Journal of Computational Biology最新文献

{"title":"Reflections on the Presence of Human Cancer Genes in Primate Genomes.","authors":"Xiao Liang, Lenwood S Heath","doi":"10.1177/15578666251388496","DOIUrl":"10.1177/15578666251388496","url":null,"abstract":"<p><p>Cancer genes, including oncogenes and tumor suppressor genes, are crucial subjects of study in human health. With advancements in nonhuman primate genome research, recent investigations have started to explore the relationship between human cancer genes and various primate species, implying possible links between cancer genes and nonhuman primate species. However, these findings are commonly limited to only a few primate species. Here, we investigate the distribution of 726 human cancer genes across 31 nonhuman primate species and 4 nonprimate species, comparing them with a randomly selected set of 339 other human genes. Overall, a higher ratio of cancer genes was found to have emerged before primate speciation compared with random human genes; however, this trend did not necessarily continue after primate speciation. Furthermore, our investigation into primate clades suggests that the absence of certain cancer genes in a clade may reflect recent emergence in other clades in which they exist, rather than elimination within the clades in which they are absent.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"219-235"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145389885","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":"A Probabilistic Algorithm for Gene-Species Reconciliation with Segmental Duplications.","authors":"Yao-Ban Chan, Celine Scornavacca, Michael Charleston","doi":"10.1177/15578666251392595","DOIUrl":"10.1177/15578666251392595","url":null,"abstract":"<p><p>Reconciliations are a mathematical tool to compare the phylogenetic trees of genes to the species that contain them, accounting for events such as gene duplication and loss. Traditional reconciliation methods have predominantly relied on parsimony to infer gene-only evolutionary events and usually make the hypothesis that genes evolve independently. Recently, more advanced models have been developed that account for complex gene interactions stemming from phenomena such as segmental duplications, where multiple genes undergo simultaneous duplication. In this article, we study the NP-hard problem of reconciling gene trees to a species tree with segmental duplications, without the aid of synteny information. We address this problem by proposing a novel probabilistic approach, imposing a Boltzmann distribution over the space of reconciliations. This allows for a Gibbs sampling-like Markov chain Monte Carlo algorithm that uses simulated annealing to effectively find or approximate the most parsimonious reconciliation, as demonstrated through rigorous simulations and re-analysis of empirical datasets. Our findings present a promising new framework for addressing NP-hard reconciliation challenges in phylogenetics, enhancing our understanding of gene evolution and its relationship with species evolution.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"203-218"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145809960","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":"Bayesian Validation of Dynamic Systems for Biological Networks.","authors":"Donghui Son, Jaejik Kim","doi":"10.1177/15578666251382251","DOIUrl":"10.1177/15578666251382251","url":null,"abstract":"<p><p>Dynamic systems encompass a broad class of mathematical models used to describe the behavior of complex networks or systems over time. One of the most common approaches to modeling such dynamics is through a set of ordinary differential equations (ODEs), typically constructed based on hypotheses, known interactions, or observed trajectories. However, ODEs are deterministic and inflexible, while biological data are typically noisy. Thus, the model fit might not account for all possible data variations, and there might be a discrepancy between the actual biological process and the assumed model. This discrepancy could lead to inaccuracies in the prediction and interpretation of the biological networks. Therefore, it is required to validate ODE models in terms of observed data. Given that biological networks typically involve multiple sources of errors and uncertainties, the validation process should account for these factors. The Bayesian approaches offer a robust framework for quantifying errors and uncertainties. Thus, in this study, we propose a Bayesian validation method for ODE models that addresses model inadequacy, presented as bias. Since the proposed method estimates bias as a function of time, it can provide prediction bounds for the entire observed time interval. Consequently, it allows for a direct evaluation of the model's validity across the whole time interval, and it can lead to better prediction by correcting the bias.