BMC Bioinformatics最新文献

{"title":"SMILES alignment: a dynamic programming approach for the alignment of metabolites and other small organic molecules.","authors":"Alexis L Tang, David A Liberles","doi":"10.1186/s12859-025-06278-y","DOIUrl":"10.1186/s12859-025-06278-y","url":null,"abstract":"<p><strong>Background: </strong>There is a need for computational approaches to compare small organic molecules based on chemical similarity or for evaluating biochemical transformations. No tool currently exists to generate global molecular alignments for small organic molecules. The study introduces a new approach to molecular alignment in the Simplified Molecular Input Line Entry System (SMILES) format. This method leverages programming and scoring alignments to minimize differences in electronegativity, here using a measure of atomic partial charges to address the challenge of understanding structural transformations in reaction pathways. This can be applied to study transitions from linear to cyclical pathways.</p><p><strong>Results: </strong>The proposed method is based on the Needleman-Wunsch algorithm for sequence alignment, but it uses a modified scoring function for different input data. Validation against a benchmarked dataset from the Krebs cycle, based on the known chemical transformations in the pathway, confirmed the efficacy of the approach in aligning atoms that are known to be the same across the transformation. The algorithm also quantified each transformation of metabolites in the Pentose Phosphate Pathway and in Glycolysis. The method was used to study the difference in chemical similarity over transformations between linear and cyclical pathways. The study found a midpoint dissimilarity peak in cyclical pathways (particularly the Krebs Cycle) and a progressive decrease in molecular similarity in linear pathways, consistent with expectations.</p><p><strong>Conclusions: </strong>The study introduces an algorithm that quantifies molecular transformations in metabolic pathways. The algorithm effectively highlights structural changes and was applied to a hypothesis about the transition from linear to cyclical structures. The software, which provides valuable insights into molecular transformations, is available at: https://github.com/24atang/SMILES-Alignment.git.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"251"},"PeriodicalIF":3.3,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12534939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312332","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}

{"title":"GRiNS: a python library for simulating gene regulatory network dynamics.","authors":"Pradyumna Harlapur, Harshavardhan Bv, Mohit Kumar Jolly","doi":"10.1186/s12859-025-06268-0","DOIUrl":"10.1186/s12859-025-06268-0","url":null,"abstract":"<p><strong>Background: </strong>The emergent dynamics of complex gene regulatory networks govern various cellular processes. However, understanding these dynamics is challenging due to the difficulty of parameterizing the computational models for these networks, especially as the network size increases. Here, we introduce a simulation library, Gene Regulatory Interaction Network Simulator (GRiNS), to address these challenges.</p><p><strong>Results: </strong>GRiNS integrates popular parameter-agnostic simulation frameworks, RACIPE and Boolean Ising formalism, into a single Python library capable of leveraging GPU acceleration for efficient and scalable simulations. GRiNS extends the ordinary differential equations (ODE) based RACIPE framework with a more modular design, allowing users to choose parameters, initial conditions, and time-series outputs for greater customisability and accuracy in simulations. For large networks, where ODE-based simulation formalisms do not scale well, GRiNS implements Boolean Ising formalism, providing a simplified, coarse-grained alternative, significantly reducing the computational cost while capturing key dynamical behaviours of large regulatory networks.</p><p><strong>Conclusion: </strong>GRiNS enables parameter-agnostic modeling of gene regulatory networks to study their dynamic and steady-state behaviors in a scalable and efficient manner. The documentation and installation instructions for GRiNS can be found at https://moltenecdysone09.github.io/GRiNS/ .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"250"},"PeriodicalIF":3.3,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12535162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312397","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}

{"title":"Direct construction of sparse suffix arrays with Libsais.","authors":"Simon Van de Vyver, Tibo Vande Moortele, Peter Dawyndt, Bart Mesuere, Pieter Verschaffelt","doi":"10.1186/s12859-025-06277-z","DOIUrl":"10.1186/s12859-025-06277-z","url":null,"abstract":"<p><strong>Background: </strong>Pattern matching is a fundamental challenge in bioinformatics, especially in the fields of genomics, transcriptomics and proteomics. Efficient indexing structures, such as suffix arrays, are critical for searching large datasets. A sparse suffix array (SSA) retains only suffixes at every k-th position in the text, where k is the sparseness factor. While sparse suffix arrays offer significant memory savings compared to full suffix arrays, they typically still require the construction of a full suffix array prior to a sampling step, resulting in substantial memory overhead during the construction phase.</p><p><strong>Results: </strong>We present an alternative method to directly construct the sparse suffix array using a simple, yet powerful text encoding. This encoding reduces the input text length by grouping characters, thereby enabling direct SSA construction by extending the widely used Libsais library. This approach bypasses the need to construct a full suffix array, reducing memory usage and construction time by 50 to 75% when building a sparse suffix array with sparseness factor 3 or 4 for various nucleotide and amino acid datasets. Depending on the alphabet size, similar gains can be achieved for sparseness factors up to 8. For higher sparseness factors, comparable performance improvements can be obtained by constructing the SSA using a suitable divisor of the desired sparseness factor, followed by a subsampling step. The method is particularly effective for applications with small alphabets, such as a nucleotide or amino acid alphabet. An open-source implementation of this method is available on GitHub, enabling easy adoption for large-scale bioinformatics applications.</p><p><strong>Conclusions: </strong>We introduce an efficient method for the construction of sparse suffix arrays for large datasets. Central to this approach is the introduction of a simple text transformation, which then serves as input to Libsais. This method reduces the length of both the input text and the resulting suffix array by a factor of k, which improves execution time and memory usage significantly.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"252"},"PeriodicalIF":3.3,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12535041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145312395","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}

