BMC Bioinformatics最新文献

{"title":"mspms: an R package and GUI for multiplex substrate profiling by mass spectrometry.","authors":"Charlie Bayne, Brianna Hurysz, David J Gonzalez, Anthony O'Donoghue","doi":"10.1186/s12859-026-06373-8","DOIUrl":"10.1186/s12859-026-06373-8","url":null,"abstract":"<p><strong>Background: </strong>Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS) is a powerful method for determining the substrate specificity of proteolytic enzymes, which is essential for developing protease inhibitors, diagnostics, and protease-activated therapeutics. However, the complex datasets generated by MSP-MS pose significant analytical challenges and have limited accessibility for non-specialist users.</p><p><strong>Results: </strong>We developed mspms, a Bioconductor R package with an accompanying graphical interface, to streamline the analysis of MSP-MS data. Mspms standardizes workflows for data preparation, processing, statistical analysis, and visualization. The tool is designed for accessibility, serving advanced users through the R package and broader audiences through a web-based interface. We validated mspms using data from four well-characterized cathepsins (A-D), demonstrating that it reliably captures expected substrate specificities.</p><p><strong>Conclusions: </strong>mspms is the first publicly available, comprehensive platform for MSP-MS data analysis downstream of peptide identification and quantification. It integrates preprocessing, normalization, statistical testing, and visualization into a single, transparent, and user-friendly framework, making it a valuable resource for the protease research community. The package is distributed via Bioconductor, and a graphical interface is available online for interactive use.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12934049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043733","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":"zDUR: reference-free FASTQ compressor with high compression ratio and speed.","authors":"Artem Ershov, Renpeng Ding, Qian Fu, Ivan Kozlov, Ekaterina Fadeeva, Evgeniy Mozheiko, Ming Ni, Yong Hou, Yan Zhou","doi":"10.1186/s12859-025-06364-1","DOIUrl":"10.1186/s12859-025-06364-1","url":null,"abstract":"<p><strong>Background: </strong>High-throughput sequencing technologies generate massive amounts of FASTQ data comprising nucleotide sequences, quality scores, and read identifiers, necessitating efficient compression to alleviate storage and transmission burdens. Compared to general-purpose compressors, specialized FASTQ compressors achieve higher compression performance by exploiting the inherent redundancy in FASTQ files. However, existing FASTQ-specialized compressors often suffer from limited data applicability and tend to over-optimize either compression ratio or compression speed at the expense of the other.</p><p><strong>Results: </strong>We present zDUR, a reference-free FASTQ compressor designed for efficient and scalable handling of next-generation sequencing data across diverse platforms and sequencing data types. Benchmarking against six reference-free compressors on 15 representative datasets spanning four sequencing data types demonstrates that zDUR achieves a favorable overall balance between compression ratio and speed, with broad applicability across data types. In particular, on single-cell RNA-seq and spatial transcriptomics datasets, zDUR achieves over a tenfold increase in runtime performance while maintaining higher compression ratios than SPRING, one of the state-of-the-art reference-free FASTQ compressors.</p><p><strong>Conclusions: </strong>zDUR offers a scalable and efficient solution for reference-free FASTQ compression, balancing performance, speed, and usability across diverse datasets.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"46"},"PeriodicalIF":3.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12911337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040309","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":"Identification of differentially expressed genes in RNA-seq data via semi-rigid orthogonal sparse KL-NMTF.","authors":"Grazia Gargano, Flavia Esposito, Nicoletta Del Buono, Sabino Ciavarella, Maria Carmela Vegliante","doi":"10.1186/s12859-026-06370-x","DOIUrl":"10.1186/s12859-026-06370-x","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"39"},"PeriodicalIF":3.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12874962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040346","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":"CNValidatron: accurate and efficient validation of PennCNV calls using computer vision.","authors":"Simone Montalbano, G Bragi Walters, Gudbjorn F Jonsson, Jesper R Gådin, Thomas Werge, Daniel F Gudbjartsson, Hreinn Stefansson, Andrés Ingason","doi":"10.1186/s12859-026-06375-6","DOIUrl":"10.1186/s12859-026-06375-6","url":null,"abstract":"<p><strong>Background: </strong>Large, rare copy number variants (CNVs) are a main source of genetic variation in the genome and are important in both evolution and disease risk. CNVs can be detected using different data sources, including genome sequencing, genotyping arrays and quantitative PCR experiments, but in most large cohorts, genotyping arrays remain the most prevalent source. Current methods to call CNVs from genotyping array data suffer from high false positive rates and while multiple approaches, including QC filtering, visual inspection of intensity tracks, and wet-lab validation are commonly applied to counter this problem, such methods are often non-specific (QC filtering) or inefficient (visual and wet-lab validations) at a genome-wide scale.