Pub Date : 2024-10-23eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae162
Simon G Coetzee, Dennis J Hazelett
Motivation: motifbreakR scans genetic variants against position weight matrices of transcription factors (TFs) to determine the potential for the disruption of binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to query a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single-nucleotide variants on TF binding sites, in motifbreakR v2, we have updated the functionality.
Results: New features include the ability to query other types of complex genetic variants, such as short insertions and deletions. This capability allows modeling a more extensive array of variants that may have significant effects on TF binding. Additionally, predictions based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR can directly query the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.
Availability and implementation: motifbreakR is implemented in R. Source code, documentation, and tutorials are available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and GitHub at https://github.com/Simon-Coetzee/motifBreakR.
{"title":"<i>motifbreakR</i> v2: expanded variant analysis including indels and integrated evidence from transcription factor binding databases.","authors":"Simon G Coetzee, Dennis J Hazelett","doi":"10.1093/bioadv/vbae162","DOIUrl":"https://doi.org/10.1093/bioadv/vbae162","url":null,"abstract":"<p><strong>Motivation: </strong><i>motifbreakR</i> scans genetic variants against position weight matrices of transcription factors (TFs) to determine the potential for the disruption of binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to query a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single-nucleotide variants on TF binding sites, in <i>motifbreakR</i> v2, we have updated the functionality.</p><p><strong>Results: </strong>New features include the ability to query other types of complex genetic variants, such as short insertions and deletions. This capability allows modeling a more extensive array of variants that may have significant effects on TF binding. Additionally, predictions based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, <i>motifbreakR can directly query</i> the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in <i>motifbreakR</i>, in addition to the existing interface, we implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.</p><p><strong>Availability and implementation: </strong><i>motifbreakR</i> is implemented in R. Source code, documentation, and tutorials are available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and GitHub at https://github.com/Simon-Coetzee/motifBreakR.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549260","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}
Pub Date : 2024-10-22eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae152
Mariia Zelenskaia, Yazhini Arangasamy, Milot Mirdita, Johannes Söding, Venket Raghavan
Summary: The annotation of deeply sequenced, de novo assembled transcriptomes continues to be a challenge as some of the state-of-the-art tools are slow, difficult to install, and hard to use. We have tackled these issues with TransAnnot, a fast, automated transcriptome annotation pipeline that is easy to install and use. Leveraging the fast sequence searches provided by the MMseqs2 suite, TransAnnot offers one-step annotation of homologs from Swiss-Prot, gene ontology terms and orthogroups from eggNOG, and functional domains from Pfam. Users also have the option to annotate against custom databases. TransAnnot accepts sequencing reads (short and long), nucleotide sequences, or amino acid sequences as input for annotation. When benchmarked with test data sets of amino acid sequences, TransAnnot was 333, 284, and 18 times faster than comparable tools such as EnTAP, Trinotate, and eggNOG-mapper respectively.
Availability and implementation: TransAnnot is free to use, open sourced under GPLv3, and is implemented in C++ and Bash. Source code, documentation, and pre-compiled binaries are available at https://github.com/soedinglab/transannot. TransAnnot is also available via bioconda (https://anaconda.org/bioconda/transannot).
{"title":"TransAnnot-a fast transcriptome annotation pipeline.","authors":"Mariia Zelenskaia, Yazhini Arangasamy, Milot Mirdita, Johannes Söding, Venket Raghavan","doi":"10.1093/bioadv/vbae152","DOIUrl":"10.1093/bioadv/vbae152","url":null,"abstract":"<p><strong>Summary: </strong>The annotation of deeply sequenced, <i>de novo</i> assembled transcriptomes continues to be a challenge as some of the state-of-the-art tools are slow, difficult to install, and hard to use. We have tackled these issues with TransAnnot, a fast, automated transcriptome annotation pipeline that is easy to install and use. Leveraging the fast sequence searches provided by the MMseqs2 suite, TransAnnot offers one-step annotation of homologs from Swiss-Prot, gene ontology terms and orthogroups from eggNOG, and functional domains from Pfam. Users also have the option to annotate against custom databases. TransAnnot accepts sequencing reads (short and long), nucleotide sequences, or amino acid sequences as input for annotation. When benchmarked with test data sets of amino acid sequences, TransAnnot was 333, 284, and 18 times faster than comparable tools such as EnTAP, Trinotate, and eggNOG-mapper respectively.</p><p><strong>Availability and implementation: </strong>TransAnnot is free to use, open sourced under GPLv3, and is implemented in C++ and Bash. Source code, documentation, and pre-compiled binaries are available at https://github.com/soedinglab/transannot. TransAnnot is also available via bioconda (https://anaconda.org/bioconda/transannot).</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570211","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}
Pub Date : 2024-10-14eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae154
Dea Gogishvili, Emmanuel Minois-Genin, Jan van Eck, Sanne Abeln
Motivation: Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has shown to be a difficult task. Fine-tuning foundation models allows for adapting a model to the specific nuances of a new task using a much smaller dataset. Additionally, multitask deep learning offers a promising solution for addressing data gaps, simultaneously outperforming single-task methods.