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"267-279"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225417","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":"Multi-Perspective Natural Vector: A Novel Method for Viral Sequence Feature Extraction.","authors":"Xiang Shi, Jiayi Kang, Nan Sun, Xin Zhao, Stephen S-T Yau","doi":"10.1177/15578666251391211","DOIUrl":"10.1177/15578666251391211","url":null,"abstract":"<p><p>The rapid expansion of biological data in recent decades has highlighted the need for efficient methods in sequence analysis. Traditional pairwise alignment approaches are both time-consuming and memory-intensive. Alignment-free (AF) methods such as natural vector (NV) and k-mer operate on a one-dimensional framework, interpreting DNA primarily as a linear string of nucleotides. To achieve a more comprehensive interpretation of molecular structure, this study incorporates the three-dimensional architectural features of DNA and introduces a novel AF method named Multi-perspective natural vector (MNV). The MNV method maps genome sequences of varying lengths to points within a unified geometric space, facilitating large-size data processing tasks such as variant classification and clustering. Across datasets of different sizes and types, MNV attains a 100% convex hull separation ratio in lower dimensions compared with widely used methods NV and k-mer methods. In neural network classification, MNV achieves better classification accuracy of 99.55% and 98.78% on SARS-CoV-2 and poliovirus datasets respectively, demonstrating its effectiveness in viral genome analysis while maintaining computational efficiency.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"255-266"},"PeriodicalIF":1.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145701154","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":"MCMC-CE: A Novel and Efficient Algorithm for Estimating Small Right-Tail Probabilities of Quadratic Forms with Applications in Genomics.","authors":"Vy Q Ong, Bich N Choi, Devin P Lundy, Yingnan Zhang, Yin Wan, Hongyan Xu, Santu Ghosh, Hui Jiang, Yang Shi","doi":"10.1177/15578666251406305","DOIUrl":"10.1177/15578666251406305","url":null,"abstract":"<p><p>Quadratic forms of multivariate normal variables play a critical role in statistical applications, particularly in genomics and bioinformatics. However, accurately computing small right-tail probabilities (<i>p</i>-values) for large-scale quadratic forms is computationally challenging due to the intractability of their probability distributions, as well as significant numerical constraints and computational burdens. To address these problems, we propose Markov chain Monte Carlo cross-entropy (MCMC-CE), an innovative algorithm that integrates MCMC sampling with the CE method, coupled with leading eigenvalue extraction and Satterthwaite-type approximation techniques. Our approach efficiently estimates small <i>p</i>-values for quadratic forms with their ranks exceeding 10,000. Through extensive simulation studies and real-world applications in genomics, including genome-wide association studies and pathway enrichment analyses, our method demonstrates advantageous numerical accuracy and computational reliability compared with existing approaches such as Davies', Imhof's, Farebrother's, Liu-Tang-Zhang's, and saddlepoint approximation methods. MCMC-CE provides a robust and scalable solution for accurately computing small <i>p</i>-values for quadratic forms, facilitating more precise statistical inference in large-scale genomic studies.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"236-254"},"PeriodicalIF":1.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085961","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":"Pharming: Joint Clonal Tree Reconstruction of Single-Nucleotide Variant and Copy Number Aberration Evolution from Single-Cell DNA Sequencing of Tumors.","authors":"Leah L Weber, Anna Hart, Idoia Ochoa, Mohammed El-Kebir","doi":"10.1177/15578666251374694","DOIUrl":"10.1177/15578666251374694","url":null,"abstract":"<p><p>Cancer arises through an evolutionary process in which somatic mutations, including single-nucleotide variants (SNVs) and copy number aberrations (CNAs), drive the development of a malignant, heterogeneous tumor. Reconstructing this evolutionary history from sequencing data is critical for understanding the order in which mutations are acquired and the dynamic interplay between different types of alterations. Advances in modern whole-genome single-cell sequencing now enable the accurate inference of copy number profiles in individual cells. However, the low sequencing coverage of these low-pass sequencing technologies poses a challenge for reliably inferring the presence or absence of SNVs within tumor cells, limiting the ability to simultaneously study the evolutionary relationships between SNVs and CNAs. In this work, we introduce a novel tumor phylogeny inference method, Pharming, that infers the joint evolutionary history of SNVs and