Journal of Computational Biology最新文献_第3页

Fast Context-Aware Analysis of Genome Annotation Colocalization. 基因组注释定位的快速上下文感知分析

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-10-01 Epub Date: 2024-10-09 DOI: 10.1089/cmb.2024.0667

Askar Gafurov, Tomáš VinaŘ, Paul Medvedev, BroŇa Brejová

{"title":"Fast Context-Aware Analysis of Genome Annotation Colocalization.","authors":"Askar Gafurov, Tomáš VinaŘ, Paul Medvedev, BroŇa Brejová","doi":"10.1089/cmb.2024.0667","DOIUrl":"10.1089/cmb.2024.0667","url":null,"abstract":"An annotation is a set of genomic intervals sharing a particular function or property. Examples include genes or their exons, sequence repeats, regions with a particular epigenetic state, and copy number variants. A common task is to compare two annotations to determine if one is enriched or depleted in the regions covered by the other. We study the problem of assigning statistical significance to such a comparison based on a null model representing random unrelated annotations. To incorporate more background information into such analyses, we propose a new null model based on a Markov chain that differentiates among several genomic contexts. These contexts can capture various confounding factors, such as GC content or assembly gaps. We then develop a new algorithm for estimating p-values by computing the exact expectation and variance of the test statistic and then estimating the p-value using a normal approximation. Compared to the previous algorithm by Gafurov et al., the new algorithm provides three advances: (1) the running time is improved from quadratic to linear or quasi-linear, (2) the algorithm can handle two different test statistics, and (3) the algorithm can handle both simple and context-dependent Markov chain null models. We demonstrate the efficiency and accuracy of our algorithm on synthetic and real data sets, including the recent human telomere-to-telomere assembly. In particular, our algorithm computed p-values for 450 pairs of human genome annotations using 24 threads in under three hours. Moreover, the use of genomic contexts to correct for GC bias resulted in the reversal of some previously published findings.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"946-964"},"PeriodicalIF":1.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390933","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}

引用次数: 0

Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart. 通过重新开始的受限随机游走，推算元基因组 Hi-C 联系促进了整合式 Contig 分选。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-10-01 Epub Date: 2024-09-09 DOI: 10.1089/cmb.2024.0663

Yuxuan Du, Wenxuan Zuo, Fengzhu Sun

{"title":"Imputing Metagenomic Hi-C Contacts Facilitates the Integrative Contig Binning Through Constrained Random Walk with Restart.","authors":"Yuxuan Du, Wenxuan Zuo, Fengzhu Sun","doi":"10.1089/cmb.2024.0663","DOIUrl":"10.1089/cmb.2024.0663","url":null,"abstract":"Metagenomic Hi-C (metaHi-C) has shown remarkable potential for retrieving high-quality metagenome-assembled genomes from complex microbial communities. Nevertheless, existing metaHi-C-based contig binning methods solely rely on Hi-C interactions between contigs, disregarding crucial biological information such as the presence of single-copy marker genes. To overcome this limitation, we introduce ImputeCC, an integrative contig binning tool optimized for metaHi-C datasets. ImputeCC integrates both Hi-C interactions and the discriminative power of single-copy marker genes to group marker-gene-containing contigs into preliminary bins. It also introduces a novel constrained random walk with restart algorithm to enhance Hi-C connectivity among contigs. Comprehensive assessments using both mock and real metaHi-C datasets from diverse environments demonstrate that ImputeCC consistently outperforms other Hi-C-based contig binning tools. A genus-level analysis of the sheep gut microbiota reconstructed by ImputeCC underlines its capability to recover key species from dominant genera and identify previously unknown genera.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1008-1021"},"PeriodicalIF":1.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154267","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}

引用次数: 0

Positivity and Boundedness Preserving Numerical Scheme for a Stochastic Multigroup Susceptible-Infected-Recovering Epidemic Model with Age Structure. 具有年龄结构的随机多群体易感-感染-恢复流行病模型的正向性和有界性保留数值方案。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-09-27 DOI: 10.1089/cmb.2023.0443

