MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta‐omics and bioinformatics
The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data...
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| Published in: | iMeta Vol. 4; no. 1; pp. e70002 - n/a |
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| Main Authors: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Australia
John Wiley & Sons, Inc
01.02.2025
John Wiley and Sons Inc Wiley |
| Subjects: | |
| ISSN: | 2770-596X, 2770-5986, 2770-596X |
| Online Access: | Get full text |
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| Abstract | The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi‐omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R‐based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.
MicrobiomeStatPlots is a comprehensive platform for microbiome data analysis and visualization, offering bioinformatics tips, integrating multi‐omics pipelines, and featuring 82 visualization and interpretation cases. The platform allows users to customize the code and encourages contributions to expand its content, with plans for future updates. This platform is available at https://github.com/YongxinLiu/MicrobiomeStatPlot.
Highlights
Comprehensive visualization resources: MicrobiomeStatPlots provides over 80 reproducible visualization cases and integrates multi‐omics analysis pipelines to facilitate the analysis and interpretation of microbiome data.
Customizable and user‐friendly: The platform includes detailed tutorials, example datasets, and R‐based plotting codes, enabling researchers to tailor visualizations to their needs.
Collaborative and open‐source: MicrobiomeStatPlots encourages community contributions to expand its functionality and supports the continuous development of microbiome research. It's accessible for use and contribution at https://github.com/YongxinLiu/MicrobiomeStatPlot. |
|---|---|
| AbstractList | The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi‐omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R‐based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.
MicrobiomeStatPlots is a comprehensive platform for microbiome data analysis and visualization, offering bioinformatics tips, integrating multi‐omics pipelines, and featuring 82 visualization and interpretation cases. The platform allows users to customize the code and encourages contributions to expand its content, with plans for future updates. This platform is available at https://github.com/YongxinLiu/MicrobiomeStatPlot.
Highlights
Comprehensive visualization resources: MicrobiomeStatPlots provides over 80 reproducible visualization cases and integrates multi‐omics analysis pipelines to facilitate the analysis and interpretation of microbiome data.
Customizable and user‐friendly: The platform includes detailed tutorials, example datasets, and R‐based plotting codes, enabling researchers to tailor visualizations to their needs.
Collaborative and open‐source: MicrobiomeStatPlots encourages community contributions to expand its functionality and supports the continuous development of microbiome research. It's accessible for use and contribution at https://github.com/YongxinLiu/MicrobiomeStatPlot. The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research. The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi-omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R-based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research. The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi‐omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R‐based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub ( https://github.com/YongxinLiu/MicrobiomeStatPlot ). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research. Comprehensive visualization resources: MicrobiomeStatPlots provides over 80 reproducible visualization cases and integrates multi‐omics analysis pipelines to facilitate the analysis and interpretation of microbiome data. Customizable and user‐friendly: The platform includes detailed tutorials, example datasets, and R‐based plotting codes, enabling researchers to tailor visualizations to their needs. Collaborative and open‐source: MicrobiomeStatPlots encourages community contributions to expand its functionality and supports the continuous development of microbiome research. It's accessible for use and contribution at https://github.com/YongxinLiu/MicrobiomeStatPlot . Abstract The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi‐omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R‐based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research. The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi‐omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R‐based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research. MicrobiomeStatPlots is a comprehensive platform for microbiome data analysis and visualization, offering bioinformatics tips, integrating multi‐omics pipelines, and featuring 82 visualization and interpretation cases. The platform allows users to customize the code and encourages contributions to expand its content, with plans for future updates. This platform is available at https://github.com/YongxinLiu/MicrobiomeStatPlot. Comprehensive visualization resources: MicrobiomeStatPlots provides over 80 reproducible visualization cases and integrates multi‐omics analysis pipelines to facilitate the analysis and interpretation of microbiome data.Customizable and user‐friendly: The platform includes detailed tutorials, example datasets, and R‐based plotting codes, enabling researchers to tailor visualizations to their needs.Collaborative and open‐source: MicrobiomeStatPlots encourages community contributions to expand its functionality and supports the continuous development of microbiome research. It's accessible for use and contribution at https://github.com/YongxinLiu/MicrobiomeStatPlot. |
| Author | Xun, Jiani Xu, Shanshan Luo, Hao Gao, Yunyun Bai, Defeng Zhu, Zhihao Yang, Haifei Liu, Yong‐Xin Wang, Yao Gai, Anran Peng, Kai Ma, Chuang Lyu, Hujie Yousuf, Salsabeel |
| AuthorAffiliation | 2 School of Horticulture Anhui Agricultural University Hefei China 1 Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China 9 School of Ecology and Nature Conservation Beijing Forestry University Beijing China 5 Zhanjiang Key Laboratory of Human Microecology and Clinical Translation Research, the Marine Biomedical Research Institute, College of Basic Medicine Guangdong Medical University Zhanjiang China 6 School of Agricultural Sciences Zhengzhou University Zhengzhou China 7 Jiangsu Co‐Innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, College of Veterinary Medicine Yangzhou University Yangzhou China 8 School of Food and Biological Engineering Hefei University of Technology Hefei China 4 Department of Food Science and Nutrition The Hong Kong Polytechnic University Hong Kong SAR China 3 College of Life Sciences Qingdao Agricult |
| AuthorAffiliation_xml | – name: 8 School of Food and Biological Engineering Hefei University of Technology Hefei China – name: 2 School of Horticulture Anhui Agricultural University Hefei China – name: 1 Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China – name: 7 Jiangsu Co‐Innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, College of Veterinary Medicine Yangzhou University Yangzhou China – name: 9 School of Ecology and Nature Conservation Beijing Forestry University Beijing China – name: 3 College of Life Sciences Qingdao Agricultural University Qingdao China – name: 5 Zhanjiang Key Laboratory of Human Microecology and Clinical Translation Research, the Marine Biomedical Research Institute, College of Basic Medicine Guangdong Medical University Zhanjiang China – name: 6 School of Agricultural Sciences Zhengzhou University Zhengzhou China – name: 4 Department of Food Science and Nutrition The Hong Kong Polytechnic University Hong Kong SAR China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40027478$$D View this record in MEDLINE/PubMed |
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| Copyright | 2025 The Author(s). published by John Wiley & Sons Australia, Ltd on behalf of Science. 2025 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science. 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | visualization multi‐omics interpretation microbiome bioinformatics |
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| License | Attribution 2025 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
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| Notes | Defeng Bai, Chuang Ma, and Jiani Xun contributed equally to this study. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| Snippet | The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To... The rapid growth of microbiome research has generated an unprecedented amount of multi-omics data, presenting challenges in data analysis and visualization. To... Abstract The rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and... |
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| StartPage | e70002 |
| SubjectTerms | Analysis Bioinformatics Biomarkers Codes Data analysis interpretation Linux microbiome Microbiota multi‐omics Researchers Software upgrading Statistical analysis Visualization |
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| Title | MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta‐omics and bioinformatics |
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| Volume | 4 |
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