IEEE/ACM Transactions on Computational Biology and Bioinformatics最新文献

AirLift: A Fast and Comprehensive Technique for Remapping Alignments Between Reference Genomes. AirLift：快速、全面的参考基因组间重配技术。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2026-03-01 DOI: 10.1109/TCBB.2024.3433378

Jeremie S Kim, Can Firtina, Meryem Banu Cavlak, Damla Senol Cali, Nastaran Hajinazar, Mohammed Alser, Can Alkan, Onur Mutlu

引用次数: 0

DLP: Duplex Link Prediction via Subspace Segmentation for Predicting Drug-MiRNA Associations. DLP：通过子空间分割进行双链路预测，用于预测药物-MiRNA 关联。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2026-03-01 DOI: 10.1109/TCBB.2024.3402215

Kai Zheng, Guihua Duan, Qichang Zhao, Mengyun Yang, Xiao Liang, Yiwei Liu, Jianxin Wang

{"title":"DLP: Duplex Link Prediction via Subspace Segmentation for Predicting Drug-MiRNA Associations.","authors":"Kai Zheng, Guihua Duan, Qichang Zhao, Mengyun Yang, Xiao Liang, Yiwei Liu, Jianxin Wang","doi":"10.1109/TCBB.2024.3402215","DOIUrl":"10.1109/TCBB.2024.3402215","url":null,"abstract":"The arduous and costly journey of drug discovery is increasingly intersecting with computational approaches, which promise to accelerate the analysis of bioassays and biomedical literature. The critical role of microRNAs (miRNAs) in disease progression has been underscored in recent studies, elevating them as potential therapeutic targets. This emphasizes the need for the development of sophisticated computational models that can effectively identify promising drug targets such as miRNAs. Herein, we present a novel method, termed Duplex Link Prediction (DLP), rooted in subspace segmentation, to pinpoint potential miRNA targets. Our approach initiates with the application of the Network Enhancement (NE) algorithm to refine the similarity metric between miRNAs. Thereafter, we construct two matrices by pre-loading the association matrix from both the drug and miRNA perspectives, employing the K Nearest Neighbors (KNN) technique. The DLSR algorithm is then applied to predict potential associations. The final predicted association scores are ascertained through the weighted mean of the two matrices. Our empirical findings suggest that the DLP algorithm outperforms current methodologies in the realm of identifying potential miRNA drug targets. Case study validations further reinforce the real-world applicability and effectiveness of our proposed method.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"560-568"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141070851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

SMCC: A Novel Clustering Method for Single- and Multi-Omics Data Based on Co-Regularized Network Fusion. SMCC：基于共规化网络融合的单体和多体征数据新型聚类方法。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2026-03-01 DOI: 10.1109/TCBB.2024.3353335

Sha Tian, Ying Yang, Yushan Qiu, Quan Zou

{"title":"SMCC: A Novel Clustering Method for Single- and Multi-Omics Data Based on Co-Regularized Network Fusion.","authors":"Sha Tian, Ying Yang, Yushan Qiu, Quan Zou","doi":"10.1109/TCBB.2024.3353335","DOIUrl":"10.1109/TCBB.2024.3353335","url":null,"abstract":"Clustering is a common technique for statistical data analysis and is essential for developing precision medicine. Numerous computational methods have been proposed for integrating multi-omics data to identify cancer subtypes. However, most existing clustering models based on network fusion fail to preserve the consistency of the distribution of the data before and after fusion. Motivated by this observation, we would like to measure and minimize the distribution difference between networks, which may not be in the same space, to improve the performance of data fusion. We were therefore motivated to develop a flexible clustering model, based on network fusion, that minimizes the distribution difference between the data before and after fusion by co-regularization; the model can be applied to both single- and multi-omics data. We propose a new network fusion model for single- and multi-omics data clustering for identifying cancer or cell subtypes based on co-regularized network fusion (SMCC). SMCC integrates low-rank subspace representation and entropy to fuse networks. In addition, it measures and minimizes the distribution difference between the similarity networks and the fusion network by co-regularization. The model can both reduce the noise interference in the source data and make the statistical characteristics of the fusion result closer to those of the source data. We evaluated the clustering performance of SMCC across 16 real single- and multi-omics dataset. The experimental results demonstrated that SMCC is superior to 17 state-of-the-art clustering methods. Moreover, it is effective for identifying cancer or cell subtypes, thereby promoting the development of precision medicine.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"611-618"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139432039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

