Bioinformatics (Oxford, England)最新文献

{"title":"Exact model-free function inference using uniform marginal counts for null population.","authors":"Yiyi Li, Mingzhou Song","doi":"10.1093/bioinformatics/btaf121","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf121","url":null,"abstract":"<p><strong>Motivation: </strong>Recognizing cause-effect relationships is a fundamental inquiry in science. However, current causal inference methods often focus on directionality but not statistical significance. A ramification is chance patterns of uneven marginal distributions achieving a perfect directionality score.</p><p><strong>Results: </strong>To overcome such issues, we design the uniform exact function test with continuity correction (UEFTC) to detect functional dependency between two discrete random variables. The null hypothesis is two variables being statistically independent. Unique from related tests whose null populations use observed marginals, we define the null population by an embedded uniform square. We also present a fast algorithm to accomplish the test. On datasets with ground truth, the UEFTC exhibits accurate directionality, low biases, and robust statistical behavior over alternatives. We found non-monotonic response by gene TCB2 to beta-estradiol dosage in engineered yeast strains. In the human duodenum with environmental enteric dysfunction, we discovered pathology-dependent anti-co-methylated CpG sites in the vicinity of genes POU2AF1 and LSP1; such activity represents orchestrated methylation and demethylation along the same gene, unreported previously. The UEFTC has much improved effectiveness in exact model-free function inference for data-driven knowledge discovery.</p><p><strong>Availability: </strong>An open-source R package 'UniExactFunTest' implementing the presented uniform exact function tests is available via CRAN at doi : 10.32614/CRAN.package.UniExactFunTest. Code for reproducing figures can be found in supplementary file 'UEFTC-main.zip'.</p><p><strong>Supplementary information: </strong>Supplementary Materials are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671940","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":"NanoASV: a snakemake workflow for reproducible field-based Nanopore full length 16S Metabarcoding amplicon data analysis.","authors":"Arthur Cousson, Frédéric Mahé, Ulysse Guyet, Damase Razafimahafaly, Laetitia Bernard","doi":"10.1093/bioinformatics/btaf089","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf089","url":null,"abstract":"<p><strong>Summary: </strong>NanoASV is a conda environment and snakemake-based workflow using state of the art bioinformatics software to process full-length SSU rRNA (16S/18S) amplicons acquired with Oxford Nanopore Sequencing technology. Its strength lies in reproducibility, portability and the possibility to run offline, allowing in-field analysis. It can be installed on the Nanopore MK1C sequencing device and process data locally.</p><p><strong>Availability: </strong>Source code and documentation are freely available at https://github.com/ImagoXV/NanoASV - Zenodo archive https://doi.org/10.5281/zenodo.14730742.</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":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671943","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":"MAFin: Motif Detection in Multiple Alignment Files.","authors":"Michail Patsakis, Kimonas Provatas, Fotis A Baltoumas, Nikol Chantzi, Ioannis Mouratidis, Georgios A Pavlopoulos, Ilias Georgakopoulos-Soares","doi":"10.1093/bioinformatics/btaf125","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf125","url":null,"abstract":"<p><strong>Motivation: </strong>Whole Genome and Proteome Alignments, represented by the Multiple Alignment File (MAF) format, have become a standard approach in comparative genomics and proteomics. These often require identifying conserved motifs, which is crucial for understanding functional and evolutionary relationships. However, current approaches lack a direct method for motif detection within MAF files. We present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files to address this gap, streamlining genomic and proteomic research.</p><p><strong>Results: </strong>We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: 1) using user-specified k-mers to search the sequences. 2) with regular expressions, in which case one or more patterns are searched, and 3) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enables the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses.</p><p><strong>Availability: </strong>MAFin is offered as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin.</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":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665691","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":"A Framework for Analyzing EEG Data Using High-Dimensional Tests.","authors":"Qiuyan Zhang, Wenjing Xiang, Bo Yang, Hu Yang","doi":"10.1093/bioinformatics/btaf109","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf109","url":null,"abstract":"<p><strong>Motivation: </strong>The objective of EEG data analysis is to extract meaningful insights, enhancing our understanding of brain function. However, the high dimensionality and temporal dependency of EEG data present significant challenges to the effective application of statistical methods. This study systematically addresses these challenges by introducing a high-dimensional statistical framework that includes testing changes in the mean vector and precision matrix, as well as conducting relevant analyses. Specifically, the Ridgelized Hotelling's T2 test (RIHT) is introduced to test changes in the mean vector of EEG data over time while relaxing traditional distributional and moment assumptions. Secondly, a multiple population de-biased estimation and testing method (MPDe) is developed to estimate and simultaneously test differences in the precision matrix before and after stimulation. This approach extends the joint Gaussian graphical model to multiple populations while incorporating the temporal dependency of EEG data. Meanwhile, a novel data-driven fine-tuning method is applied to automatically search for optimal hyperparameters.