Bioinformatics advances最新文献_第9页

The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training. ISCB能力框架v. 3：修订和扩展的生物信息学教育和培训标准。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-18 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae166

Cath Brooksbank, Michelle D Brazas, Nicola Mulder, Russell Schwartz, Verena Ras, Sarah L Morgan, Marta Lloret Llinares, Patricia Carvajal López, Lee Larcombe, Amel Ghouila, Tom Hancocks, Venkata Satagopam, Javier De Las Rivas, Gaston Mazandu, Bruno Gaeta

{"title":"The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training.","authors":"Cath Brooksbank, Michelle D Brazas, Nicola Mulder, Russell Schwartz, Verena Ras, Sarah L Morgan, Marta Lloret Llinares, Patricia Carvajal López, Lee Larcombe, Amel Ghouila, Tom Hancocks, Venkata Satagopam, Javier De Las Rivas, Gaston Mazandu, Bruno Gaeta","doi":"10.1093/bioadv/vbae166","DOIUrl":"10.1093/bioadv/vbae166","url":null,"abstract":"Motivation: Developing competency in the broad area of bioinformatics is challenging globally, owing to the breadth of the field and the diversity of its audiences for education and training. Course design can be facilitated by the use of a competency framework-a set of competency requirements that define the knowledge, skills and attitudes needed by individuals in (or aspiring to be in) a particular profession or role. These competency requirements can help to define curricula as they can inform both the content and level to which competency needs to be developed. The International Society for Computational Biology (ISCB) developed a list of bioinformatics competencies in 2014, and these have undergone several rounds of improvement. In consultation with a broad bioinformatics training community, these have now been further refined and extended to include knowledge skills and attitudes, and mappings to previous and other existing competency frameworks.Results: Here, we present version 3 of the ISCB competency framework. We describe how it was developed and how to access it, as well as providing some examples of how it has been used.Availability and implementation: The framework is openly accessible at https://competency.ebi.ac.uk/framework/iscb/3.0/competencies.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae166"},"PeriodicalIF":2.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11646570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831057","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

Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models: a methodical affirmation.

IF 2.4

Bioinformatics advances Pub Date : 2024-11-18 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbae181

Maytha Alshammari, Jing He, Willy Wriggers

{"title":"Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models: a methodical affirmation.","authors":"Maytha Alshammari, Jing He, Willy Wriggers","doi":"10.1093/bioadv/vbae181","DOIUrl":"10.1093/bioadv/vbae181","url":null,"abstract":"Motivation: This study investigates the flexible refinement of AlphaFold2 models against corresponding cryo-electron microscopy (cryo-EM) maps using normal modes derived from elastic network models (ENMs) as basis functions for displacement. AlphaFold2 generally predicts highly accurate structures, but 18 of the 137 models of isolated chains exhibit a TM-score below 0.80. We achieved a significant improvement in four of these deviating structures and used them to systematically optimize the parameters of the ENM motion model.Results: We successfully refined four AlphaFold2 models with notable discrepancies: lipid-preserved respiratory supercomplex (TM-score increased from 0.52 to 0.69), flagellar L-ring protein (TM-score increased from 0.53 to 0.64), cation diffusion facilitator YiiP (TM-score increased from 0.76 to 0.83), and Sulfolobus islandicus pilus (TM-score increased from 0.77 to 0.85). We explored the effect of three different mode ranges (modes 1-9, 7-9, and 1-12), masked or box-cropped density maps, numerical optimization methods, and two similarity measures (Pearson correlation and inner product). The best results were achieved for the widest mode range (modes 1-12), masked maps, inner product, and local Powell optimization. These optimal parameters were implemented in the flexible fitting utility elforge.py in version 1.4 of our Python-based ModeHunter package.Availability and implementation: https://modehunter.biomachina.org.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbae181"},"PeriodicalIF":2.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082478","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

MeTEor: an R Shiny app for exploring longitudinal metabolomics data. MeTEor：一个用于探索纵向代谢组学数据的R Shiny应用程序。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-14 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae178

Gordon Grabert, Daniel Dehncke, Tushar More, Markus List, Anke R M Kraft, Markus Cornberg, Karsten Hiller, Tim Kacprowski

