Bioinformatics advances最新文献

{"title":"Isopeptor: a tool for detecting intramolecular isopeptide bonds in protein structures.","authors":"Francesco Costa, Rob Barringer, Ioannis Riziotis, Antonina Andreeva, Alex Bateman","doi":"10.1093/bioadv/vbaf049","DOIUrl":"10.1093/bioadv/vbaf049","url":null,"abstract":"<p><strong>Motivation: </strong>Intramolecular isopeptide bonds contribute to the structural stability of proteins, and have primarily been identified in domains of bacterial fibrillar adhesins and pili. At present, there is no systematic method available to detect them in newly determined molecular structures. This can result in mis-annotations and incorrect modeling.</p><p><strong>Results: </strong>Here, we present Isopeptor, a computational tool designed to predict the presence of intramolecular isopeptide bonds in experimentally determined structures. Isopeptor utilizes structure-guided template matching via the Jess software, combined with a logistic regression classifier that incorporates root mean square deviation and relative solvent accessible area as key features. The tool demonstrates a precision of 1.0 and a recall of 0.947 when tested on a Protein Data Bank subset of domains known to contain intramolecular isopeptide bonds that have been deposited with incorrectly modeled geometries.</p><p><strong>Availability and implementation: </strong>Isopeptor's Python-based implementation supports integration into bioinformatics workflows and can be accessed via the command line, through a Python API or via a Google Colaboratory implementation (https://colab.research.google.com/github/FranceCosta/Isopeptor_development/blob/main/notebooks/Isopeptide_finder.ipynb). Source code is hosted on GitHub (https://github.com/FranceCosta/isopeptor) and can be installed via the Python package installation manager PIP.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf049"},"PeriodicalIF":2.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11919812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665529","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}

{"title":"HuBMAPR: an R client for the HuBMAP data portal.","authors":"Christine Hou, Shila Ghazanfar, Federico Marini, Martin Morgan, Stephanie C Hicks","doi":"10.1093/bioadv/vbaf048","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf048","url":null,"abstract":"<p><strong>Summary: </strong>The Human BioMolecular Atlas Program (HuBMAP) constructs the worldwide available platform to research the human body at the cellular level. The HuBMAP Data Portal encompasses a wide range of data resources measured on emerging experimental technologies at a spatial resolution. To broaden access to the HuBMAP Data Portal, we introduce an R client called HuBMAPR available on Bioconductor. This provides an efficient and programmatic interface that enables researchers to discover and retrieve HuBMAP data more easily and quickly.</p><p><strong>Availability and implementation: </strong>HuBMAPR is available at https://bioconductor.org/packages/HuBMAPR.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf048"},"PeriodicalIF":2.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11985162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057007","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}

{"title":"DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform.","authors":"Demetris Avraam, Rebecca C Wilson, Noemi Aguirre Chan, Soumya Banerjee, Tom R P Bishop, Olly Butters, Tim Cadman, Luise Cederkvist, Liesbeth Duijts, Xavier Escribà Montagut, Hugh Garner, Gonçalo Gonçalves, Juan R González, Sido Haakma, Mette Hartlev, Jan Hasenauer, Manuel Huth, Eleanor Hyde, Vincent W V Jaddoe, Yannick Marcon, Michaela Th Mayrhofer, Fruzsina Molnar-Gabor, Andrei Scott Morgan, Madeleine Murtagh, Marc Nestor, Anne-Marie Nybo Andersen, Simon Parker, Angela Pinot de Moira, Florian Schwarz, Katrine Strandberg-Larsen, Morris A Swertz, Marieke Welten, Stuart Wheater, Paul Burton","doi":"10.1093/bioadv/vbaf046","DOIUrl":"10.1093/bioadv/vbaf046","url":null,"abstract":"<p><strong>Motivation: </strong>The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practical considerations and by ethico-legal and data governance restrictions. Gaining access to such data can be time-consuming due to the governance requirements associated with data access requests to institutions in different jurisdictions.</p><p><strong>Results: </strong>DataSHIELD is a software solution that enables remote analysis without the need for data transfer (federated analysis). DataSHIELD is a scientifically mature, open-source data access and analysis platform aligned with the 'Five Safes' framework, the international framework governing safe research access to data. It allows real-time analysis while mitigating disclosure risk through an active multi-layer system of disclosure-preventing mechanisms. This combination of real-time remote statistical analysis, disclosure prevention mechanisms, and federation capabilities makes DataSHIELD a solution for addressing many of the technical and regulatory challenges in performing the large-scale statistical analysis of health and biomedical data. This paper describes the key components that comprise the disclosure protection system of DataSHIELD. These broadly fall into three classes: (i) system protection elements, (ii) analysis protection elements, and (iii) governance protection elements.</p><p><strong>Availability and implementation: </strong>Information about the DataSHIELD software is available in https://datashield.org/ and https://github.com/datashield.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf046"},"PeriodicalIF":2.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11968321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797198","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}