{"title":"JINet: easy and secure private data analysis for everyone.","authors":"Giada Lalli, James Collier, Yves Moreau, Daniele Raimondi","doi":"10.1186/s12859-025-06244-8","DOIUrl":"10.1186/s12859-025-06244-8","url":null,"abstract":"<p><strong>Background: </strong>The barriers to effective data analysis are sometimes insurmountable. Concerns ranging from privacy, security, and complexity can prevent researchers from using existing data analysis tools.</p><p><strong>Results: </strong>JINet is a web browser-based platform intended to democratise access to advanced clinical and genomic data analysis software. It hosts numerous data analysis applications that are run in the safety of each User's web browser, without the data ever leaving their machine.</p><p><strong>Conclusions: </strong>JINet promotes collaboration, standardisation and reproducibility by sharing scripts rather than data and creating a self-sustaining community around it in which Users and data analysis tools Developers interact thanks to JINet's interoperability primitives.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"248"},"PeriodicalIF":3.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12532838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306782","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}

{"title":"Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations.","authors":"Tianjian Yang, Wei Vivian Li","doi":"10.1186/s12859-025-06227-9","DOIUrl":"10.1186/s12859-025-06227-9","url":null,"abstract":"<p><p>The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only integrate diverse modalities but also leverage their complementary information to improve clustering accuracy and insights, especially when dealing with partial observations with missing data. We propose Generalized Probabilistic Canonical Correlation Analysis (GPCCA), an unsupervised method for the integration and joint dimensionality reduction of multi-modal data. GPCCA addresses key challenges in multi-modal data analysis by handling missing values within the model, enabling the integration of more than two modalities, and identifying informative features while accounting for correlations within individual modalities. GPCCA demonstrates robustness to various missing data patterns and provides low-dimensional embeddings that facilitate downstream clustering and analysis. In a range of simulation settings, GPCCA outperforms existing methods in capturing essential patterns across modalities. Additionally, we demonstrate its applicability to multi-omics data from TCGA cancer datasets and a multi-view image dataset. GPCCA offers a useful framework for multi-modal data integration, effectively handling missing data and providing informative low-dimensional embeddings. Its performance across cancer genomics and multi-view image data highlights its robustness and potential for broad application. To make the method accessible to the wider research community, we have released an R package, GPCCA, which is available at https://github.com/Kaversoniano/GPCCA .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"249"},"PeriodicalIF":3.3,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12533326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145306739","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}

{"title":"DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion.","authors":"Siqi Chen, Anhong Zheng, Weichi Yu, Chao Zhan","doi":"10.1186/s12859-025-06272-4","DOIUrl":"10.1186/s12859-025-06272-4","url":null,"abstract":"<p><strong>Background: </strong>The identification of protein-protein interaction (PPI) plays a crucial role in understanding the mechanisms of complex biological processes. Current research in predicting PPI has shown remarkable progress by integrating protein information with PPI topology structure. Nevertheless, these approaches frequently overlook the dynamic nature of protein and PPI structures during cellular processes, including conformational alterations and variations in binding affinities under diverse environmental circumstances. Additionally, the insufficient availability of comprehensive protein data hinders accurate protein representation. Consequently, these shortcomings restrict the model's generalizability and predictive precision.</p><p><strong>Results: </strong>To address this, we introduce DCMF-PPI (Dynamic condition and multi-feature fusion framework for PPI), a novel hybrid framework that integrates dynamic modeling, multi-scale feature extraction, and probabilistic graph representation learning. DCMF-PPI comprises three core modules: (1) PortT5-GAT Module: The protein language model PortT5 is utilized to extract residue-level protein features, which are integrated with dynamic temporal dependencies. Graph attention networks are then employed to capture context-aware structural variations in protein interactions; (2) MPSWA Module: Employs parallel convolutional neural networks combined with wavelet transform to extract multi-scale features from diverse protein residue types, enhancing the representation of sequence and structural heterogeneity; (3) VGAE Module: Utilizes a Variational Graph Autoencoder to learn probabilistic latent representations, facilitating dynamic modeling of PPI graph structures and capturing uncertainty in interaction dynamics.</p><p><strong>Conclusion: </strong>We conducted comprehensive experiments on benchmark datasets demonstrating that DCMF-PPI outperforms state-of-the-art methods in PPI prediction, achieving significant improvements in accuracy, precision, and recall. The framework's ability to fuse dynamic conditions and multi-level features highlights its effectiveness in modeling real-world biological complexities, positioning it as a robust tool for advancing PPI research and downstream applications in systems biology and drug discovery.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"247"},"PeriodicalIF":3.3,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12522320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145298374","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}