{"title":"MeTEor: an R Shiny app for exploring longitudinal metabolomics data.","authors":"Gordon Grabert, Daniel Dehncke, Tushar More, Markus List, Anke R M Kraft, Markus Cornberg, Karsten Hiller, Tim Kacprowski","doi":"10.1093/bioadv/vbae178","DOIUrl":"10.1093/bioadv/vbae178","url":null,"abstract":"Motivation: The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization.Results: To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients.Availability and implementation: MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae178"},"PeriodicalIF":2.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808674","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

MultiOmicsIntegrator: a nextflow pipeline for integrated omics analyses. MultiOmicsIntegrator：一个用于综合 omics 分析的 nextflow 管道。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-14 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae175

Bianka Alexandra Pasat, Eleftherios Pilalis, Katarzyna Mnich, Afshin Samali, Aristotelis Chatziioannou, Adrienne M Gorman

{"title":"MultiOmicsIntegrator: a nextflow pipeline for integrated omics analyses.","authors":"Bianka Alexandra Pasat, Eleftherios Pilalis, Katarzyna Mnich, Afshin Samali, Aristotelis Chatziioannou, Adrienne M Gorman","doi":"10.1093/bioadv/vbae175","DOIUrl":"10.1093/bioadv/vbae175","url":null,"abstract":"Motivation: Analysis of gene and isoform expression levels is becoming critical for the detailed understanding of biochemical mechanisms. In addition, integrating RNA-seq data with other omics data types, such as proteomics and metabolomics, provides a strong approach for consolidating our understanding of biological processes across various organizational tiers, thus promoting the identification of potential therapeutic targets.Results: We present our pipeline, called MultiOmicsIntegrator (MOI), an inclusive pipeline for comprehensive omics analyses. MOI represents a unified approach that performs in-depth individual analyses of diverse omics. Specifically, exhaustive analysis of RNA-seq data at the level of genes, isoforms of genes, as well as miRNA is offered, coupled with functional annotation and structure prediction of these transcripts. Additionally, proteomics and metabolomics data are supported providing a holistic view of biological systems. Finally, MOI has tools to integrate simultaneously multiple and diverse omics datasets, with both data- and function-driven approaches, fostering a deeper understanding of intricate biological interactions.Availability and implementation: MOI and ReadTheDocs.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae175"},"PeriodicalIF":2.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576358/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683388","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

mxfda: a comprehensive toolkit for functional data analysis of single-cell spatial data. mxfda：用于单细胞空间数据功能数据分析的综合工具包。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-13 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae155

Julia Wrobel, Alex C Soupir, Mitchell T Hayes, Lauren C Peres, Thao Vu, Andrew Leroux, Brooke L Fridley

引用次数: 0

Conditional flux balance analysis toolbox for python: application to research metabolism in cyclic environments. python 条件通量平衡分析工具箱：循环环境中新陈代谢研究的应用。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-13 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae174

Timothy Páez-Watson, Ricardo Hernández Medina, Loek Vellekoop, Mark C M van Loosdrecht, S Aljoscha Wahl

引用次数: 0

Introducing GWAStic: a user-friendly, cross-platform solution for genome-wide association studies and genomic prediction. 介绍 GWAStic：全基因组关联研究和基因组预测的用户友好型跨平台解决方案。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-12 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae177

