Bioinformatics (Oxford, England)最新文献

{"title":"AlphaPulldown2-a general pipeline for high-throughput structural modeling.","authors":"Dmitry Molodenskiy, Valentin J Maurer, Dingquan Yu, Grzegorz Chojnowski, Stefan Bienert, Gerardo Tauriello, Konstantin Gilep, Torsten Schwede, Jan Kosinski","doi":"10.1093/bioinformatics/btaf115","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf115","url":null,"abstract":"<p><strong>Summary: </strong>AlphaPulldown2 streamlines protein structural modeling by automating workflows, improving code adaptability, and optimizing data management for large-scale applications. It introduces an automated Snakemake pipeline, compressed data storage, support for additional modeling backends like UniFold and AlphaLink2, and a range of other improvements. These upgrades make AlphaPulldown2 a versatile platform for predicting both binary interactions and complex multi-unit assemblies.</p><p><strong>Availability and implementation: </strong>AlphaPulldown2 is freely available at https://github.com/KosinskiLab/AlphaPulldown.</p><p><strong>Supplementary information: </strong>Supplementary information is available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"bpRNA-CosMoS: A Robust and Efficient RNA Structural Comparison Method Using k-mer based Cosine Similarity.","authors":"Brittany Lasher, David A Hendrix","doi":"10.1093/bioinformatics/btaf108","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf108","url":null,"abstract":"<p><strong>Motivation: </strong>RNA secondary structure is often essential to function. Recent work has led to the development of high-throughput experimental probing methods for structure determination. Although structure is more conserved than primary sequence, much of the bioinformatics pipelines to connect RNA structure to function rely on nucleotide sequence alignments rather than structural similarity. There is a need to develop methods for secondary structure comparisons that are also fast and efficient to navigate the vast amounts of structural data. K-mer based similarity approaches are valued for their computational efficiency and have been applied for protein, DNA, and RNA primary sequences. However, these approaches have yet to be implemented for RNA secondary structure.</p><p><strong>Results: </strong>Our method, bpRNA-CosMoS, fills this gap by using k-mers and length-weighted cosine similarity to compute similarity scores between RNA structures. bpRNA-CosMoS is built upon the bpRNA structure array, which represents the structural category of each nucleotide as a single-character structural code (e.g. hairpin=H, etc). A structural comparison score is calculated through cosine similarity of the k-mer count vectors, generated from structure arrays. A major challenge with k-mer based methods is that they often ignore the length of the sequences being compared. We have overcome this with a length-weighted penalty that addresses cases of two RNAs of vastly different lengths. In addition, the use of \"fuzzy counting\" has added some optional flexibility to decrease the negative impact that small structural variations have on the similarity score. This results in a robust and efficient way to identify structural comparisons across large datasets.</p><p><strong>Availability: </strong>The code and application guidelines of bpRNA-CosMoS are made available at github (https://github.com/BLasher113/bpRNA-CosMoS) and Zenodo (10.5281/zenodo.14715285).</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging basecaller's move table to generate a lightweight k-mer model for nanopore sequencing analysis.","authors":"Hiruna Samarakoon, Yuk Kei Wan, Sri Parameswaran, Jonathan Göke, Hasindu Gamaarachchi, Ira W Deveson","doi":"10.1093/bioinformatics/btaf111","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf111","url":null,"abstract":"<p><strong>Motivation: </strong>Nanopore sequencing by Oxford Nanopore Technologies (ONT) enables direct analysis of DNA and RNA by capturing raw electrical signals. Different nanopore chemistries have varied k-mer lengths, current levels, and standard deviations, which are stored in 'k-mer models'. In cases where official models are lacking or unsuitable for specific sequencing conditions, tailored k-mer models are crucial to ensure precise signal-to-sequence alignment, analysis and interpretation. The process of transforming raw signal data into nucleotide sequences, known as basecalling, is a fundamental step in nanopore sequencing.</p><p><strong>Results: </strong>In this study, we leverage the move table produced by ONT's basecalling software to create a lightweight de novo k-mer model for RNA004 chemistry. We demonstrate the validity of our custom k-mer model by using it to guide signal-to-sequence alignment analysis, achieving high alignment rates (97.48%) compared to larger default models. Additionally, our 5-mer model exhibits similar performance as the default 9-mer models another analysis, such as detection of m6A RNA modifications. We provide our method, termed Poregen, as a generalisable approach for creation of custom, de novo k-mer models for nanopore signal data analysis.</p><p><strong>Availability and implementation: </strong>Poregen is an open source package under an MIT licence: https://github.com/hiruna72/poregen.</p><p><strong>Supplementary information: </strong>Supplementary Note 1.