MLRR-ATV: A Robust Manifold Nonnegative Low-Rank Representation With Adaptive Total-Variation Regularization for scRNA-seq Data Clustering. MLRR-ATV：用于 scRNA-seq 数据聚类的具有自适应总变异正则化功能的稳健歧面非负低方根表示。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2026-03-01 DOI: 10.1109/TCBB.2024.3432740

Gao-Fei Wang, Juan Wang, Shasha Yuan, Chun-Hou Zheng, Jin-Xing Liu

{"title":"MLRR-ATV: A Robust Manifold Nonnegative Low-Rank Representation With Adaptive Total-Variation Regularization for scRNA-seq Data Clustering.","authors":"Gao-Fei Wang, Juan Wang, Shasha Yuan, Chun-Hou Zheng, Jin-Xing Liu","doi":"10.1109/TCBB.2024.3432740","DOIUrl":"10.1109/TCBB.2024.3432740","url":null,"abstract":"Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise. At the same time, it also has the characteristics of high-dimensional and sparse. Clustering is a common method of analyzing scRNA-seq data. This paper proposes a novel single-cell clustering method called Robust Manifold Nonnegative Low-Rank Representation with Adaptive Total-Variation Regularization (MLRR-ATV). The Adaptive Total-Variation (ATV) regularization is introduced into Low-Rank Representation (LRR) model to reduce the influence of noise through gradient learning. Then, the linear and nonlinear manifold structures in the data are learned through Euclidean distance and cosine similarity, and more valuable information is retained. Because the model is non-convex, we use the Alternating Direction Method of Multipliers (ADMM) to optimize the model. We tested the performance of the MLRR-ATV model on eight real scRNA-seq datasets and selected nine state-of-the-art methods as comparison methods. The experimental results show that the performance of the MLRR-ATV model is better than the other nine methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"579-588"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141758476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

MG-TCCA: Tensor Canonical Correlation Analysis Across Multiple Groups. MG-TCCA：跨多组的张量典型相关分析。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-07-01 DOI: 10.1109/TCBB.2024.3471930

Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J Saykin, Qi Long, Li Shen

{"title":"MG-TCCA: Tensor Canonical Correlation Analysis Across Multiple Groups.","authors":"Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J Saykin, Qi Long, Li Shen","doi":"10.1109/TCBB.2024.3471930","DOIUrl":"10.1109/TCBB.2024.3471930","url":null,"abstract":"Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA and Sparse TCCA (STCCA) in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"1299-1310"},"PeriodicalIF":3.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NeoMS: Mass Spectrometry-Based Method for Uncovering Mutated MHC-I Neoantigens. NeoMS：基于质谱的发现变异 MHC-I 新抗原的方法。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3447746

Shaokai Wang, Ming Zhu, Bin Ma

{"title":"NeoMS: Mass Spectrometry-Based Method for Uncovering Mutated MHC-I Neoantigens.","authors":"Shaokai Wang, Ming Zhu, Bin Ma","doi":"10.1109/TCBB.2024.3447746","DOIUrl":"10.1109/TCBB.2024.3447746","url":null,"abstract":"Major Histocompatibility Complex (MHC) molecules play a critical role in the immune system by presenting peptides on the cell surface for recognition by T-cells. Tumor cells often produce MHC peptides with amino acid mutations, known as neoantigens, which evade T-cell recognition, leading to rapid tumor growth. In immunotherapies such as TCR-T and CAR-T, identifying these mutated MHC peptide sequences is crucial. Current mass spectrometry-based peptide identification methods primarily rely on database searching, which fails to detect mutated peptides not present in human databases. In this paper, we propose a novel workflow called NeoMS, designed to efficiently identify both non-mutated and mutated MHC-I peptides from mass spectrometry data. NeoMS utilizes a tagging algorithm to generate an expanded sequence database that includes potential mutated proteins for each sample. Furthermore, it employs a machine learning-based scoring function for each peptide-spectrum match (PSM) to maximize search sensitivity. Finally, a rigorous target-decoy approach is implemented to control the false discovery rates (FDR) of the peptides with and without mutations separately. Experimental results for regular peptides demonstrate that NeoMS outperforms four benchmark methods. For mutated peptides, NeoMS successfully identifies hundreds of high-quality mutated peptides in a melanoma-associated sample, with their validity confirmed by further studies.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"444-454"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AnglesRefine: Refinement of 3D Protein Structures Using Transformer Based on Torsion Angles. AnglesRefine：利用基于扭转角的变换器完善三维蛋白质结构