Results: In this study, we harnessed a recently released leading large language model Evolutionary Scale Models (ESM-2). Efficient fine-tuning of ESM-2 was achieved by leveraging a recently developed parameter-efficient fine-tuning method. This approach enabled comprehensive training of model layers without excessive parameters and without the need to include a computationally expensive multiple sequence analysis. We explored several related tasks, at local (residue) and global (protein) levels, to improve the representation of the model. As a result, our model, PatchProt, cannot only predict hydrophobic patch areas but also outperforms existing methods at predicting primary tasks, including secondary structure and surface accessibility predictions. Importantly, our analysis shows that including related local tasks can improve predictions on more difficult global tasks. This research sets a new standard for sequence-based protein property prediction and highlights the remarkable potential of fine-tuning foundation models enriching the model representation by training over related tasks.
Availability and implementation: https://github.com/Deagogishvili/chapter-multi-task.
{"title":"PatchProt: hydrophobic patch prediction using protein foundation models.","authors":"Dea Gogishvili, Emmanuel Minois-Genin, Jan van Eck, Sanne Abeln","doi":"10.1093/bioadv/vbae154","DOIUrl":"10.1093/bioadv/vbae154","url":null,"abstract":"<p><strong>Motivation: </strong>Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patches from a protein sequence has shown to be a difficult task. Fine-tuning foundation models allows for adapting a model to the specific nuances of a new task using a much smaller dataset. Additionally, multitask deep learning offers a promising solution for addressing data gaps, simultaneously outperforming single-task methods.</p><p><strong>Results: </strong>In this study, we harnessed a recently released leading large language model Evolutionary Scale Models (ESM-2). Efficient fine-tuning of ESM-2 was achieved by leveraging a recently developed parameter-efficient fine-tuning method. This approach enabled comprehensive training of model layers without excessive parameters and without the need to include a computationally expensive multiple sequence analysis. We explored several related tasks, at local (residue) and global (protein) levels, to improve the representation of the model. As a result, our model, PatchProt, cannot only predict hydrophobic patch areas but also outperforms existing methods at predicting primary tasks, including secondary structure and surface accessibility predictions. Importantly, our analysis shows that including related local tasks can improve predictions on more difficult global tasks. This research sets a new standard for sequence-based protein property prediction and highlights the remarkable potential of fine-tuning foundation models enriching the model representation by training over related tasks.</p><p><strong>Availability and implementation: </strong>https://github.com/Deagogishvili/chapter-multi-task.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11525051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559614","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}
Pub Date : 2024-10-11eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae153
Greta Bellinzona, Davide Sassera, Alexandre M J J Bonvin
Motivation: Discovering new protein-protein interactions (PPIs) across entire proteomes offers vast potential for understanding novel protein functions and elucidate system properties within or between an organism. While recent advances in computational structural biology, particularly AlphaFold-Multimer, have facilitated this task, scaling for large-scale screenings remains a challenge, requiring significant computational resources.
Results: We evaluated the impact of reducing the number of models generated by AlphaFold-Multimer from five to one on the method's ability to distinguish true PPIs from false ones. Our evaluation was conducted on a dataset containing both intra- and inter-species PPIs, which included proteins from bacterial and eukaryotic sources. We demonstrate that reducing the sampling does not compromise the accuracy of the method, offering a faster, efficient, and environmentally friendly solution for PPI predictions.
Availability and implementation: The code used in this article is available at https://github.com/MIDIfactory/AlphaFastPPi. Note that the same can be achieved using the latest version of AlphaPulldown available at https://github.com/KosinskiLab/AlphaPulldown.
