Bioinformatics (Oxford, England)最新文献_第10页

gapTrick-structural characterization of protein-protein interactions using AlphaFold. gapTrick -蛋白质相互作用的结构表征使用AlphaFold。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf532

Grzegorz Chojnowski

{"title":"gapTrick-structural characterization of protein-protein interactions using AlphaFold.","authors":"Grzegorz Chojnowski","doi":"10.1093/bioinformatics/btaf532","DOIUrl":"10.1093/bioinformatics/btaf532","url":null,"abstract":"Motivation: The structural characterization of protein-protein interactions is a key step in understanding the functions of living cells. Here, I show that AlphaFold3 often fails to predict protein complexes that are either weak or dependent on the presence of a cofactor that is not included in a prediction.Results: To address this problem, I developed gapTrick, an AlphaFold2-based approach that uses multimeric templates to improve prediction reliability. I demonstrate that gapTrick improves predictions of weak and incomplete complexes based on low-accuracy templates, such as individual protein models that have been rigid-body fitted into cryo-EM reconstructions. I also show that gapTrick identifies residue-residue interactions with high precision. These interaction predictions are a very strong indicator of model correctness. The approach can aid in the interpretation of challenging experimental structures and the computational identification of protein-protein interactions.Availability and implementation: The gapTrick source code is available at https://github.com/gchojnowski/gapTrick and requires only a standard AlphaFold2 installation to run. The repository also provides a Colab notebook that can be used to run gapTrick without installing it on the user's computer.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12479393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UMI-nea: a fast, robust tool for reference-free UMI deduplication and accurate quantification. UMI-nea：一个快速，强大的工具，用于无参考的UMI重复数据删除和准确定量。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf514

Jixin Deng, Jingxiao Zhang, Song Tian, John DiCarlo, Hong Xu, Samuel J Rulli, Jonathan M Shaffer, Vikas Gupta, Toeresin Karakoyun

{"title":"UMI-nea: a fast, robust tool for reference-free UMI deduplication and accurate quantification.","authors":"Jixin Deng, Jingxiao Zhang, Song Tian, John DiCarlo, Hong Xu, Samuel J Rulli, Jonathan M Shaffer, Vikas Gupta, Toeresin Karakoyun","doi":"10.1093/bioinformatics/btaf514","DOIUrl":"10.1093/bioinformatics/btaf514","url":null,"abstract":"Motivation: One of the key applications of Unique Molecular Identifiers (UMIs) in high-throughput sequencing is to correct for PCR amplification bias and removal of PCR duplicates, thereby improving quantification in DNA-seq and RNA-seq applications. Accurately grouping error-bearing UMIs that originate from the same input molecule through a UMI deduplication method is a critical step in this process. However, many existing UMI deduplication tools rely on simple Hamming distance comparisons or suboptimal clustering algorithms, often resulting in erroneous UMI groupings, particularly in error-prone long-read sequencing or ultra-high-depth short-read sequencing.Results: We introduce UMI-nea, a tool that utilizes Levenshtein distance comparisons and a novel clustering approach to optimize multithreading workflows. Compared against three other indel-aware UMI deduplication tools, UMI-nea achieves more accurate UMI groupings with efficient run time. It demonstrates robust performance across diverse sequencing platforms, depths, and UMI lengths. Additionally, UMI-nea incorporates a data-guided adaptive UMI filter, further enhancing quantification accuracy.Availability and implementation: UMI-nea is available on github https://github.com/Qiaseq-research/UMI-nea.git or Zenodo https://doi.org/10.5281/zenodo.16745758. Sequencing data are stored at https://qiagenpublic.blob.core.windows.net/umi-nea-datasets/.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CDACHIE: chromatin domain annotation by integrating chromatin interaction and epigenomic data with contrastive learning. 结合染色质相互作用和表观基因组数据与对比学习的染色质结构域注释。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf464