</p><p><strong>Results: </strong>Through comprehensive simulation studies and applications, we have obtained substantial evidence to validate that the RIHT has relatively high power, and it can test for changes when the distribution is unknown. Similarly, the MPDe can infer the precision matrix under time-dependent conditions. Additionally, the conducted analysis of channel selection and dominant channel can identify significant channels which play a crucial role in human cognitive ability, such as PO3, PO4, Pz, P4, P8, FT7 and FT8. All findings confirm that the proposed methods outperform existing ones, demonstrating the effectiveness of the framework in EEG data analysis.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660124","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":"Lit-OTAR Framework for Extracting Biological Evidences from Literature.","authors":"Santosh Tirunagari, Shyamasree Saha, Aravind Venkatesan, Daniel Suveges, Miguel Carmona, Annalisa Buniello, David Ochoa, Johanna McEntyre, Ellen McDonagh, Melissa Harrison","doi":"10.1093/bioinformatics/btaf113","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf113","url":null,"abstract":"<p><strong>Summary: </strong>The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionise drug discovery by extracting evidence from scientific literature for drug target identification and validation. This novel framework combines Named Entity Recognition (NER) for identifying gene/protein (target), disease, organism, and chemical/drug within scientific texts, and entity normalisation to map these entities to databases like Ensembl, Experimental Factor Ontology (EFO), and ChEMBL. Continuously operational, it has processed over 39 million abstracts and 4.5 million full-text articles and preprints to date, identifying more than 48.5 million unique associations that significantly help accelerate the drug discovery process and scientific research >29.9 m distinct target-disease, 11.8 m distinct target-drug, and 8.3 m distinct disease-drug relationships).</p><p><strong>Availability and implementation: </strong>The results are accessible through Europe PMC's SciLite web app (https://europepmc.org/) and its annotations API (https://europepmc.org/annotationsapi), as well as via the Open Targets Platform (https://platform.opentargets.org/). The daily pipeline is available at https://github.com/ML4LitS/otar-maintenance, and the Open Targets ETL processes are available at https://github.com/opentargets.</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":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652564","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":"nf-core/pacvar: a pipeline for analyzing longread PacBio whole genome and repeat expansion sequencing data.","authors":"Tanya Jain, Claire Clelland","doi":"10.1093/bioinformatics/btaf116","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf116","url":null,"abstract":"<p><strong>Motivation: </strong>Pacific Biosciences (PacBio) single molecule, long-read sequencing enables whole genome annotation and the characterization of 20 complex repetitive repeat regions especially relevant to neurodegenerative diseases through their PureTarget panel. Long-read whole genome sequencing (WGS) also allows for the detection of structural variants that would be difficult to detect with traditional short-read sequencing. However, the raw unaligned Binary Alignment Map (BAM) data needs to be processed before analysis. There is a need for an intuitive comprehensive bioinformatic pipeline that can analyze this data.</p><p><strong>Results: </strong>We present nf-core/pacvar, a comprehensive pipeline for analyzing both PacBio single-molecule PureTarget and WGS data that demultiplexes and parallelizes pre-processing, variant calling and repeat characterization. nf-core/pacvar is compatible with little configuration and has few dependencies. This pipeline enables rapid end-to-end, parallel processing of PacBio single-molecule whole genome and targeted repeat expansion sequencing.</p><p><strong>Availability: </strong>nf-core/pacvar is available on nf-core website (https://nf-co.re/pacvar/) and on github (https://github.com/nf-core/pacvar) under MIT License (DOI 10.5281/zenodo.14813048).</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652566","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":"mastR: An R Ppackage for Automated Identification of Tissue-Specific Gene Signatures in Multi-Group Differential Expression Analysis.","authors":"Jinjin Chen, Ahmed Mohamed, Dharmesh D Bhuva, Melissa J Davis, Chin Wee Tan","doi":"10.1093/bioinformatics/btaf114","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf114","url":null,"abstract":"<p><strong>Motivation: </strong>Biomarker discovery is important and offers insight into potential underlying mechanisms of disease. While existing biomarker identification methods primarily focus on single cell RNA sequencing (scRNA-seq) data, there remains a need for automated methods designed for labeled bulk RNA-seq data from sorted cell populations or experiments. Current methods require curation of results or statistical thresholds and may not account for tissue background expression. Here we bridge these limitations with an automated marker identification method for labeled bulk RNA-seq data that explicitly considers background expressions.</p><p><strong>Results: </strong>We developed mastR, a novel tool for accurate marker identification using transcriptomic data. It leverages robust statistical pipelines like edgeR and limma to perform pairwise comparisons between groups, and aggregates results using rank-product-based permutation test. A signal-to-noise ratio approach is implemented to minimize background signals. We assessed the performance of mastR-derived NK cell signatures against published curated signatures and found that the mastR-derived signature performs as well, if not better than the published signatures. We further demonstrated the utility of mastR on simulated scRNA-seq data and in comparison with Seurat in terms of marker selection performance.</p><p><strong>Availability: </strong>mastR is freely available from https://bioconductor.org/packages/release/bioc/html/mastR.html. A vignette and guide are available at https://davislaboratory.github.io/mastR. All statistical analyses were carried out using R (version ≥ 4.3.0) and Bioconductor (version ≥3.17).