Bioinformatics advances最新文献_第3页

diel_models: a python package for systematic integration of day-night cycles into plant genome-scale metabolic models. Diel_models：一个python包，用于将昼夜周期系统集成到植物基因组级代谢模型中。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-16 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf087

Luciana Martins, João Capela, Emanuel Cunha, Marta Sampaio, Oscar Dias

{"title":"diel_models: a python package for systematic integration of day-night cycles into plant genome-scale metabolic models.","authors":"Luciana Martins, João Capela, Emanuel Cunha, Marta Sampaio, Oscar Dias","doi":"10.1093/bioadv/vbaf087","DOIUrl":"10.1093/bioadv/vbaf087","url":null,"abstract":"Summary: In recent years, genome-scale metabolic models have become indispensable tools for studying complex metabolic processes occurring within living organisms. Understanding plants' metabolic behaviour under diel cycles (24-h day-night cycles) is essential to explain their adaptive strategies to different light conditions. However, integrating these cycles in plant GEMs is complex, laborious, time-consuming, and not systematized. Here, we present diel_models, a novel python package that enables the systematization and accurate construction of diel models based on non-diel plant GEMs, tailored for generic and multi-tissue models. diel_models is a lightweight, modular package with minimal dependencies and broad Python compatibility (v3.8+), making it easy to use, integrate into reconstruction pipelines, and extend with community-driven enhancements. It is also supported on all operating systems, including Windows, MacOS, and Linux, ensuring cross-platform compatibility for a wide range of users.Availability and implementation: The code is freely available at https://github.com/BioSystemsUM/diel_models.git and can be installed using the command pip install diel_models.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf087"},"PeriodicalIF":2.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036699","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}

引用次数: 0

Space: reconciling multiple spatial domain identification algorithms via consensus clustering. 空间：通过共识聚类协调多个空间域识别算法。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf084

Daoliang Zhang, Wenrui Li, Xinyi Sui, Na Yu, Shan Wang, Zhiping Liu, Xiaowo Wang, Zhiyuan Yuan, Rui Gao, Wei Zhang

{"title":"Space: reconciling multiple spatial domain identification algorithms via consensus clustering.","authors":"Daoliang Zhang, Wenrui Li, Xinyi Sui, Na Yu, Shan Wang, Zhiping Liu, Xiaowo Wang, Zhiyuan Yuan, Rui Gao, Wei Zhang","doi":"10.1093/bioadv/vbaf084","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf084","url":null,"abstract":"Motivation: The rapid development of spatially resolved transcriptomics (SRT) technologies has provided unprecedented opportunities for characterizing and understanding tissue architecture. As this field continues to advance, various methods have been developed to computationally identify spatial domains within tissues. However, the performance of different algorithms on the same dataset is not always consistent. This inconsistency makes it difficult for researchers to select the most reliable results for downstream analysis.Results: To address this challenge, we propose a domain identification method named Space. Space measures consistency between different methods to select reliable algorithms. It then constructs a consensus matrix to integrate the outputs from multiple algorithms. We introduce similarity loss, spatial loss, and low-rank loss in Space to enhance the accuracy and optimize computational efficiency. This strategy not only resolves the inconsistent issue of clustering labels among different methods but also achieves highly reliable clustering output. Flexible interfaces are also provided for downstream analysis such as visualization, domain-specific gene analysis and trajectory inference. Testing results on multiple publicly available SRT datasets demonstrate that Space performs exceptionally well in deciphering key tissue structures and biological features.Availability and implementation: The Space package can be easily installed through conda or mamba, and its source code is available at https://honchkrow.github.io/Space.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf084"},"PeriodicalIF":2.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12037102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043438","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}

引用次数: 0

Rodin: a streamlined metabolomics data analysis and visualization tool. Rodin：一个流线型代谢组学数据分析和可视化工具。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf088

Boris Minasenko, Dongxue Wang, Piera Cirillo, Nickilou Krigbaum, Barbara Cohn, Dean P Jones, Jeffrey M Collins, Xin Hu

{"title":"Rodin: a streamlined metabolomics data analysis and visualization tool.","authors":"Boris Minasenko, Dongxue Wang, Piera Cirillo, Nickilou Krigbaum, Barbara Cohn, Dean P Jones, Jeffrey M Collins, Xin Hu","doi":"10.1093/bioadv/vbaf088","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf088","url":null,"abstract":"Summary: Recent advances in high-resolution mass spectrometry have revolutionized metabolomics, enabling the profiling of hundreds of thousands of metabolic features in a single experiment, with widespread applications across health sciences. To streamline analysis of metabolomics data, we developed Rodin, a Python-based application offering fast, efficient processing of large datasets via a web interface or programming library. Rodin integrates multiple stages of analysis, including feature preprocessing, statistical testing, interactive visualizations, and pathway analysis, generating outputs while tracking user-defined parameters within a single page. By enhancing the accessibility of tools for metabolomics data analysis, Rodin not only streamlines the workflow but also enhances analytic throughput by enabling a broader range of users to perform these analyses. Compared to other tools, Rodin excels in user-friendliness, ease of access, and seamless integration of multiple functionalities, enabling reproducible, efficient workflows for users of all computational skill levels.Availability and implementation: Web interface-https://rodin-meta.com/. Python library-https://github.com/BM-Boris/rodin.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf088"},"PeriodicalIF":2.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054621","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}