动机在整个蛋白质组中发现新的蛋白质-蛋白质相互作用(PPIs)为了解新的蛋白质功能和阐明生物体内或生物体之间的系统特性提供了巨大的潜力。虽然计算结构生物学(尤其是 AlphaFold-Multimer)的最新进展促进了这项任务的完成,但大规模筛选的扩展仍是一项挑战,需要大量的计算资源:我们评估了将 AlphaFold-Multimer 生成的模型数量从五个减少到一个对该方法区分真假 PPI 的能力的影响。我们的评估是在一个包含种内和种间 PPI 的数据集上进行的,其中包括来自细菌和真核生物的蛋白质。我们证明,减少采样并不会影响该方法的准确性,从而为 PPI 预测提供了一种更快、更高效、更环保的解决方案:本文使用的代码可从 https://github.com/MIDIfactory/AlphaFastPPi 网站获取。请注意,使用 https://github.com/KosinskiLab/AlphaPulldown 上最新版本的 AlphaPulldown 也能实现同样的效果。
{"title":"Accelerating protein-protein interaction screens with reduced AlphaFold-Multimer sampling.","authors":"Greta Bellinzona, Davide Sassera, Alexandre M J J Bonvin","doi":"10.1093/bioadv/vbae153","DOIUrl":"10.1093/bioadv/vbae153","url":null,"abstract":"<p><strong>Motivation: </strong>Discovering new protein-protein interactions (PPIs) across entire proteomes offers vast potential for understanding novel protein functions and elucidate system properties within or between an organism. While recent advances in computational structural biology, particularly AlphaFold-Multimer, have facilitated this task, scaling for large-scale screenings remains a challenge, requiring significant computational resources.</p><p><strong>Results: </strong>We evaluated the impact of reducing the number of models generated by AlphaFold-Multimer from five to one on the method's ability to distinguish true PPIs from false ones. Our evaluation was conducted on a dataset containing both intra- and inter-species PPIs, which included proteins from bacterial and eukaryotic sources. We demonstrate that reducing the sampling does not compromise the accuracy of the method, offering a faster, efficient, and environmentally friendly solution for PPI predictions.</p><p><strong>Availability and implementation: </strong>The code used in this article is available at https://github.com/MIDIfactory/AlphaFastPPi. Note that the same can be achieved using the latest version of AlphaPulldown available at https://github.com/KosinskiLab/AlphaPulldown.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11513016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513907","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}
Pub Date : 2024-10-08eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae150
Maria Tarradas-Alemany, Sandra Martínez-Puchol, Cristina Mejías-Molina, Marta Itarte, Marta Rusiñol, Sílvia Bofill-Mas, Josep F Abril
Summary: Target Enrichment Sequencing or Capture-based metagenomics has emerged as an approach of interest for viral metagenomics in complex samples. However, these datasets are usually analyzed with standard downstream Bioinformatics analyses. CAPTVRED (Capture-based metagenomics Analysis Pipeline for tracking ViRal species from Environmental Datasets), has been designed to assess the virome present in complex samples, specially focused on those obtained by Target Enrichment Sequencing approach. This work aims to provide a user-friendly tool that complements this sequencing approach for the total or partial virome description, especially from environmental matrices. It includes a setup module which allows preparation and adjustment of the pipeline to any capture panel directed to a set of species of interest. The tool also aims to reduce time and computational cost, as well as to provide comprehensive, reproducible, and accessible results while being easy to costume, set up, and install.
Availability and implementation: Source code and test datasets are freely available at github repository: https://github.com/CompGenLabUB/CAPTVRED.git.
{"title":"CAPTVRED: an automated pipeline for viral tracking and discovery from capture-based metagenomics samples.","authors":"Maria Tarradas-Alemany, Sandra Martínez-Puchol, Cristina Mejías-Molina, Marta Itarte, Marta Rusiñol, Sílvia Bofill-Mas, Josep F Abril","doi":"10.1093/bioadv/vbae150","DOIUrl":"https://doi.org/10.1093/bioadv/vbae150","url":null,"abstract":"<p><strong>Summary: </strong>Target Enrichment Sequencing or Capture-based metagenomics has emerged as an approach of interest for viral metagenomics in complex samples. However, these datasets are usually analyzed with standard downstream Bioinformatics analyses. CAPTVRED (<i>Capture-based metagenomics Analysis Pipeline for tracking ViRal species from Environmental Datasets</i>), has been designed to assess the virome present in complex samples, specially focused on those obtained by Target Enrichment Sequencing approach. This work aims to provide a user-friendly tool that complements this sequencing approach for the total or partial virome description, especially from environmental matrices. It includes a setup module which allows preparation and adjustment of the pipeline to any capture panel directed to a set of species of interest. The tool also aims to reduce time and computational cost, as well as to provide comprehensive, reproducible, and accessible results while being easy to costume, set up, and install.</p><p><strong>Availability and implementation: </strong>Source code and test datasets are freely available at github repository: https://github.com/CompGenLabUB/CAPTVRED.git.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11495672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513908","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}
Pub Date : 2024-10-07eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae145
Masato Tsutsui, Mariko Okada
Summary: Signaling dynamics encode important features and regulatory mechanisms of biological systems, and recent studies have reported the use of simulated signaling dynamics with mechanistic modeling as biomarkers for human diseases. Given the success of deep learning techniques, it is expected that they can extract informative patterns from simulation results more effectively than traditional approaches involving manual feature selection, which can be used for subsequent analyses, such as patient stratification and survival prediction. Here, we propose DynProfiler, which utilizes the entire signaling dynamics, including intermediate variables, as input and leverages deep learning techniques to extract informative features without requiring any labels. Furthermore, DynProfiler incorporates a modern explainable AI solution to provide quantitative time-dependent importance scores for each dynamics. Using simulated dynamics of patients with breast cancer as an example, we demonstrate DynProfiler's ability to extract high-quality features that can predict mortality risk and identify important dynamics, highlighting upregulated phosphorylated GSK3β as a biomarker for poor prognosis. Overall, this tool can be useful for clinical application, as well as for elucidating biological system dynamics.