</p><p><strong>Results: </strong>We have assembled the largest collection of human-verified CNV calls using visual validation, totalling almost 60,000 calls from 22,500 samples from three cohorts genotyped on several different arrays. Across all cohorts our visual validation found the majority of CNV calls to be false positive (53.7%) or unclear (9.7%). The false positive fraction varied substantially across datasets and genomic regions, and we show that existing filtering methods based on QC metrics are inefficient in removing false calls. Given the supremacy of visual validation over existing filtering methods in controlling the false positive fraction, we used a subset of our visual validation dataset to train a convolutional neural network to automate the validation of CNVs through machine vision. We tested the efficacy of the model using the remainder of the dataset and found the performance exceeded 90% in most measures, approximating that of a human analyst. Orthogonal validation with genome sequencing data found our visual validation to be highly accurate, with only 1.7% of calls supported by the sequencing dataset deemed as false by the human analyst, and a further 7.5% deemed as unclear.</p><p><strong>Conclusions: </strong>Visual inspection is the only effective validation approach for CNV calls. Our model is capable of automating this task at scale with very high accuracy, as shown by testing both within-sample and out-of-sample. The software is available as an R package at https://github.com/SinomeM/CNValidatron_fl .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"47"},"PeriodicalIF":3.3,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12910869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146040336","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":"A negative binomial latent factor model for paired microbiome sequencing data.","authors":"Hyotae Kim, Nazema Y Siddiqui, Lisa Karstens, Li Ma","doi":"10.1186/s12859-025-06362-3","DOIUrl":"10.1186/s12859-025-06362-3","url":null,"abstract":"<p><strong>Background: </strong>Microbiome sequencing data are often collected from several body sites and exhibit dependencies. Our objective is to develop a model that enables joint analysis of data from different sites by capturing the underlying cross-site dependencies. The proposed model incorporates (i) latent factors shared across sites to explain common subject effects and to serve as the source of correlation between the sites and (ii) mixtures of latent factors to allow heterogeneity among the subjects in cross-site associations.</p><p><strong>Results: </strong>Our simulation studies demonstrate that stronger associations between two sites lead to greater efficiency loss in regression analysis when such dependence is ignored in modeling. In a case study involving samples collected from a study on the female urogenital microbiome with aging, our model leads to the detection of covariate associations of the vaginal and urine microbiomes that are otherwise not statistically significant under a similar regression model applied to the two sites separately.</p><p><strong>Conclusions: </strong>We propose a latent factor model for microbiome sequencing data collected from multiple sites. It captures the presumptive underlying cross-site associations without compromising estimation accuracy or inference efficiency in the absence of such associations. In addition, our proposed model improves predictive performance by enabling the prediction of microbial abundance at one site based on observations from another. We also provide an extended framework that allows for clustering of subjects (samples) and cluster-specific levels of paired association. Under this extended framework, clusters can be classified according to their association strengths.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"45"},"PeriodicalIF":3.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12910815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028136","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":"QuAPPro: an R shiny app for quantification and alignment of polysome profiles.","authors":"Chiara Schiller, Matthias Lemmer, Sonja Reitter, Janina A Lehmann, Kai Fenzl, Johanna Schott","doi":"10.1186/s12859-026-06379-2","DOIUrl":"10.1186/s12859-026-06379-2","url":null,"abstract":"<p><strong>Background: </strong>Polysome profiling is a widespread technique to study mRNA translation. After separation of cellular particles by ultracentrifugation on a sucrose-density gradient, a UV absorbance profile is recorded during elution, which mostly reflects RNA content and shows distinct peaks for ribosomal subunits, monosomes and polysomes with increasing number of ribosomes. This profile can be used to assess global translational activity, or to reveal changes in ribosome biogenesis and translation elongation. In addition, it is also possible to measure the association of fluorescently tagged proteins with ribosomal subunits or polysomes. Alignment and quantification of polysome profiles usually relies on spreadsheet programs, custom R/Python scripts or commercial software.