Asato Yoshinaga, Osamu Maruyama

{"title":"CDACHIE: chromatin domain annotation by integrating chromatin interaction and epigenomic data with contrastive learning.","authors":"Asato Yoshinaga, Osamu Maruyama","doi":"10.1093/bioinformatics/btaf464","DOIUrl":"10.1093/bioinformatics/btaf464","url":null,"abstract":"Motivation: Chromatin domain annotation identifies functional genomic regions, such as active and inactive zones, based on epigenomic features like histone modifications, DNA methylation, and chromatin accessibility. While recent methods have utilized both chromatin interaction data (e.g. Hi-C) and epigenomic data, they often overlook the direct relationship between these data types.Results: In this study, we introduce Chromatin Domain Annotation using Contrastive Learning for Hi-C and Epigenomic Data (CDACHIE), a method for identifying chromatin domains from Hi-C and epigenomic data. Our approach leverages contrastive learning to generate aligned representative vectors for both data types at each genomic bin. The concatenated vectors are then clustered using K-means to classify distinct chromatin domain types. CDACHIE achieves superior performance in Variance Explained, evaluated across gene expression, replication timing, and ChIA-PET data. This highlights its robust ability to integrate semantic associations between Hi-C and epigenomic features within the embedding space.Availability and implementation: The source code is available at GitHub: https://github.com/maruyama-lab-design/CDACHIE. An archival snapshot of the code used in this study is available on Zenodo: https://doi.org/10.5281/zenodo.15751780.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CompareM2 is a genomes-to-report pipeline for comparing microbial genomes. CompareM2是一个基因组到报告的管道，用于比较微生物基因组。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf517

Carl M Kobel, Velma T E Aho, Ove Øyås, Niels Nørskov-Lauritsen, Ben J Woodcroft, Phillip B Pope

引用次数: 0

MultiNMRFit: a software to fit 1D and pseudo-2D NMR spectra. multimrfit：一个软件拟合1D和伪2d核磁共振光谱。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf463

Pierre Millard, Loïc Le Grégam, Svetlana Dubiley, Valeria Gabrielli, Thomas Gosselin-Monplaisir, Guy Lippens, Cyril Charlier

{"title":"MultiNMRFit: a software to fit 1D and pseudo-2D NMR spectra.","authors":"Pierre Millard, Loïc Le Grégam, Svetlana Dubiley, Valeria Gabrielli, Thomas Gosselin-Monplaisir, Guy Lippens, Cyril Charlier","doi":"10.1093/bioinformatics/btaf463","DOIUrl":"10.1093/bioinformatics/btaf463","url":null,"abstract":"Motivation: Nuclear Magnetic Resonance (NMR) is widely used for quantitative analysis of metabolic systems. Accurate extraction of NMR signal parameters-such as chemical shift, intensity, coupling constants, and linewidth-is essential for obtaining information on the structure, concentration, and isotopic composition of metabolites.Results: We present MultiNMRFit, an open-source software designed for high-throughput analysis of 1D NMR spectra, whether acquired individually or as pseudo-2D experiments. MultiNMRFit extracts signal parameters (e.g. intensity, area, chemical shift, and coupling constants) by fitting the experimental spectra using built-in or user-defined signal models that account for multiplicity, providing high flexibility along with robust and reproducible results. The software is accessible both as a Python library and via a graphical user interface, enabling intuitive use by end-users without computational expertise. We demonstrate the robustness and flexibility of MultiNMRFit on 1H, 13C, and 31P NMR datasets collected in metabolomics and isotope labeling studies.Availability and implementation: MultiNMRFit is implemented in Python 3 and was tested on Unix, Windows, and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/NMRTeamTBI/MultiNMRFit/ and https://multinmrfit.readthedocs.io, respectively.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MolPrompt: improving multi-modal molecular pre-training with knowledge prompts. MolPrompt：用知识提示改进多模态分子预训练。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf466