Availability and implementation: The DynProfiler Python library is available in GitHub at https://github.com/okadalabipr/DynProfiler.
{"title":"DynProfiler: a Python package for comprehensive analysis and interpretation of signaling dynamics leveraged by deep learning techniques.","authors":"Masato Tsutsui, Mariko Okada","doi":"10.1093/bioadv/vbae145","DOIUrl":"10.1093/bioadv/vbae145","url":null,"abstract":"<p><strong>Summary: </strong>Signaling dynamics encode important features and regulatory mechanisms of biological systems, and recent studies have reported the use of simulated signaling dynamics with mechanistic modeling as biomarkers for human diseases. Given the success of deep learning techniques, it is expected that they can extract informative patterns from simulation results more effectively than traditional approaches involving manual feature selection, which can be used for subsequent analyses, such as patient stratification and survival prediction. Here, we propose DynProfiler, which utilizes the entire signaling dynamics, including intermediate variables, as input and leverages deep learning techniques to extract informative features without requiring any labels. Furthermore, DynProfiler incorporates a modern explainable AI solution to provide quantitative time-dependent importance scores for each dynamics. Using simulated dynamics of patients with breast cancer as an example, we demonstrate DynProfiler's ability to extract high-quality features that can predict mortality risk and identify important dynamics, highlighting upregulated phosphorylated GSK3β as a biomarker for poor prognosis. Overall, this tool can be useful for clinical application, as well as for elucidating biological system dynamics.</p><p><strong>Availability and implementation: </strong>The DynProfiler Python library is available in GitHub at https://github.com/okadalabipr/DynProfiler.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11464416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402170","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}
Pub Date : 2024-10-04eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae140
[This corrects the article DOI: 10.1093/bioadv/vbae125.].
[此处更正了文章 DOI:10.1093/bioadv/vbae125]。
{"title":"Correction to: Utilizing biological experimental data and molecular dynamics for the classification of mutational hotspots through machine learning.","authors":"","doi":"10.1093/bioadv/vbae140","DOIUrl":"https://doi.org/10.1093/bioadv/vbae140","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/bioadv/vbae125.].</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11453097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382608","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}
Pub Date : 2024-10-03eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae148
Veronica Paparozzi, Christine Nardini
Summary: We present tidysbml, an R package able to perform compartments, species, and reactions data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as igraph, RCy3, and biomaRt. Exemplar data (i.e. SBML files) are extracted from Reactome.
Availability and implementation: The tidysbml R package is distributed under CC BY 4.0 License and can be found publicly available in Bioconductor (https://bioconductor.org/packages/tidysbml) and on GitHub (https://github.com/veronicapaparozzi/tidysbml).