</p><p><strong>Results: </strong>With QuAPPro, we present the first interactive web app that allows quantification and alignment of polysome profiles, independently of the device or software that was used to generate the profiles. QuAPPro was written in R, with a graphical user interface implemented in R shiny. It supports interactive visualization and analysis of polysome profiles, including profile smoothing, baseline selection, alignment along a defined point on the x-axis, quantification of profile subsections and deconvolution for resolving individual peaks. Fluorescence profiles can be aligned and quantified in parallel. Finally, quantification results can be summarized and visualized as bar plots. Every interactive plot can be exported directly in a publication-ready format.</p><p><strong>Conclusions: </strong>This user-friendly tool does not only speed up the analysis of polysome profiles but also facilitates reproducibility and documentation of the process, without the need for programming abilities or commercial software.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"22"},"PeriodicalIF":3.3,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12849056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028149","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":"CRESCENT, a comprehensive RNA-Seq expression, splicing, and coding/non-coding element network tool.","authors":"Gilles Sireta, Gwendal Cueff, Vincent Darbot, Marie Lefebvre, Simon Amiard, Aline V Probst, Christophe Tatout","doi":"10.1186/s12859-026-06368-5","DOIUrl":"10.1186/s12859-026-06368-5","url":null,"abstract":"<p><strong>Background: </strong>Traditional short-read RNA-Seq analysis pipelines predominantly focus on protein-coding genes, often overlooking other genomic sequences such as transposable elements (TEs) and non-coding RNA dynamics and do not usually investigate splicing events or transcript usage. To fully capture the complexity of the transcriptome, and in particular transcriptomic regulation, it is crucial to adopt a comprehensive approach that integrates these diverse aspects, providing a more complete and nuanced understanding of expression dynamics in the studied organism.</p><p><strong>Results: </strong>To address these limitations, we present CRESCENT (Comprehensive RNA-seq Expression, Splicing, and Coding/non-coding Element Network Tool), a Snakemake workflow capable of performing a fully automated and comprehensive RNA-Seq analysis. CRESCENT integrates multiple tools at each step of the workflow and enables analysis of differential expression, differential alternative splicing, differential transcript usage, and gene ontology-based functional enrichment for all three. The workflow takes advantage of multiple Snakemake wrappers to minimize required installations for the user, integrating the latest versions of popular bioinformatic tools. It can be run for a complete analysis or for only a specific part in accordance with the configuration file provided by the user. The CRESCENT workflow was validated, demonstrating the pipeline's reliability, as differentially expressed protein-coding genes, TEs and differential alternative splicing events were consistent with previously published datasets. Finally, benchmarking CRESCENT performance indicated that it can be run on a personal computer or a remote server, including a high-performance computing cluster, allowing a user to process small single-end sequencing on species possessing a small genome like Arabidopsis thaliana to very large paired-end sequencing on polyploid species like wheat.</p><p><strong>Conclusion and availability: </strong>CRESCENT is a scalable solution for comprehensive transcriptomic profiling. It is freely available at https://github.com/gilless429/crescent .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"43"},"PeriodicalIF":3.3,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12895761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017181","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":"Beyond the clipboard: data collection with GridScore NEXT.","authors":"Sebastian Raubach, Miriam Schreiber, Ruth Hamilton, Gaynor McKenzie, Susan McCallum, Benjamin Kilian, Alan Humphries, Loi Huu Nguyen, Tin Huynh Quang, Akanksha Singh, Shivali Sharma, Sarah Trinder, Manuel Feser, Paul D Shaw","doi":"10.1186/s12859-025-06352-5","DOIUrl":"10.1186/s12859-025-06352-5","url":null,"abstract":"<p><strong>Background: </strong>Accurate acquisition of phenotypic data is critical for cataloguing and utilising genetic variation in cultivated crops, landraces, and their wild relatives. The collection of phenotypic data using handwritten notes often introduces errors which can and should be avoided. Electronic data collection is crucial for ensuring error prevention and data standardisation and thus ensuring high-quality, reliable data.