{"title":"LipidSigR: a R-based solution for integrated lipidomics data analysis and visualization.","authors":"Chia-Hsin Liu, Pei-Chun Shen, Meng-Hsin Tsai, Hsiu-Cheng Liu, Wen-Jen Lin, Yo-Liang Lai, Yu-De Wang, Mien-Chie Hung, Wei-Chung Cheng","doi":"10.1093/bioadv/vbaf047","DOIUrl":"10.1093/bioadv/vbaf047","url":null,"abstract":"<p><strong>Motivation: </strong>Lipidomics is a rapidly expanding field focused on studying lipid species and classes within biological systems. As the field evolves, there is an increasing demand for user-friendly, open-source software tools capable of handling large and complex datasets while keeping pace with technological advancements. LipidSig, a widely used web-based platform, has been instrumental in data analysis and visualization of lipidomics. However, its limitations become evident when users want to build customized workflows. To address the limitation, we developed a companion R package, LipidSigR, based on the R code of the LipidSig web platform.</p><p><strong>Results: </strong>LipidSigR offers greater flexibility, allowing researchers with basic R programming skills to modify and adapt workflows according to their needs. It has been rigorously tested following CRAN guidelines to ensure compatibility and reproducibility. In demonstrating its functionality, we analyze the case with commonly used experimental design, case versus control, in lipidomics studies. Researchers can follow the use case to explore the key capabilities and build customized lipidomics data analysis workflows using LipidSigR.</p><p><strong>Availability and implementation: </strong>LipidSigR is freely available from https://lipidsig.bioinfomics.org/lipidsigr/index.html and https://github.com/BioinfOMICS/LipidSigR.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf047"},"PeriodicalIF":2.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11919814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665533","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}

{"title":"Drug target assessments: classifying target modulation and associated health effects using multi-level BERT-based classification models.","authors":"Jennifer Venhorst, Gino Kalkman","doi":"10.1093/bioadv/vbaf043","DOIUrl":"10.1093/bioadv/vbaf043","url":null,"abstract":"<p><strong>Motivation: </strong>Drug target selection determines the success of the drug development pipeline. Therefore, novel drug targets need to be assessed for their therapeutic benefits/risks at the earliest stage possible. Where manual risk/benefit analyses are often user-biased and time-consuming, Large Language Models can offer a systematic and efficient approach to curating and analysing literature. Currently, publicly available Large Language Models are lacking for this task, while public platforms for target assessments are limited to co-occurrences.</p><p><strong>Results: </strong>BERT-models for multi-level classification of drug target-health effect relationships described in PubMed were developed. Relationships were classified based on (i) causality; (ii) direction of target modulation; (iii) direction of the associated health effect. The models showed competitive performances with F1 scores between 0.86 and 0.92 and their applicability was demonstrated using ADAM33 and OSM as case study. The developed classification pipeline is the first to allow detailed classification of drug target-health effect relationships. The models provide mechanistic insight into how target modulation affects health and disease, both from an efficacy and safety perspective. The models, deployed on the whole of PubMed and available through the TargetTri platform, are expected to offer a significant advancement in artificial intelligence-assisted target identification and evaluation.</p><p><strong>Availability and implementation: </strong>https://www.targettri.com.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf043"},"PeriodicalIF":2.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11919816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665599","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}