Stefanie Lück, Uwe Scholz, Dimitar Douchkov

{"title":"Introducing GWAStic: a user-friendly, cross-platform solution for genome-wide association studies and genomic prediction.","authors":"Stefanie Lück, Uwe Scholz, Dimitar Douchkov","doi":"10.1093/bioadv/vbae177","DOIUrl":"10.1093/bioadv/vbae177","url":null,"abstract":"Motivation: Advances in genomics have created an insistent need for accessible tools that simplify complex genetic data analysis, enabling researchers across fields to harness the power of genome-wide association studies and genomic prediction. GWAStic was developed to bridge this gap, providing an intuitive platform that combines artificial intelligence with traditional statistical methods, making sophisticated genomic analysis accessible without requiring deep expertise in statistical software.Results: We present GWAStic, an intuitive, cross-platform desktop application designed to streamline genome-wide association studies and genomic prediction for biological and medical researchers. With a user-friendly graphical interface, GWAStic integrates machine learning and traditional statistical approaches to support genetic analysis. The application accepts inputs from standard text-based Variant Call Formats and PLINK binary files, generating clear graphical outputs, including Manhattan plots, quantile-quantile plots, and genomic prediction correlation plots to enhance data visualization and analysis.Availability and implementation: Project page: https://github.com/snowformatics/gwastic_desktop; GWAStic documentation: https://snowformatics.gitbook.io/product-docs; PyPI: https://pypi.org/project/gwastic-desktop/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae177"},"PeriodicalIF":2.4,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11643344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142831010","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

LUKB: preparing local UK Biobank data for analysis. LUKB：准备用于分析的英国生物库本地数据。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-09 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae176

Xiangnan Li, Yaqi Huang, Shuming Wang, Meng Hao, Yi Li, Hui Zhang, Zixin Hu

引用次数: 0

Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics. 以系统发育为基础的图深度学习对传染病流行中的动态传播集群进行分类。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-07 eCollection Date: 2024-01-01 DOI: 10.1093/bioadv/vbae158

Chaoyue Sun, Yanjun Li, Simone Marini, Alberto Riva, Dapeng Oliver Wu, Ruogu Fang, Marco Salemi, Brittany Rife Magalis

{"title":"Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics.","authors":"Chaoyue Sun, Yanjun Li, Simone Marini, Alberto Riva, Dapeng Oliver Wu, Ruogu Fang, Marco Salemi, Brittany Rife Magalis","doi":"10.1093/bioadv/vbae158","DOIUrl":"https://doi.org/10.1093/bioadv/vbae158","url":null,"abstract":"Motivation: In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g. infectivity) or host (e.g. vaccination), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population.Results: We evaluated the limitation of existing tree shape metrics when dealing with dynamic transmission clusters and propose instead a phylogeny-based deep learning system -DeepDynaTree- for dynamic classification. Comprehensive experiments carried out on a variety of simulated epidemic growth models and HIV epidemic data indicate that this graph deep learning approach is effective, robust, and informative for cluster dynamic prediction. Our results confirm that DeepDynaTree is a promising tool for transmission cluster characterization that can be modified to address the existing limitations and deficiencies in knowledge regarding the dynamics of transmission trajectories for groups at risk of pathogen infection.Availability and implementation: DeepDynaTree is available under an MIT Licence in https://github.com/salemilab/DeepDynaTree.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"4 1","pages":"vbae158"},"PeriodicalIF":2.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633757","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

MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism. MitoMAMMAL：哺乳动物线粒体的基因组尺度模型预测心脏和BAT代谢。

IF 2.4

Bioinformatics advances Pub Date : 2024-11-05 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbae172

Stephen Chapman, Theo Brunet, Arnaud Mourier, Bianca H Habermann

{"title":"MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism.","authors":"Stephen Chapman, Theo Brunet, Arnaud Mourier, Bianca H Habermann","doi":"10.1093/bioadv/vbae172","DOIUrl":"https://doi.org/10.1093/bioadv/vbae172","url":null,"abstract":"Motivation: Mitochondria are essential for cellular metabolism and are inherently flexible to allow correct function in a wide range of tissues. Consequently, dysregulated mitochondrial metabolism affects different tissues in different ways leading to challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is useful in studying tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism in research, no mouse specific mitochondrial metabolic model is currently available.Results: Building upon the similarity between human and mouse mitochondrial metabolism, we present mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is able to model mouse and human mitochondrial metabolism. To demonstrate this, using an adapted E-Flux algorithm, we integrated proteomic data from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue, as well as transcriptomic data from in vitro differentiated human brown adipocytes and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research in cardiac disease and diabetes in both mouse and human contexts.Availability and implementation: The MitoMammal Jupyter Notebook is available at: https://gitlab.com/habermann_lab/mitomammal.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbae172"},"PeriodicalIF":2.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11696703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933933","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