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3422288

Lei Zhang, Junyong Zhu, Sheng Wang, Jie Hou, Dong Si, Renzhi Cao

{"title":"AnglesRefine: Refinement of 3D Protein Structures Using Transformer Based on Torsion Angles.","authors":"Lei Zhang, Junyong Zhu, Sheng Wang, Jie Hou, Dong Si, Renzhi Cao","doi":"10.1109/TCBB.2024.3422288","DOIUrl":"10.1109/TCBB.2024.3422288","url":null,"abstract":"The goal of protein structure refinement is to enhance the precision of predicted protein models, particularly at the residue level of the local structure. Existing refinement approaches primarily rely on physics, whereas molecular simulation methods are resource-intensive and time-consuming. In this study, we employ deep learning methods to extract structural constraints from protein structure residues to assist in protein structure refinement. We introduce a novel method, AnglesRefine, which focuses on a protein's secondary structure and employs transformer to refine various protein structure angles (psi, phi, omega, CA_C_N_angle, C_N_CA_angle, N_CA_C_angle), ultimately generating a superior protein model based on the refined angles. We evaluate our approach against other cutting-edge methods using the CASP11-14 and CASP15 datasets. Experimental outcomes indicate that our method generally surpasses other techniques on the CASP11-14 test dataset, while performing comparably or marginally better on the CASP15 test dataset. Our method consistently demonstrates the least likelihood of model quality degradation, e.g., the degradation percentage of our method is less than 10%, while other methods are about 50%. Furthermore, as our approach eliminates the need for conformational search and sampling, it significantly reduces computational time compared to existing refinement methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"397-408"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141497925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

GrapHiC: An Integrative Graph Based Approach for Imputing Missing Hi-C Reads. GrapHiC：一种基于图的综合方法，用于估算缺失的 Hi-C 读数。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3477909

Ghulam Murtaza, Justin Wagner, Justin M Zook, Ritambhara Singh

{"title":"GrapHiC: An Integrative Graph Based Approach for Imputing Missing Hi-C Reads.","authors":"Ghulam Murtaza, Justin Wagner, Justin M Zook, Ritambhara Singh","doi":"10.1109/TCBB.2024.3477909","DOIUrl":"10.1109/TCBB.2024.3477909","url":null,"abstract":"Hi-C experiments allow researchers to study and understand the 3D genome organization and its regulatory function. Unfortunately, sequencing costs and technical constraints severely restrict access to high-quality Hi-C data for many cell types. Existing frameworks rely on a sparse Hi-C dataset or cheaper-to-acquire ChIP-seq data to predict Hi-C contact maps with high read coverage. However, these methods fail to generalize to sparse or cross-cell-type inputs because they do not account for the contributions of epigenomic features or the impact of the structural neighborhood in predicting Hi-C reads. We propose GrapHiC, which combines Hi-C and ChIP-seq in a graph representation, allowing more accurate embedding of structural and epigenomic features. Each node represents a binned genomic region, and we assign edge weights using the observed Hi-C reads. Additionally, we embed ChIP-seq and relative positional information as node attributes, allowing our representation to capture structural neighborhoods and the contributions of proteins and their modifications for predicting Hi-C reads. We show that GrapHiC generalizes better than the current state-of-the-art on cross-cell-type settings and sparse Hi-C inputs. Moreover, we can utilize our framework to impute Hi-C reads even when no Hi-C contact map is available, thus making high-quality Hi-C data accessible for many cell types.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"409-419"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma. 开发并验证用于识别食管鳞状细胞癌免疫相关靶点的竞争性内源性 circRNA/miRNA/mRNA 网络综合分析方法