{"title":"A unified hypothesis-free feature extraction framework for diverse epigenomic data.","authors":"Ali Tuğrul Balcı, Maria Chikina","doi":"10.1093/bioadv/vbaf013","DOIUrl":"10.1093/bioadv/vbaf013","url":null,"abstract":"<p><strong>Motivation: </strong>Epigenetic assays using next-generation sequencing have furthered our understanding of the functional genomic regions and the mechanisms of gene regulation. However, a single assay produces billions of data points, with limited information about the biological process due to numerous sources of technical and biological noise. To draw biological conclusions, numerous specialized algorithms have been proposed to summarize the data into higher-order patterns, such as peak calling and the discovery of differentially methylated regions. The key principle underlying these approaches is the search for locally consistent patterns.</p><p><strong>Results: </strong>We propose <math> <mrow> <mrow> <msub><mrow><mi>L</mi></mrow> <mn>0</mn></msub> </mrow> </mrow> </math> segmentation as a universal framework for extracting locally coherent signals for diverse epigenetic sources. <math> <mrow> <mrow> <msub><mrow><mi>L</mi></mrow> <mn>0</mn></msub> </mrow> </mrow> </math> serves to compress the input signal by approximating it as a piecewise constant. We implement a highly scalable <math> <mrow> <mrow> <msub><mrow><mi>L</mi></mrow> <mn>0</mn></msub> </mrow> </mrow> </math> segmentation with additional loss functions designed for sequencing epigenetic data types including Poisson loss for single tracks and binomial loss for methylation/coverage data. We show that the <math> <mrow> <mrow> <msub><mrow><mi>L</mi></mrow> <mn>0</mn></msub> </mrow> </mrow> </math> segmentation approach retains the salient features of the data yet can identify subtle features, such as transcription end sites, missed by other analytic approaches.</p><p><strong>Availability and implementation: </strong>Our approach is implemented as an R package \"l01segmentation\" with a C++ backend. Available at https://github.com/boooooogey/l01segmentation.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf013"},"PeriodicalIF":2.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617585","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}

{"title":"Assessing genome conservation on pangenome graphs with PanSel.","authors":"Matthias Zytnicki","doi":"10.1093/bioadv/vbaf018","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf018","url":null,"abstract":"<p><strong>Motivation: </strong>With more and more telomere-to-telomere genomes assembled, pangenomes make it possible to capture the genomic diversity of a species. Because they introduce less biases, pangenomes, represented as graphs, tend to supplant the usual linear representation of a reference genome, augmented with variations. However, this major change requires new tools adapted to this data structure. Among the numerous questions that can be addressed to a pangenome graph is the search for conserved or divergent genes.</p><p><strong>Results: </strong>In this article, we present a new tool, named PanSel, which computes a conservation score for each segment of the genome, and finds genomic regions that are significantly conserved, or divergent. PanSel can be used on prokaryotes and eukaryotes, with a sequence identity not less than 98%.</p><p><strong>Availability and implementation: </strong>PanSel, written in C++11 with no dependency, is available at https://github.com/mzytnicki/pansel.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf018"},"PeriodicalIF":2.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11908644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652376","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}