Han Ma, Yanyan Du, Zong Wang, Qimin Zhang

{"title":"Positivity and Boundedness Preserving Numerical Scheme for a Stochastic Multigroup Susceptible-Infected-Recovering Epidemic Model with Age Structure.","authors":"Han Ma, Yanyan Du, Zong Wang, Qimin Zhang","doi":"10.1089/cmb.2023.0443","DOIUrl":"https://doi.org/10.1089/cmb.2023.0443","url":null,"abstract":"Since the stochastic age-structured multigroup susceptible-infected-recovering (SIR) epidemic model is nonlinear, the solution of this model is hard to be explicitly represented. It is necessary to construct effective numerical methods so as to predict the number of infections. In addition, the stochastic age-structured multigroup SIR model has features of positivity and boundedness of the solution. Therefore, in this article, in order to ensure that the numerical and analytical solutions must have the same properties, by modifying the classical Euler-Maruyama (EM) scheme, we generate a positivity and boundedness preserving EM (PBPEM) method on temporal space for stochastic age-structured multigroup SIR model, which is proved to have a strong convergence to the true solution over finite time intervals. Moreover, by combining the standard finite element method and the PBPEM method, we propose a full-discrete scheme to show the numerical solutions, as well as analyze the error estimations. Finally, the full-discrete scheme is applied to a general stochastic two-group SIR model and the Chlamydia epidemic model, which shows the superiority of the numerical method.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142347649","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}

引用次数: 0

Bifurcations and Homoclinic Orbits of a Model Consisting of Vegetation-Prey-Predator Populations. 由植被-猎物-捕食者种群组成的模型的分岔和同轴轨道

IF 1.7 4区生物学

Journal of Computational Biology Pub Date : 2024-09-12 DOI: 10.1089/cmb.2024.0485

Maryam Jafari Khanghahi,Reza Khoshsiar Ghaziani

{"title":"Bifurcations and Homoclinic Orbits of a Model Consisting of Vegetation-Prey-Predator Populations.","authors":"Maryam Jafari Khanghahi,Reza Khoshsiar Ghaziani","doi":"10.1089/cmb.2024.0485","DOIUrl":"https://doi.org/10.1089/cmb.2024.0485","url":null,"abstract":"This study provides a comprehensive analysis of the dynamics of a three-level vertical food chain model, specifically focusing on the interactions between vegetation, herbivores, and predators in a Snowshoe hare-Canadian lynx system. By simplifying the model through dimensional analysis, we determine conditions for equilibrium existence and identify various types of bifurcations, including Saddle-Node and Hopf bifurcations. Additionally, the study explores codimension-two bifurcations such as Bogdanov-Takens (BT) and zero-Hopf bifurcations. Coefficient formulas of normal forms are derived through the use of center manifold reduction and normal form theory. The study also presents an approximation of homoclinic orbits near a BT bifurcation of the system by computing explicit asymptotics based on regular perturbation methods. Utilizing the MATLAB package MATCONT, a family of limit cycles and their associated bifurcations are computed, including limit point cycles, period-doubling bifurcations, cusp points of cycles, fold-flip bifurcations, and various resonance bifurcations (R1, R2, R3, and R4). The biological implications of the findings are discussed in detail, highlighting how the identified bifurcations and dynamics can impact the population dynamics of vegetation, herbivores, and predators in real-world ecosystems. Numerical experiments validate the theoretical results and provide further support for the conclusions.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"71 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190465","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}

引用次数: 0

A Cancer Subpopulation Competition Model Reveals Optimal Levels of Immune Response that Minimize Tumor Size. 癌症亚群竞争模型揭示了使肿瘤体积最小化的最佳免疫反应水平

IF 1.7 4区生物学

Journal of Computational Biology Pub Date : 2024-09-10 DOI: 10.1089/cmb.2024.0618

Wimonnat Sukpol,Teeraphan Laomettachit,Anuwat Tangthanawatsakul

{"title":"A Cancer Subpopulation Competition Model Reveals Optimal Levels of Immune Response that Minimize Tumor Size.","authors":"Wimonnat Sukpol,Teeraphan Laomettachit,Anuwat Tangthanawatsakul","doi":"10.1089/cmb.2024.0618","DOIUrl":"https://doi.org/10.1089/cmb.2024.0618","url":null,"abstract":"Breast cancer is a complex disease with significant phenotypic heterogeneity of cells, even within a single breast tumor. Emerging evidence underscores the significance of intratumoral competition, which can serve as a key contributor to cancer drug resistance, imparting substantial clinical implications. Understanding the competitive dynamics is paramount as it can significantly influence disease progression and treatment outcomes. In the present work, a mathematical model was developed using a system of differential equations to describe the dynamic interactions between two cancer subtypes (each further classified into cancer stem cells and tumor cells) and innate immune cells. The purpose of the model is to comprehensively understand the competitive interactions between the heterogeneous subpopulations. The equilibrium points and stability analysis for each equilibrium point were established. Model simulations showed that the competition between two cancer subtypes directly affects the number of both species. When competition between two cancer subtypes is strong, increasing the immune response rate specific to the more competitive species effectively reduces the tumor size. However, if the competition is relatively weak, an optimal immune response rate is required to minimize the total number of tumor cells. Rates below the optimal level fail to reduce the population of the stronger species, whereas rates above the optimal level can lead to the recurrence of the weaker species. Overall, this model provides insights into breast cancer dynamics and guides the development of effective treatment strategies.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"74 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190466","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}