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QuickEd: High-performance exact sequence alignment based on bound-and-align.","authors":"Max Doblas, Oscar Lostes-Cazorla, Quim Aguado-Puig, Cristian Iñiguez, Miquel Moreto, Santiago Marco-Sola","doi":"10.1093/bioinformatics/btaf112","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf112","url":null,"abstract":"<p><strong>Motivation: </strong>Pairwise sequence alignment is a core component of multiple sequencing-data analysis tools. Recent advancements in sequencing technologies have enabled the generation of longer sequences at a much lower price. Thus, long-read sequencing technologies have become increasingly popular in sequencing-based studies. However, classical sequence analysis algorithms face significant scalability challenges when aligning long sequences. As a result, several heuristic methods have been developed to improve performance at the expense of accuracy, as they often fail to produce the optimal alignment.</p><p><strong>Results: </strong>This paper introduces QuickEd, a sequence alignment algorithm based on a bound-and-align strategy. First, QuickEd effectively bounds the maximum alignment-score using efficient heuristic strategies. Then, QuickEd utilizes this bound to reduce the computations required to produce the optimal alignment. Compared to O(n2) complexity of traditional dynamic programming algorithms, QuickEd's bound-and-align strategy achieves O(ns^) complexity, where n is the sequence length and s^ is an estimated upper bound of the alignment-score between the sequences. As a result, QuickEd is consistently faster than other state-of-the-art implementations, such as Edlib and BiWFA, achieving performance speedups of 4.2-5.9× and 3.8-4.4×, respectively, aligning long and noisy datasets. In addition, QuickEd maintains a stable memory footprint below 35 MB while aligning sequences up to 1 Mbp.</p><p><strong>Availability: </strong>QuickEd code and documentation are publicly available at https://github.com/maxdoblas/QuickEd.</p><p><strong>Supplementary data: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LipidFun: A Database of Lipid Functions.","authors":"Wen-Jen Lin, Chia-Hsin Liu, Ming-Siang Huang, Pei-Chun Shen, Hsiu-Cheng Liu, Meng-Hsin Tsai, Yo-Liang Lai, Yu-De Wang, Mien-Chie Hung, Nai-Wen Chang, Wei-Chung Cheng","doi":"10.1093/bioinformatics/btaf110","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf110","url":null,"abstract":"<p><strong>Motivation: </strong>Lipids play crucial roles in various biological functions and diseases. However, a gap exists in databases providing information of lipids functions based on curated information. Consequently, LipidFun is purposed as the first lipid function database with sentence-level evidence detailing lipid-related phenotypes and biological functions.</p><p><strong>Results: </strong>Potential lipid functions were extracted from the biomedical literature using natural language processing techniques, with accuracy and reliability ensured through manual curation by four domain experts. LipidFun constructs classification systems for lipids, biological functions, and phenotypes for named entity recognition. Sentence-level evidence is extracted to highlight connections to lipid-associated biological processes and diseases. Integrating these classification systems and a large amount of sentence-level evidence allows LipidFun to provide an overview of lipid-phenotype and lipid-biological function associations through concise visualizations. Overall, LipidFun unravels the relationships between lipids and biological mechanisms, underscoring their overarching influence on physiological processes.</p><p><strong>Availability and implementation: </strong>LipidFun is available at https://lipidfun.bioinfomics.org/.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AJGM: Joint Learning of Heterogeneous Gene Networks with Adaptive Graphical Model.","authors":"Shunqi Yang, Lingyi Hu, Pengzhou Chen, Xiangxiang Zeng, Shanjun Mao","doi":"10.1093/bioinformatics/btaf096","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf096","url":null,"abstract":"<p><strong>Motivation: </strong>Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships. Additionally, GGM assumes data follows a multivariate Gaussian distribution, which is often not the case with zero-inflated scRNA-seq data.</p><p><strong>Results: </strong>We propose an Adaptive Joint Graphical Model (AJGM) for estimating multiple gene networks from single-cell or bulk data with unknown heterogeneity. In AJGM, an overall network is introduced to capture relationships shared by all samples. The model establishes connections between the subtype networks and the overall network through adaptive weights, enabling it to focus more effectively on gene relationships shared across all networks, thereby enhancing the accuracy of network estimation. On synthetic data, the proposed approach outperforms existing methods in terms of sample classification and network inference, particularly excelling in the identification of shared relationships. Applying this method to gene expression data from triple-negative breast cancer confirms known gene pathways and hub genes, while also revealing novel biological insights.</p><p><strong>Availability and implementation: </strong>The Python code and demonstrations of the proposed approaches are available at https://github.com/yyytim/AJGM, and the software is archived in Zenodo with DOI: 10.5281/zenodo.14740972.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UnifiedGreatMod: A New Holistic Modelling Paradigm for Studying Biological Systems on a Complete and Harmonious Scale.","authors":"Riccardo Aucello, Simone Pernice, Dora Tortarolo, Raffaele A Calogero, Celia Herrera-Rincon, Giulia Ronchi, Stefano Geuna, Francesca Cordero, Pietro Lió, Marco Beccuti","doi":"10.1093/bioinformatics/btaf103","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf103","url":null,"abstract":"<p><strong>Motivation: </strong>Computational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognisable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, for example, to the cancer evolution study.