</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":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652565","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":"H2GnnDTI: hierarchical heterogeneous graph neural networks for drug target interaction prediction.","authors":"Yueying Jing, Dongxue Zhang, Limin Li","doi":"10.1093/bioinformatics/btaf117","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf117","url":null,"abstract":"<p><strong>Motivation: </strong>Identifying drug target interactions is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying drug target interactions necessitate computational tools for automated prediction and comprehension of drug target interactions. Despite recent advancements, current methods fail to fully leverage the hierarchical information in drug target interactions.</p><p><strong>Results: </strong>Here we introduce H2GnnDTI, a novel two-level hierarchical heterogeneous graph learning model to predict drug target interactions, by integrating the structures of drugs and proteins via a low-level view GNN (LGNN) and a high-level view GNN (HGNN). The hierarchical graph consists of high-level heterogeneous nodes representing drugs and proteins, connected by edges representing known DTIs. Each drug or protein node is further detailed in a low-level graph, where nodes represent molecules within each drug or amino acids within each protein, accompanied by their respective chemical descriptors. Two distinct low-level graph neural networks are first deployed to capture structural and chemical features specific to drugs and proteins from these low-level graphs. Subsequently, a high-level graph encoder is employed to comprehensively capture and merge interactive features pertaining to drugs and proteins from the high-level graph. The high-level encoder incorporates a structure and attribute information fusion module designed to explicitly integrate representations acquired from both a feature encoder and a graph encoder, facilitating consensus representation learning. Extensive experiments conducted on three benchmark datasets have shown that our proposed H2GnnDTI model consistently outperforms state-of-the-art deep learning methods.</p><p><strong>Availability and implementation: </strong>The codes are freely available at https://github.com/LiminLi-xjtu/H2GnnDTI.</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":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652563","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":"Generating Multiple Alignments on a Pangenomic Scale.","authors":"Jannik Olbrich, Thomas Büchler, Enno Ohlebusch","doi":"10.1093/bioinformatics/btaf104","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf104","url":null,"abstract":"<p><strong>Motivation: </strong>Since novel long read sequencing technologies allow for de novo assembly of many individuals of a species, high-quality assemblies are becoming widely available. For example, the recently published draft human pangenome reference was based on assemblies composed of contigs. There is an urgent need for a software-tool that is able to generate a multiple alignment of genomes of the same species because current multiple sequence alignment programs cannot deal with such a volume of data.</p><p><strong>Results: </strong>We show that the combination of a well-known anchor-based method with the technique of prefix-free parsing yields an approach that is able to generate multiple alignments on a pangenomic scale, provided that large-scale structural variants are rare. Furthermore, experiments with real world data show that our software tool PANAMA (PANgenomic Anchor-based Multiple Alignment) significantly outperforms current state-of-the art programs.</p><p><strong>Availability: </strong>Source code is available at: https://gitlab.com/qwerzuiop/panama, archived at swh  :  1: dir: e90c9f664995acca9063245cabdd97549cf39694.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652559","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":"VisionMol: A Novel Virtual Reality Tool for Protein Molecular Structure Visualization and Manipulation.","authors":"Xin Wang, Yicheng Zhuang, Wenrui Liang, Haoyang Wen, Zhencong Cai, Yujia He, Yuxi Su, Wei Qin, Yuanzhe Cai, Lixin Liang, Bingding Huang","doi":"10.1093/bioinformatics/btaf118","DOIUrl":"https://doi.org/10.1093/bioinformatics/btaf118","url":null,"abstract":"<p><strong>Motivation: </strong>Virtual reality technology holds significant potential for applications in biomedicine, particularly in the visualization and manipulation of protein molecular structures. To facilitate the study of protein molecules and enable the state-of-the-art VR hardware, we developed a novel VR software named VisionMol, which allows users to engage in immersive exploration and analysis of three-dimensional molecular structures using a range of virtual reality platforms (such as Rhino X Pro, Meta's Oculus Quest Pro/3) as well as personal computers.</p><p><strong>Results: </strong>Built on the Unity engine and programmed using C#, VisionMol incorporates custom scripts to enable a variety of molecular operations. Users can rotate, scale, and translate molecular models using gestures, controllers, or other input devices. Furthermore, VisionMol offers rich visualization and interactive features, including multi-model molecular display, distance measurement between molecular components, and molecular alignment and docking.</p><p><strong>Summary: </strong>These capabilities facilitate a more intuitive understanding of molecular interactions and chemical properties. The real-time interactive effects and clear visual representations allow users to delve deeper into the relationships between molecular structures and their properties, thereby accelerating research progress and promoting scientific discovery. We believe that this VR-based protein molecule analysis has significant application value in several fields, including biomedicine, life science education, drug design and optimization, biotechnology, and engineering applications.</p><p><strong>Availability: </strong>The code is at https://github.com/WangLabforComputationalBiology/VisionMol. The v1.1 code (for Oculus Quest) could also be found at https://doi.org/10.5281/zenodo.14705790. The v1.0 code (for Rhino X Pro) could also be found at https://doi.org/10.5281/zenodo.14865216. Detailed documentation could be found at https://visionmol.surge.sh/#/en-us/README.</p><p><strong>Supplementary information: </strong>Supplementary are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652580","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}