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3443854

Chu-Ting Yu, Bo Tian, Qian-Qian Meng, Zhe-Ran Chen, Ya-Nan Pang, Xun Zhang, Yan Bian, Si-Wei Zhou, Mei-Juan Hao, Ye Gao, Lei Xin, Han Lin, Wei Wang, Luo-Wei Wang

{"title":"Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma.","authors":"Chu-Ting Yu, Bo Tian, Qian-Qian Meng, Zhe-Ran Chen, Ya-Nan Pang, Xun Zhang, Yan Bian, Si-Wei Zhou, Mei-Juan Hao, Ye Gao, Lei Xin, Han Lin, Wei Wang, Luo-Wei Wang","doi":"10.1109/TCBB.2024.3443854","DOIUrl":"10.1109/TCBB.2024.3443854","url":null,"abstract":"Immunotherapy for esophageal squamous cell carcinoma (ESCC) exhibits notable variability in efficacy. Concurrently, recent research emphasizes circRNAs' impact on the ESCC tumor microenvironment. To further explore the relationship, we leveraged circRNA, microRNA, and mRNA sequence datasets to construct a comprehensive immune-related circRNA-microRNA-mRNA network, revealing competing endogenous RNA (ceRNA) roles in ESCC. The network comprises 16 circular RNAs, 13 microRNAs, and 1,560 mRNAs. Weighted gene co-expression analysis identified immune-related modules, notably cancer-associated fibroblast (CAF) and myeloid-derived suppressor cell modules, correlating significantly with immune and stemness scores. Among them, the CAF module plays a crucial role in extracellular matrix function and effectively discriminates ESCC patients. Four hub collagen family genes within CAF correlated robustly with CAF, macrophage infiltration, and T-cell exclusion. In-house sequencing and RT-qPCR validated their elevated expression. We also identified CAF module-targeting drugs as potential ESCC treatments. In summary, we established an immune-related circRNA-miRNA-mRNA network that not only illuminates ceRNA functionality but also highlights circRNAs' involvement in the CAF through collagen gene targeting. These findings hold promise to predict ESCC immune landscapes and therapy responses, ultimately aiding in more personalized and effective clinical decision-making.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"481-492"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142106989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Partition Based Algorithms for Rearrangement Distances With Flexible Intergenic Regions. 基于分区的灵活基因间重排距离算法

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3467033

Gabriel Siqueira, Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Geraldine Jean, Guillaume Fertin, Zanoni Dias

{"title":"Partition Based Algorithms for Rearrangement Distances With Flexible Intergenic Regions.","authors":"Gabriel Siqueira, Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Geraldine Jean, Guillaume Fertin, Zanoni Dias","doi":"10.1109/TCBB.2024.3467033","DOIUrl":"10.1109/TCBB.2024.3467033","url":null,"abstract":"Genome Rearrangement distance problems are used in Computational Biology to estimate the evolutionary distance between genomes. These problems consist of minimizing the number of rearrangement events necessary to transform one genome into another. Two commonly used rearrangement events are reversal and transposition. The first studied problems ignored nucleotides outside genes (called intergenic regions), or assumed that genomes have a single copy of each gene. Recent works made advancements in more general problems considering the number of nucleotides in intergenic regions, and replicated genes. Nevertheless, genomes tend to have wildly different quantities of nucleotides on their intergenic regions, which poses a problem when comparing these regions exactly. To overcome this limitation, our work considers some flexibility when matching intergenic regions that do not have the same number of nucleotides. We propose new problems seeking the minimum number of reversals, or reversals and transpositions, necessary to transform one genome into another, while considering flexible intergenic region information. We show approximations for these problems by exploring their relationship with the Signed Minimum Common Flexible Intergenic String Partition problem. We also present different heuristics for the partition problem, and conduct experimental tests on simulated genomes to assess the performance of our algorithms.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"455-468"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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