摘要:我们介绍的 tidysbml 是一个 R 软件包,它能够以表格数据结构(即 R 数据框)从系统生物学标记语言(SBML)文档(最高 3 级)中提取区系、物种和反应数据,从而轻松访问和处理丰富的生物信息。得益于其输出格式,该软件包方便了数据操作,可通过 igraph、RCy3 和 biomaRt 等 R 软件包管理自定义网络的构建和分析,以及数据检索。 示例数据(即 SBML 文件)从 Reactome.Availability 和实现中提取:tidysbml R 软件包以 CC BY 4.0 许可发布,可在 Bioconductor (https://bioconductor.org/packages/tidysbml) 和 GitHub (https://github.com/veronicapaparozzi/tidysbml) 上公开获取。
{"title":"tidysbml: R/Bioconductor package for SBML extraction into dataframes.","authors":"Veronica Paparozzi, Christine Nardini","doi":"10.1093/bioadv/vbae148","DOIUrl":"https://doi.org/10.1093/bioadv/vbae148","url":null,"abstract":"<p><strong>Summary: </strong>We present <i>tidysbml</i>, an R package able to perform <i>compartments</i>, <i>species</i>, and <i>reactions</i> data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as <i>igraph</i>, <i>RCy3</i>, and <i>biomaRt</i>. Exemplar data (i.e. SBML files) are extracted from Reactome.</p><p><strong>Availability and implementation: </strong>The <i>tidysbml</i> R package is distributed under CC BY 4.0 License and can be found publicly available in Bioconductor (https://bioconductor.org/packages/tidysbml) and on GitHub (https://github.com/veronicapaparozzi/tidysbml).</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11479578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482213","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}
Pub Date : 2024-10-03eCollection Date: 2024-01-01DOI: 10.1093/bioadv/vbae147
Rodolfo S Allendes Osorio, Yuji Kosugi, Johan T Nyström-Persson, Kenji Mizuguchi, Yayoi Natsume-Kitatani
Summary: To address the challenges of the storage, sharing, and analysis of multi-omics data, here we introduce the newest version of Panomicon, which includes the improvement of the underlying data model, the introduction of new registration and control access service, together with the seamless integration with other services (like TargetMine for data enrichment analysis), integrated in a completely new, more user friendly web application.
Availability and implementation: Panomicon is available online at https://panomicon.nibiohn.go.jp. Unregistered users can access the publicly available data uploaded to Panomicon using the following account: user: guest, password: anonymous. Source code for the application is also freely available under a GNU license at https://github.com/Toxygates/Panomicon/. A brief user guide for the new features of Panomicon is provided as supplementary material online.
{"title":"A modern multi-omics data exploration experience with Panomicon.","authors":"Rodolfo S Allendes Osorio, Yuji Kosugi, Johan T Nyström-Persson, Kenji Mizuguchi, Yayoi Natsume-Kitatani","doi":"10.1093/bioadv/vbae147","DOIUrl":"https://doi.org/10.1093/bioadv/vbae147","url":null,"abstract":"<p><strong>Summary: </strong>To address the challenges of the storage, sharing, and analysis of multi-omics data, here we introduce the newest version of Panomicon, which includes the improvement of the underlying data model, the introduction of new registration and control access service, together with the seamless integration with other services (like TargetMine for data enrichment analysis), integrated in a completely new, more user friendly web application.</p><p><strong>Availability and implementation: </strong>Panomicon is available online at https://panomicon.nibiohn.go.jp. Unregistered users can access the publicly available data uploaded to Panomicon using the following account: user: guest, password: anonymous. Source code for the application is also freely available under a GNU license at https://github.com/Toxygates/Panomicon/. A brief user guide for the new features of Panomicon is provided as supplementary material online.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549261","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}
Motivation: Visualization and analysis of biological networks play crucial roles in understanding living systems. Biological networks include diverse types, from gene regulatory networks and protein-protein interactions to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. However, the analysis of these diverse omics types is challenging due to the diversity of databases and the complexity of network analysis.
Results: We developed iTraNet, a web application that visualizes and analyses trans-omics networks involving four types of networks: gene regulatory networks, protein-protein interactions, metabolic networks, and metabolite exchange networks. Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas in ob/ob mice, this tendency disappeared. With its ability to facilitate network analysis, we anticipate that iTraNet will help researchers gain insights into living systems.
Availability and implementation: iTraNet is available at https://itranet.streamlit.app/.
{"title":"iTraNet: a web-based platform for integrated trans-omics network visualization and analysis.","authors":"Hikaru Sugimoto, Keigo Morita, Dongzi Li, Yunfan Bai, Matthias Mattanovich, Shinya Kuroda","doi":"10.1093/bioadv/vbae141","DOIUrl":"https://doi.org/10.1093/bioadv/vbae141","url":null,"abstract":"<p><strong>Motivation: </strong>Visualization and analysis of biological networks play crucial roles in understanding living systems. Biological networks include diverse types, from gene regulatory networks and protein-protein interactions to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. However, the analysis of these diverse omics types is challenging due to the diversity of databases and the complexity of network analysis.</p><p><strong>Results: </strong>We developed iTraNet, a web application that visualizes and analyses trans-omics networks involving four types of networks: gene regulatory networks, protein-protein interactions, metabolic networks, and metabolite exchange networks. Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas in <i>ob/ob</i> mice, this tendency disappeared. With its ability to facilitate network analysis, we anticipate that iTraNet will help researchers gain insights into living systems.</p><p><strong>Availability and implementation: </strong>iTraNet is available at https://itranet.streamlit.app/.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11493990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513909","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}