引用次数: 0

PDFll: Predictors of Disorder and Function of Proteins from the Language of Life. PDFll：从生命语言中预测蛋白质的紊乱和功能

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-09-09 DOI: 10.1089/cmb.2024.0506

Wanyi Yang, Qingsong Du, Xunyu Zhou, Chuanfang Wu, Jinku Bao

引用次数: 0

Rosalind Franklin Society Proudly Announces the 2023 Award Recipient for Journal of Computational Biology. 罗莎琳德-富兰克林学会自豪地宣布《计算生物学杂志》2023 年获奖者。

IF 1.7 4区生物学

Journal of Computational Biology Pub Date : 2024-09-01 DOI: 10.1089/cmb.2024.15655.rfs2023

Teresa M Przytycka

引用次数: 0

Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention. 基于 ProtBert-BiGRU-Attention 的蛋白质-蛋白质相互作用预测模型。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-09-01 Epub Date: 2024-07-29 DOI: 10.1089/cmb.2023.0297

Qian Gao, Chi Zhang, Ming Li, Tianfei Yu

{"title":"Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention.","authors":"Qian Gao, Chi Zhang, Ming Li, Tianfei Yu","doi":"10.1089/cmb.2023.0297","DOIUrl":"10.1089/cmb.2023.0297","url":null,"abstract":"The physiological activities within cells are mainly regulated through protein-protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"797-814"},"PeriodicalIF":1.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788234","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}

引用次数: 0

Unveiling Commonalities and Differences in Genetic Regulations via Two-Way Fusion. 通过双向融合揭示基因调控的共性与差异

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-09-01 Epub Date: 2024-08-12 DOI: 10.1089/cmb.2023.0437

Biao Mei, Yu Jiang, Yifan Sun

{"title":"Unveiling Commonalities and Differences in Genetic Regulations via Two-Way Fusion.","authors":"Biao Mei, Yu Jiang, Yifan Sun","doi":"10.1089/cmb.2023.0437","DOIUrl":"10.1089/cmb.2023.0437","url":null,"abstract":"Understanding the genetic regulation, for example, gene expressions (GEs) by copy number variations and methylations, is crucial to uncover the development and progression of complex diseases. Advancing from early studies that are mostly focused on homogeneous groups of patients, some recent studies have shifted their focus toward different patient groups, explored their commonalities and differences, and led to insightful findings. However, the analysis can be very challenging with one GE possibly regulated by multiple regulators and one regulator potentially regulating the expressions of multiple genes, leading to two distinct types of commonalities/differences in the patterns of genetic regulation. In addition, the high dimensionality of both sides of regulation poses challenges to computation. In this study, we develop a two-way fusion integrative analysis approach, which innovatively applies two fusion penalties to simultaneously identify commonalities/differences in the regulated pattern of GEs and regulating pattern of regulators, and adopt a Huber loss function to accommodate the possible data contamination. Moreover, a simple yet efficient iterative optimization algorithm is developed, which does not need to introduce any auxiliary variables and extra tuning parameters and is guaranteed to converge to a globally optimal solution. The advantages of the proposed approach are demonstrated in extensive simulations. The analysis of The Cancer Genome Atlas data on melanoma and lung cancer leads to interesting findings and satisfactory prediction performance.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"834-870"},"PeriodicalIF":1.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971226","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}

引用次数: 0

A Rigorous Framework to Classify the Postduplication Fate of Paralogous Genes. 对同源基因复制后命运进行分类的严格框架

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-09-01 Epub Date: 2024-08-01 DOI: 10.1089/cmb.2023.0331

Reza Kalhor, Guillaume Beslon, Manuel Lafond, Celine Scornavacca

引用次数: 0