</p><p><strong>Implementation: </strong>This paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research. Building on its predecessor, GridScore, the development of GridScore NEXT was driven by real life, in the field interactions with expert user groups across a number of crops. This iterative design methodology allowed the development and testing of new features. Collaborators from the 'Biodiversity for Opportunities, Livelihoods and Development' (BOLD) project, focusing on crops including rice, grasspea, and alfalfa, along with barley, potato, vegetable and blueberry teams, provided invaluable insights through training sessions and interviews and in the field use of the application.</p><p><strong>Results: </strong>Key improvements to GridScore NEXT include enhanced data collection tools, supporting individual plant phenotyping within plots and enabling new data types such as GPS coordinates and image traits. GridScore NEXT provides customisable user defined validation rules to help prevent errors and incorporates barcode scanning for accurate, efficient data capture. The application offers an increased toolbox of data visualizations over its predecessor including heatmaps and statistical box plots, which aid in identifying potential data issues and understanding trial performance in the field. GridScore NEXT is cross-platform and can operate without an internet connection, making it ideal for field use in remote areas. Its adoption has led to standardisation of methods, significant error reduction, and the timely sharing of data, enabling quicker decision-making in pre-breeding and characterisation experiments. GridScore NEXT is available under an open-source (Apache 2.0) licence and freely available to all with no restrictions. It offers self-hosting options for enhanced data security and privacy. GridScore NEXT shows broad applicability across a diverse range of not only plant phenotyping experiments, but any experiment that requires the collection of accurate data.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"44"},"PeriodicalIF":3.3,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12905885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017147","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":"BioMark: biomarker analysis tool.","authors":"Mehmet Ali Balikci, Cyrille Mesue Njume, Ali Cakmak","doi":"10.1186/s12859-025-06346-3","DOIUrl":"10.1186/s12859-025-06346-3","url":null,"abstract":"<p><p>Biomarkers play a pivotal role in disease diagnosis and prognosis by offering molecular insights into biological states. The rapid growth of high-throughput omics technologies has enabled the generation of large-scale biomarker datasets, yet analyzing these complex, high-dimensional data remains a major challenge-particularly for researchers lacking advanced computational expertise. While numerous tools exist for omics data analysis, many fall short in providing an integrated, user-friendly environment tailored specifically for biomarker discovery and interpretation. To address this gap, we present BioMark, a web-based platform designed to streamline biomarker analysis across diverse omics types. BioMark integrates robust statistical methods with widely used machine learning algorithms to support key workflows including statistical analysis, dimensionality reduction, classification, and subsequent model explanation. The platform emphasizes accessibility, offering intuitive visualizations and automated reporting to facilitate interpretation and dissemination of results. Notably, BioMark also offers a feature-ranking strategy that consolidates outputs from multiple analytical methods, enhancing the robustness of biomarker identification. By lowering the barrier to advanced biomarker analytics, BioMark empowers a broader range of researchers to uncover clinically relevant molecular signatures and accelerate translational research. Biomark is available online at https://bioinf.itu.edu.tr/biomark .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"42"},"PeriodicalIF":3.3,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12896108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008491","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":"Benchmarking of methods to analyse data derived from GBS-MeDIP.","authors":"Violeta de Anca Prado, Fábio Pértille, Pedro Sá, Marta Gòdia, Joëlle Rüegg, Josep C Jimenez-Chillaron, Carlos Guerrero-Bosagna","doi":"10.1186/s12859-025-06330-x","DOIUrl":"10.1186/s12859-025-06330-x","url":null,"abstract":"","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":"17"},"PeriodicalIF":3.3,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12829230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146002877","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}