</p><p><strong>Results: </strong>To address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It enables functional studies by combining the analysis of the system's multi-level stable states with its fluctuating conditions. This approach helps to investigate the functional relationships and dependencies among biological entities. This is achieved thanks to the hybridisation of two analysis approaches that capture a system's different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposal's effectiveness was demonstrated by mechanistically simulating the metabolic output of Echerichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection.</p><p><strong>Availability: </strong>GreatMod https://qbioturin.github.io/epimod/, epimod_FBAfunctions https://github.com/qBioTurin/epimod_FBAfunctions, first case study E.coli  https://github.com/qBioTurin/Ec_coli_modelling, second case study C. difficile  https://github.com/qBioTurin/EpiCell_CDifficile.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PopGLen-A Snakemake pipeline for performing population genomic analyses using genotype likelihood-based methods.","authors":"Zachary J Nolen","doi":"10.1093/bioinformatics/btaf105","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf105","url":null,"abstract":"<p><strong>Summary: </strong>PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms.</p><p><strong>Availability and implementation: </strong>PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the supplementary material.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PROLONG: Penalized Regression for Outcome guided Longitudinal Omics analysis with Network and Group constraints.","authors":"Steve Broll, Sumanta Basu, Myung Hee Lee, Martin T Wells","doi":"10.1093/bioinformatics/btaf099","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf099","url":null,"abstract":"<p><strong>Motivation: </strong>There is a growing interest in longitudinal omics data paired with some longitudinal clinical outcome. Given a large set of continuous omics variables and some continuous clinical outcome, each measured for a few subjects at only a few time points, we seek to identify those variables that co-vary over time with the outcome. To motivate this problem we study a dataset with hundreds of urinary metabolites along with Tuberculosis mycobacterial load as our clinical outcome, with the objective of identifying potential biomarkers for disease progression. For such data clinicians usually apply simple linear mixed effects models which often lack power given the low number of replicates and time points. We propose a penalized regression approach on the first differences of the data that extends the lasso + Laplacian method (Li and Li 2008) to a longitudinal group lasso + Laplacian approach. Our method, PROLONG, leverages the first differences of the data to increase power by pairing the consecutive time points. The Laplacian penalty incorporates the dependence structure of the variables, and the group lasso penalty induces sparsity while grouping together all contemporaneous and lag terms for each omic variable in the model.</p><p><strong>Results: </strong>With an automated selection of model hyper-parameters, PROLONG correctly selects target metabolites with high specificity and sensitivity across a wide range of scenarios. PROLONG selects a set of metabolites from the real data that includes interesting targets identified during EDA.</p><p><strong>Availability: </strong>An R package implementing described methods called 'prolong'is available at https://github.com/stevebroll/prolong. Code snapshot available at 10.5281/zenodo.14804245.</p><p><strong>Conclusions: </strong>PROLONG is a powerful method for selecting potential biomarkers in high dimensional longitudinal omics data that co-vary with some continuous clinical outcome.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PathoSeq-QC: a decision support bioinformatics workflow for robust genomic surveillance.","authors":"G Leoni, M Petrillo, V Ruiz Serra, M Querci, S Coecke, T Wiesenthal","doi":"10.1093/bioinformatics/btaf102","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf102","url":null,"abstract":"<p><strong>Motivation: </strong>Recommendations on the use of genomics for pathogens surveillance are evidence that high-throughput genomic sequencing plays a key role to fight global health threats. Coupled with bioinformatics and other data types (e.g., epidemiological information), genomics is used to obtain knowledge on health pathogenic threats and insights on their evolution, to monitor pathogens spread, and to evaluate the effectiveness of countermeasures. From a decision-making policy perspective, it is essential to ensure the entire process's quality before relying on analysis results as evidence. Available workflows usually offer quality assessment tools that are primarily focused on the quality of raw NGS reads but often struggle to keep pace with new technologies and threats, and fail to provide a robust consensus on results, necessitating manual evaluation of multiple tool outputs.</p><p><strong>Results: </strong>We present PathoSeq-QC, a bioinformatics decision support workflow developed to improve the trustworthiness of genomic surveillance analyses and conclusions. Designed for SARS-CoV-2, it is suitable for any viral threat. In the specific case of SARS-CoV-2, PathoSeq-QC: i) evaluates the quality of the raw data; ii) assesses whether the analysed sample is composed by single or multiple lineages; iii) produces robust variant calling results via multi-tool comparison; iv) reports whether the produced data are in support of a recombinant virus, a novel or an already known lineage. The tool is modular, which will allow easy functionalities extension.</p><p><strong>Availability: </strong>PathoSeq-QC is a command-line tool written in Python and R. The code is available at https://code.europa.eu/dighealth/pathoseq-qc.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}