引用次数: 0

Inferring differential protein binding from time-series chromatin accessibility data. 从时间序列染色质可及性数据推断差异蛋白结合。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-10 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf080

Sneha Mitra, Alexander J Hartemink

{"title":"Inferring differential protein binding from time-series chromatin accessibility data.","authors":"Sneha Mitra, Alexander J Hartemink","doi":"10.1093/bioadv/vbaf080","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf080","url":null,"abstract":"Motivation: Due to internal and external factors, the epigenomic landscape is constantly changing in ways that are linked to changes in gene expression. Chromatin accessibility data, such as MNase-seq, provide valuable insights into this landscape and have been used to compute chromatin occupancy profiles. Multiple datasets generated over time or under different conditions can thus be used to study dynamic changes in chromatin occupancy across the genome.Results: Our existing model, RoboCOP, computes a genome-wide chromatin occupancy profile for nucleosomes and hundreds of transcription factors. Here, we present a new method called DynaCOP that takes multiple chromatin occupancy profiles and uses them to generate a series of nucleosome-guided difference profiles. These profiles identify differentially binding transcription factors and reveal changes in nucleosome occupancy and positioning. We apply DynaCOP to chromatin occupancy profiles derived from deeply sequenced time-series MNase-seq data to study differential chromatin occupancy in the yeast genome under cadmium stress. We find strong correlations between the observed chromatin changes and changes in transcription.Availability and implementation: https://github.com/HarteminkLab/RoboCOP.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf080"},"PeriodicalIF":2.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12037103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144008364","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}

引用次数: 0

Benchmarking of germline copy number variant callers from whole genome sequencing data for clinical applications. 基于全基因组测序数据的种系拷贝数变异呼叫者对标研究及其临床应用。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-10 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf071

Francisco M De La Vega, Sean A Irvine, Pavana Anur, Kelly Potts, Lewis Kraft, Raul Torres, Peter Kang, Sean Truong, Yeonghun Lee, Shunhua Han, Vitor Onuchic, James Han

{"title":"Benchmarking of germline copy number variant callers from whole genome sequencing data for clinical applications.","authors":"Francisco M De La Vega, Sean A Irvine, Pavana Anur, Kelly Potts, Lewis Kraft, Raul Torres, Peter Kang, Sean Truong, Yeonghun Lee, Shunhua Han, Vitor Onuchic, James Han","doi":"10.1093/bioadv/vbaf071","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf071","url":null,"abstract":"Motivation: Whole-genome sequencing (WGS) is increasingly preferred for clinical applications due to its comprehensive coverage, effectiveness in detecting copy number variants (CNVs), and declining costs. However, systematic evaluations of WGS CNV callers tailored to germline clinical testing-where high sensitivity and confirmation of reported CNVs are essential-remain necessary. Clinical reporting typically emphasizes CNVs affecting coding regions over precise breakpoint detection. This study benchmarks several short-read WGS CNV detection tools using reference cell lines to inform their clinical use.Results: While tools vary in sensitivity (7%-83%) and precision (1%-76%), few meet the sensitivity needed for clinical testing. Callers generally perform better for deletions (up to 88% sensitivity) than duplications (up to 47% sensitivity), with poor detection of duplications under 5 kb. Notably, for CNVs in genes commonly included in clinical panels, significantly improved sensitivity and precision were observed when benchmarking against 25 cell lines with known CNVs. DRAGEN v4.2 high-sensitivity CNV calls, post-processed with custom filters, achieved 100% sensitivity and 77% precision on the optimized gene panel after excluding recurring artifacts. This level of performance may support clinical use with orthogonal confirmation of reportable CNVs, pending validation on laboratory-specific samples.Availability and implementation: The data underlying this article are available in the European Nucleo-tide Archive under project accession PRJEB87628.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf071"},"PeriodicalIF":2.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144031521","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}

引用次数: 0

Construction of edit-distance graphs for large sets of short reads through minimizer-bucketing. 通过minimizer-bucket构建大型短读集的编辑距离图。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-10 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf081