CNAs. Our key insight is to leverage the high accuracy of copy number inference methods and the fact that SNVs co-occur in regions with CNAs in order to enable more precise tumor phylogeny reconstruction for both alteration types. We demonstrate via simulations that Pharming outperforms state-of-the-art tumor phylogeny inference methods. Additionally, we apply Pharming to a triple-negative breast cancer case, achieving high-resolution, joint reconstruction of CNA and SNV evolution, including the de novo detection of a clonal whole-genome duplication event. Thus, Pharming offers the potential for more comprehensive and detailed tumor phylogeny inference for high-throughput, low-coverage single-cell DNA sequencing technologies compared with existing approaches.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"2-18"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145053702","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":"ClusterDE: A Statistical Software Package for Removing Double-Dipping Bias in Post-Clustering Differential Expression Analysis.","authors":"Christy Lee, Dongyuan Song, Siqi Chen, Jingyi Jessica Li","doi":"10.1177/15578666251383562","DOIUrl":"10.1177/15578666251383562","url":null,"abstract":"<p><p>Typical pipelines for single-cell and spatial transcriptomics involve clustering cells or spatial spots, followed by post-clustering differential expression (DE) analysis to identify marker genes for annotating clusters as cell types or spatial domains. However, using the same data for both clustering and DE analysis-a problem known as double-dipping-can lead to spurious detection of DE genes. In particular, over-clustering can produce artificial clusters that are incorrectly interpreted as distinct cell types or spatial domains. To address this issue, the ClusterDE R package implements a statistical method using a synthetic null dataset, which consists of a single homogeneous cell population or spatial domain but is constructed to match the real dataset in terms of gene means, variances, and gene-gene rank correlations. By serving as a parallel negative control, the synthetic null data allow users to identify and remove false-positive DE genes arising from double-dipping. This article introduces the ClusterDE R package and provides practical guidance on installation and usage for more reliable marker gene detection following clustering.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"36-42"},"PeriodicalIF":1.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13309996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251583","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":"Col-BWT: Pangenomic Seed Chaining with Maximal Matches Improves Read Classification.","authors":"Nathaniel K Brown, Vikram S Shivakumar, Ben Langmead","doi":"10.1177/15578666251400784","DOIUrl":"10.1177/15578666251400784","url":null,"abstract":"<p><p>Given a set of exact matches, chaining consists of finding those which are colinear with respect to the reference. However, this requires superlinear time in the number of matches and is not suitable for approaches such as read classification using a pangenome due to the increased multiplicity of matches. While compressed full-text indexes enable efficient read classification against a pangenome or tree-of-life index, past work on compressed index classification captures only fine-grained colinearity of exact matches. This fails to capture whether seeds appear colinearly in the reference, which we denote as \"coarse-grained colinearity,\" as traditionally described by chaining. We present a novel data structure, col-BWT (for colinear Burrows-Wheeler Transform [BWT]), that additionally obtains coarse-grained colinearity (\"chain\") statistics. We start with a collection of strings, avoiding the multiple-alignment step required by graph approaches. We rapidly compute multi-maximal unique matches (multi-MUMs) and identify BWT sub-runs that correspond to these multi-MUMs. From these, we select those that can be \"tunneled,\" in that they satisfy the concept of a tunnel in BWT compression, and mark them with their corresponding multi-MUM identifier. We call this approach the col-BWT due to its ability to recognize colinearity with respect to the reference. This yields an <i>O</i>(<i>r + n/d</i>)-space index for a collection of <i>d</i> sequences having a length-<i>n</i> BWT consisting of <i>r</i> maximal equal-character runs. Using col-BWT, we simultaneously compute both fine-grained statistics and coarse-grained chain statistics in linear time with respect to query length, faster than traditional superlinear chaining approaches. We found that this substantially improves classification accuracy compared with past compressed-indexing approaches and reaches the same level of accuracy as less efficient alignment-based methods.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"168-183"},"PeriodicalIF":1.