Yang Li, Chang Liu, Xin Gao, Guohua Wang

{"title":"MolPrompt: improving multi-modal molecular pre-training with knowledge prompts.","authors":"Yang Li, Chang Liu, Xin Gao, Guohua Wang","doi":"10.1093/bioinformatics/btaf466","DOIUrl":"10.1093/bioinformatics/btaf466","url":null,"abstract":"Motivation: Molecular pre-training has emerged as a foundational approach in computational drug discovery, enabling the extraction of expressive molecular representations from large-scale unlabeled datasets. However, existing methods largely focus on topological or structural features, often neglecting critical physicochemical attributes embedded in molecular systems.Result: We present MolPrompt, a knowledge-enhanced multimodal pre-training framework that integrates molecular graphs and textual descriptions via contrastive learning. MolPrompt employs a dual-encoder architecture consisting of Graphormer for graph encoding and BERT for textual encoding, and introduces knowledge prompts, semantic embeddings constructed by converting molecular descriptors into natural language, into the graph encoder to guide structure-aware representation learning. Across tasks including molecular property prediction, toxicity estimation, cross-modal retrieval, and anticancer inhibitor identification, MolPrompt consistently surpasses state-of-the-art baselines. These results highlight the value of embedding domain knowledge into structural learning to improve the depth, interpretability, and transferability of molecular representations.Availability and implementation: The source code of MolPrompt is available at: https://github.com/catly/MolPrompt.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model. GE-IA-NAM：基于图像辅助神经加性模型的基因-环境相互作用分析。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf481

Jingmao Li, Yaqing Xu, Shuangge Ma, Kuangnan Fang

{"title":"GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.","authors":"Jingmao Li, Yaqing Xu, Shuangge Ma, Kuangnan Fang","doi":"10.1093/bioinformatics/btaf481","DOIUrl":"10.1093/bioinformatics/btaf481","url":null,"abstract":"Motivation: Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling.Results: We propose the pathological imaging-assisted neural additive model for G-E analysis (GE-IA-NAM). The flexible and interpretable additive network architecture is adopted to account for individualized effects associated with genetic factors, environmental factors, and their interactions. To improve G-E modeling, an assisted-learning strategy is investigated, which adopts a joint analysis to integrate information from pathological images. Simulations and the analysis of lung and skin cancer datasets from The Cancer Genome Atlas demonstrate the competitive performance of the proposed method.Availability and implementation: Python code implementing the proposed method is available at https://github.com/Mr-maoge/NAM-IA-GE. The data that support the findings in this article are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12452269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144982337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mitigation of multi-scale biases in cell-type deconvolution for spatially resolved transcriptomics using HarmoDecon. 使用HarmoDecon缓解空间分解转录组学细胞型反褶积的多尺度偏差。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf451

Zirui Wang, Ke Xu, Yang Liu, Yu Xu, Lu Zhang

{"title":"Mitigation of multi-scale biases in cell-type deconvolution for spatially resolved transcriptomics using HarmoDecon.","authors":"Zirui Wang, Ke Xu, Yang Liu, Yu Xu, Lu Zhang","doi":"10.1093/bioinformatics/btaf451","DOIUrl":"10.1093/bioinformatics/btaf451","url":null,"abstract":"Motivation: The advent of spatially resolved transcriptomics (SRT) has revolutionized our understanding of tissue molecular microenvironments by enabling the study of gene expression in its spatial context. However, many SRT platforms lack single-cell resolution, necessitating cell-type deconvolution methods to estimate cell-type proportions in SRT spots. Despite advancements in existing tools, these methods have not addressed biases occurring at three scales: individual spots, entire tissue samples, and discrepancies between SRT and reference scRNA-seq datasets. These biases result in overbalanced cell-type proportions for each spot, mismatched cell-type fractions at the sample level, and data distribution shifts across platforms.Results: To mitigate these biases, we introduce HarmoDecon, a novel semi-supervised deep learning model for spatial cell-type deconvolution. HarmoDecon leverages pseudo-spots derived from scRNA-seq data and uses Gaussian Mixture Graph Convolutional Networks to address the aforementioned issues. Through extensive simulations on multi-cell spots from STARmap and osmFISH, HarmoDecon outperformed 11 state-of-the-art methods. Additionally, when applied to legacy SRT platforms and 10x Visium datasets, HarmoDecon achieved the highest accuracy in spatial domain clustering and maintained strong correlations between cancer marker genes and cancer cells in human breast cancer samples. These results highlight the utility of HarmoDecon in advancing spatial transcriptomics analysis.Availability and implementation: The HarmoDecon scripts, with the detailed tutorials, are available at https://github.com/ericcombiolab/HarmoDecon/tree/main.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

qcCHIP: an R package to identify clonal hematopoiesis variants using cohort-specific data characteristics. qcCHIP：一个R包，用于使用群体特异性数据特征识别克隆造血变异。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf522