{"title":"SAGPEK: fast and flexible approach to identify genotypes of Sanger sequencing data.","authors":"Jinpeng Wang, Shuo Sun, Yaran Zhang, Ning Huang, Chunhong Yang, Yaping Gao, Xiuge Wang, Zhihua Ju, Qiang Jiang, Yao Xiao, Xiaochao Wei, Wenhao Liu, Jinming Huang","doi":"10.1186/s12859-025-06271-5","DOIUrl":"10.1186/s12859-025-06271-5","url":null,"abstract":"<p><strong>Background: </strong>Although Sanger sequencing remains widely used in human genetic disease diagnosis and livestock breeding, software packages for analyzing such data have seen little innovation over time. Determining the genotypes of tens to hundreds of loci across hundreds or thousands of samples still typically relies on manual visual confirmation with traditional software, a process that is both time-consuming and prone to error.</p><p><strong>Results: </strong>We present SAGPEK, a tool that automatically identifies genotypes at target loci from hundreds to thousands of ABI-format Sanger sequencing files and directly outputs the results. SAGPEK extracts the signal intensities for A, G, C, and T bases, performs base calling, and determines each site's homozygous or heterozygous status. It then generates a primary sequence composed of the bases with the highest signal intensities and records secondary bases for heterozygous sites. Using either built-in or user-provided anchor sequences, SAGPEK maps the coordinates of target loci, reports their genotypes, and, when applicable, annotates the corresponding amino acid changes.</p><p><strong>Conclusions: </strong>SAGPEK provides an efficient, flexible, and user-friendly solution for analyzing ABI-format Sanger sequencing data, enabling simultaneous genotyping of tens of loci across hundreds of samples. Its innovation lies not in introducing new base-calling methods, but in integrating versatile functionalities-batch genotyping, customizable anchor sequences, amino acid alteration reporting, chromatogram visualization, and local execution-into a single open-source package. This makes SAGPEK well suited for applications such as human genetic disease screening, drug-resistance mutation detection, and functional mutation identification in livestock and other organisms.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"246"},"PeriodicalIF":3.3,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290903","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}

{"title":"Prior knowledge on context-driven DNA fragmentation probabilities can improve de novo genome assembly algorithms.","authors":"Patrick Pflughaupt, Aleksandr B Sahakyan","doi":"10.1186/s12859-025-06267-1","DOIUrl":"10.1186/s12859-025-06267-1","url":null,"abstract":"<p><strong>Background: </strong>De novo genome assembly poses challenges when dealing with highly degraded DNA samples or ultrashort sequencing reads. Probabilistic approaches have been offered to enhance the algorithms, though existing methods rely solely on expected k-meric frequencies in the assemblies, neglecting the broader sequence context that strongly influences DNA fragmentation patterns.</p><p><strong>Results: </strong>Here, we present a proof of concept showing that prior knowledge on sequence context-driven DNA breakage propensities, through the dedicated parameterisation of k-mer assigned breakage probabilities, can be utilised to recover DNA assemblies that originate from fragmentation patterns more likely to have happened. Our approach is beneficial even for read lengths below the common ∼ 25 bp threshold of modern de novo genome assembly algorithms, and well below the threshold used for ultrashort fragments used in ancient DNA research.</p><p><strong>Conclusions: </strong>This work could lay the groundwork for future enhanced de novo genome assembly algorithms, with improved ability to effectively assemble and evaluate ultrashort DNA fragments relevant for cell-free, ancient, and forensic DNA research.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"245"},"PeriodicalIF":3.3,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12519797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285228","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}

{"title":"Correction: CITEViz: interactively classify cell populations in CITE-Seq via a flow cytometry-like gating workflow using R-Shiny.","authors":"Garth L Kong, Thai T Nguyen, Wesley K Rosales, Anjali D Panikar, John H W Cheney, Theresa A Lusardi, William M Yashar, Brittany M Curtiss, Sarah A Carratt, Theodore P Braun, Julia E Maxson","doi":"10.1186/s12859-025-06274-2","DOIUrl":"10.1186/s12859-025-06274-2","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"244"},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145257218","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}

{"title":"H³NGST: a fully automated, web-based platform for end-to-end ChIP-seq analysis.","authors":"Hyeon Ho Heo, Soo-Jong Um","doi":"10.1186/s12859-025-06247-5","DOIUrl":"10.1186/s12859-025-06247-5","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"243"},"PeriodicalIF":3.3,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12512806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145257284","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}