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13049588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145677746","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":"Conformal Prediction with Knowledge Graphs for Reliable Antimicrobial Resistance Detection with MALDI-TOF Mass Spectra.","authors":"Nina Corvelo Benz, Lucas Miranda, Dexiong Chen, Janko Sattler, Karsten Borgwardt","doi":"10.1177/15578666251396558","DOIUrl":"10.1177/15578666251396558","url":null,"abstract":"<p><p>Bacterial antimicrobial resistance is one of the most pressing global health challenges. Infections with resistant pathogens increase patient morbidity and mortality due to limited treatment options. Rapid and reliable identification of resistance is therefore crucial. However, conventional culture-based diagnostics are slow, typically requiring at least 48 hours from patient sample arrival to result. In contrast, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, routinely used for species identification, can provide data 24 hours earlier. Repurposing this technique for antimicrobial resistance prediction has shown promise, but limited predictive performance and a lack of statistically grounded uncertainty estimates have hindered clinical integration. To address these issues, we propose an antimicrobial resistance detection framework using a knowledge-graph-enhanced conformal predictor. Conformal prediction outputs sets of likely effective antibiotics with statistical guarantees, ensuring that resistance detection meets a predefined error rate. Our approach improves upon standard conformal prediction by integrating domain knowledge through a drug- and species-specific knowledge graph that captures interdependencies between antibiotics, such as inferable resistance patterns between broad- and narrow-spectrum agents, as well as co-resistance patterns within antibiotic classes. This predictor is layered on top of a novel classifier that surpasses state-of-the-art models and overcomes key technical limitations of earlier approaches. We evaluated our framework on two clinically relevant species, <i>Klebsiella pneumoniae</i> and <i>Escherichia coli</i>, using the DRIAMS dataset. Our results demonstrate that our conformal predictor consistently achieved the expected coverage guarantees and that the knowledge-graph enhancement significantly reduced false discovery rates compared to standard conformal approaches. By adding statistically grounded uncertainty estimates and improving predictive performance, our framework strengthens the reliability of early antimicrobial resistance predictions from MALDI-TOF data. This could support the clinical integration of such rapid diagnostics by increasing trust in their results and enabling better-informed early treatment decisions.1.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"19-35"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145677712","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":"Learning Protein Structure Representation with Orientation-Aware Networks.","authors":"Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jian Peng, Jianzhu Ma","doi":"10.1177/15578666251406300","DOIUrl":"10.1177/15578666251406300","url":null,"abstract":"<p><p>The intricate three-dimensional structures of proteins dictate their diverse biological functions. Accurately representing these structures, particularly the fine-grained orientational relationships between amino acids, is crucial for understanding protein mechanisms and practical computational protein analysis. Current methodologies often fall short in capturing these essential geometric details. We introduce Orientation-Aware Graph Neural Networks (OA-GNNs), a novel deep learning framework that explicitly models local and global geometric characteristics, including inner-residue torsion angles and inter-residue orientations. OA-GNNs achieve this by uniquely extending neural network weights from scalars to 3D directed weights and by implementing an equivariant message passing paradigm that ensures SO(3)-equivariance. This approach allows for a richer, more geometrically meaningful processing of protein structural data. Comprehensive experiments demonstrate that OA-GNNs significantly outperform existing methods in sensing orientational features and achieve state-of-the-art performance across diverse computational biology tasks, including residue identification, computational protein design, model quality assessment, and protein function classification. Our findings highlight the power of orientation-aware learning and establish OA-GNNs as a versatile and robust tool for advancing our understanding of protein structure-function relationships and for developing new therapeutic and biotechnological solutions. The code is available at https://github.com/Ced3-han/OAGNN/tree/main.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"90-106"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145863118","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}