Pengyao Ping, Jinyan Li

{"title":"Construction of edit-distance graphs for large sets of short reads through minimizer-bucketing.","authors":"Pengyao Ping, Jinyan Li","doi":"10.1093/bioadv/vbaf081","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf081","url":null,"abstract":"Motivation: Pairs of short reads with small edit distances, along with their unique molecular identifier tags, have been exploited to correct sequencing errors in both reads and tags. However, brute-force identification of these pairs is impractical for large datasets containing ten million or more reads due to its quadratic complexity. Minimizer-bucketing and locality-sensitive hashing have been used to partition read sets into buckets of similar reads, allowing edit-distance calculations only within each bucket. However, challenges like minimizing missing pairs, optimizing bucketing parameters, and exploring combination bucketing to improve pair detection remain.Results: We define an edit-distance graph for a set of short reads, where nodes represent reads, and edges connect reads with small edit distances, and present a heuristic method, reads2graph, for high completeness of edge detection. Reads2graph uses three techniques: minimizer-bucketing, an improved Order-Min-Hash technique to divide large bins, and a novel graph neighbourhood multi-hop traversal within large bins to detect more edges. We then establish optimal bucketing settings to maximize ground truth edge coverage per bin. Extensive testing demonstrates that read2graph can achieve 97%-100% completeness in most cases, outperforming brute-force identification in speed while providing a superior speed-completeness balance compared to using a single bucketing method like Miniception or Order-Min-Hash.Availability and implementation: reads2graph is publicly available at https://github.com/JappyPing/reads2graph.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf081"},"PeriodicalIF":2.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12040381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057920","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}

引用次数: 0

Correction to: RecGOBD: accurate recognition of gene ontology related brain development protein functions through multi-feature fusion and attention mechanisms. RecGOBD：通过多特征融合和注意机制，准确识别与大脑发育相关的基因本体蛋白功能。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-09 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf065

引用次数: 0

"Neuropathological function estimations": a user-friendly module for analyzing neural activity in neurological disorders. “神经病理功能估计”：一个用户友好的模块，用于分析神经系统疾病中的神经活动。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-09 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf083

Alessia M Panait, Alex Kim, Rafael Rodriguez-Rojas, Lazaro M Sanchez-Rodriguez, Yasser Iturria-Medina

{"title":"\"Neuropathological function estimations\": a user-friendly module for analyzing neural activity in neurological disorders.","authors":"Alessia M Panait, Alex Kim, Rafael Rodriguez-Rojas, Lazaro M Sanchez-Rodriguez, Yasser Iturria-Medina","doi":"10.1093/bioadv/vbaf083","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf083","url":null,"abstract":"Motivation: This work introduces the Neuropathological Function Estimations software, designed to facilitate the study of neuronal activity alterations in neurological disorders without requiring programming expertise. With its user-friendly interface, researchers can input various data types to generate subject-specific functional brain models and decode neuropathological influences.Results: The software's capabilities are validated through its application to Alzheimer's disease, providing insights into neuronal excitability and disease mechanisms. This tool has the potential to enhance our understanding of the biological basis of in vivo neural activity and contribute to the development of personalized therapeutic interventions.Availability and implementation: The latest version of the software and support are freely available for noncommercial users through the Neuroinformatics for Personalized Medicine Lab (NeuroPM Lab) website at McGill University (https://www.neuropm-lab.com/neuropm-box.html). The software is maintained by the NeuroPM team. This publication is linked to version 1.0.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf083"},"PeriodicalIF":2.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036680","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}

引用次数: 0

VirDiG: a de novo transcriptome assembler for coronavirus. VirDiG：新冠病毒转录组组装器。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-08 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf075

Minghao Li, Xuaoyu Guo, Jin Zhao

{"title":"VirDiG: a de novo transcriptome assembler for coronavirus.","authors":"Minghao Li, Xuaoyu Guo, Jin Zhao","doi":"10.1093/bioadv/vbaf075","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf075","url":null,"abstract":"Motivation: The discontinuous transcription mechanism of coronaviruses contributes to their adaptation to different host environments and plays a critical role in their lifecycle. Accurate assembly of coronavirus transcripts is vital for understanding the virus's biological traits and developing precise prevention and treatment strategies. However, existing de novo assembly algorithms are primarily designed for alternative splicing events in eukaryotes and are not suitable for assembling coronavirus transcriptome, which consists of both genomic RNA and subgenomic mRNAs. Coronavirus transcriptome reconstruction from short reads remains a challenging problem.Results: In this work, we present VirDiG, a de novo transcriptome assembler specifically designed for coronaviruses. VirDiG utilizes a discontinuous graph to facilitate accurate transcript assembly by incorporating information from paired-end reads, sequence depth, and start and stop codons. Experimental results from both simulated and real datasets show that VirDiG exhibits significant advantages in reconstructing the transcriptome of coronaviruses when compared to traditional de novo assemblers tailored for classical eukaryotic transcriptome assembly.Availability and implementation: VirDiG is freely available at https://github.com/Limh616/VirDiG.git.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf075"},"PeriodicalIF":2.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028912","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}

引用次数: 0

TRACKing tandem repeats: a customizable pipeline for identification and cross-species comparison. 跟踪串联重复：一个可定制的管道识别和跨物种比较。

IF 2.4

Bioinformatics advances Pub Date : 2025-04-08 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf066

Carolina L Adam, Joana Rocha, Peter Sudmant, Rori Rohlfs

引用次数: 0