{"title":"Welly: a web-tool for visualizing growth curves from microplate data.","authors":"Felix Meier, Tom Williams, Ian Paulsen","doi":"10.1093/bioadv/vbaf038","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf038","url":null,"abstract":"<p><strong>Summary: </strong>Welly is a web-based tool designed to simplify the visualization and analysis of growth curves from 96- and 384-well plates, addressing the limitations of existing commercial and coding-based solutions. Users can upload plate reader data in CSV or Excel format, easily select sample names and replicates and Welly generates interactive growth curves displaying the mean and standard deviation of triplicates. Additional features include heat map visualizations of maximum values, and downloadable interactive graphs of publication-quality figures and statistics files containing area under curve and max growth rate value of replicates.</p><p><strong>Availability and implementation: </strong>Welly is freely available at https://synbioexplorer.pythonanywhere.com, providing an easy-to-use interface accessible to all. All the code is publicly available at the github repository https://github.com/SynBioExplorer/Welly under the MIT license. The website will remain freely accessible for at least 2 years post publication, likely longer.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf038"},"PeriodicalIF":2.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11908640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652339","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}

{"title":"A twin-tower model using MRI and gene for prediction on brain tumor patients' response to therapy.","authors":"Qiyuan Lyu, Fumie Costen","doi":"10.1093/bioadv/vbaf041","DOIUrl":"10.1093/bioadv/vbaf041","url":null,"abstract":"<p><strong>Motivation: </strong>Glioma is the most prevalent and aggressive primary brain tumor, with a poor prognosis of patients and a high mortality rate. Standard treatment of surgery, radiation, and chemotherapy may not be effective for some patients as they suffer from a stable progression of disease after treatment. Hence, it is crucial to predict the patient's response to therapy as a guide for the treatment plan. In this paper, we propose a multimodal model based on both magnetic resonance imaging and genomic data. As the dataset has a majority of single-modality samples with a few ratios of multi-modality samples, we propose a twin-tower architecture to solve the unimodal dominance issue and fully use the single-modality data.</p><p><strong>Results: </strong>The proposed architecture comprises an image encoder and a gene encoder trained on the single-modality samples for feature extraction, along with a classification head trained on multi-modality samples. In this way, all the single-modality samples can be beneficial to the whole model, and the need for the multi-modality is diminished. The proposed model outperforms the comparison methods across all metrics, achieving an accuracy of 85% on the cross-validation. The ablation experiment comparing the proposed architecture with single-modality models reflects the effectiveness of the proposed twin-tower architecture.</p><p><strong>Availability and implementation: </strong>The proposed model exhibits excellent scalability and can accommodate the integration of additional modalities without the requirement of too many multi-modality samples.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf041"},"PeriodicalIF":2.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036698","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}

{"title":"PIPLOM: prediction of exogenous peptide loading on major histocompatibility complex class I molecules.","authors":"Florian Schmidt, Kanxing Wu, Lorenz Gerber, Florence Chioh Wen Jing, Daniel Pedrosa, Glenn Wong Choon Lim, Melissa Wirawan, Christine Eng, Katja Fink, Daniel T MacLeod, Michael Fehlings, Andreas Wilm","doi":"10.1093/bioadv/vbaf037","DOIUrl":"10.1093/bioadv/vbaf037","url":null,"abstract":"<p><strong>Summary: </strong>The exogenous, i.e. <i>in vitro</i>, loading of peptides onto major histocompatibility complex (MHC) class I molecules is a key step in many immunology-related experimental workflows. Here, we provide a machine learning solution, PIPLOM, which is specifically tailored to predict whether peptides can be loaded exogenously onto an MHC class I molecule. Benchmarking on 38 unseen epitopes with in-house ELISA (enzyme-linked immunosorbent assay) experiments showed that PIPLOM is outperforming well-established methods such as NETMHCpan-4.0 or MHCflurry, which are commonly used for the related task of predicting epitope HLA (human leukocyte antigen) haplotype specificity.</p><p><strong>Availability and implementation: </strong>Source code and data are available as Zenodo package 10.5281/zenodo.13771214. PIPLOM is available as a web service at https://piplom.immunoscape.com/.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf037"},"PeriodicalIF":2.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626884","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}