-checking mechanism allows accurate pseudolabeling to be obtained, providing more supervision for the model training phase and further reducing the effect of label noise. Extensive experiments on two multimodal real mental illness datasets show that the proposed approach has better performance. Sufficient ablation experimental studies were conducted to assess the effectiveness of each component. The experimental results validate the effectiveness and scalability of our framework for population-based disease prediction, even under challenging conditions of data noise and sparsity. The implementation code is publicly available at: https://github.com/jiachengpan98/GDA-GCN.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1171-1189"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144955948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}