Xiang Liu, Yi-Han Tang, James Blachly, Stephen Edge, Yasminka A Jakubek, Martin McCarter, Abdul Rafeh Naqash, Kenneth G Nepple, Afaf Osman, Matthew J Reilley, Gregory Riedlinger, Bodour Salhia, Bryan P Schneider, Craig Shriver, Michelle L Churchman, Robert J Rounbehler, Jamie K Teer, Nancy Gillis, Mingxiang Teng

{"title":"qcCHIP: an R package to identify clonal hematopoiesis variants using cohort-specific data characteristics.","authors":"Xiang Liu, Yi-Han Tang, James Blachly, Stephen Edge, Yasminka A Jakubek, Martin McCarter, Abdul Rafeh Naqash, Kenneth G Nepple, Afaf Osman, Matthew J Reilley, Gregory Riedlinger, Bodour Salhia, Bryan P Schneider, Craig Shriver, Michelle L Churchman, Robert J Rounbehler, Jamie K Teer, Nancy Gillis, Mingxiang Teng","doi":"10.1093/bioinformatics/btaf522","DOIUrl":"10.1093/bioinformatics/btaf522","url":null,"abstract":"Summary: Clonal hematopoiesis (CH) is a molecular biomarker associated with various adverse outcomes in both healthy individuals and those with underlying conditions, including cancer. Detecting CH usually involves genomic sequencing of individual blood samples followed by robust bioinformatics data filtering. We report an R package, qcCHIP, a bioinformatics pipeline that implements permutation-based parameter optimization to guide quality control filtering and cohort-specific CH identification. We benchmark qcCHIP under various data settings, including different sequencing depths, ranges of cohort sizes, with and without normal-tumor paired samples, and across different cancer types. We show that qcCHIP allows users to customize analysis needs to generate CH calls based on cohort-specific data characteristics.Availability and implementation: qcCHIP R package is freely accessible at GitHub https://github.com/tenglab/qcCHIP and DOI: 10.5281/zenodo.16421861.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovering subnetworks in SBML models. 发现SBML模型中的子网。

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI: 10.1093/bioinformatics/btaf482

Joseph L Hellerstein, Lucian P Smith, Lillian T Tatka, Steven S Andrews, Michael A Kochen, Herbert M Sauro

{"title":"Discovering subnetworks in SBML models.","authors":"Joseph L Hellerstein, Lucian P Smith, Lillian T Tatka, Steven S Andrews, Michael A Kochen, Herbert M Sauro","doi":"10.1093/bioinformatics/btaf482","DOIUrl":"10.1093/bioinformatics/btaf482","url":null,"abstract":"Motivation: Many advances in biomedical research are driven by structural analysis, which investigates interconnections between elements in biological systems (e.g. structural analysis of proteins to infer their function). Herein, we consider subnet discovery in chemical reaction networks (CRNs)-discovering a subset of a target CRN, i.e. structurally identical to a reference CRN. Structural analysis techniques such as motif finding and graph mining look for small, arbitrary, and commonly occurring substructures (e.g. three gene feedforward loops). In contrast, subnet discovery looks for larger, specific, and infrequently occurring substructures (e.g. 10 reactions mitogen-activated protein kinase (MAPK) pathway).Results: We introduce pySubnetSB, an open source Python package for discovering subnets in CRNs that are represented in the Systems Biology Markup Language (SBML) community standard. We show that pySubnetSB achieves large reductions in computational complexity for subnet discovery. For example, in studies of randomly selected target networks with 100 reactions each with a random reference network with 20 reactions, computations are reduced from an infeasible 1078 evaluations to a more practical 108 evaluations. We develop a methodology for assessing the statistical significance of subnet discovery. Last, we study subnets in BioModels for approximately 200 000 pairs of reference and target models. We show that for a reference MAPK pathway, subnet discovery correctly indicates the presence of MAPK function in several target models. The studies also suggest two interesting hypotheses: (a) the potential presence of hidden oscillators in several models in BioModels, and (b) the possibility of a conserved mechanism for intracellular immune response.Availability and implenetation: pySubnetSB is installed using pip install pySubnetSB, and is hosted at https://github.com/ModelEngineering/pySubnetSB/.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0