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The outputs of phylogenetic tools have historically been confined to fragmented, non-standard formats, creating significant barriers to knowledge integration. To resolve this, we developed [**treeio**](https://bioconductor.org/packages/treeio), which serves as the **universal infrastructure** for the field.
<strong>Format Interoperability:</strong> **treeio** resolved the "Format Fragmentation" problem by providing a robust parser for over 20 standard and non-standard formats. This enables the seamless exchange of evolutionary data across disparate software ecosystems and forms the basis for ESI-highly-cited research published in <em>Molecular Biology and Evolution</em>.
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## Pillar 2: The Grammar of Graphics for Evolution — Theoretical Leadership
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Before our work, tree visualization was largely restricted to topological display. We pioneered the application of the **Grammar of Graphics** to phylogenetics through [**ggtree**](https://bioconductor.org/packages/ggtree), decoupling evolutionary data from its visual representation.
<strong>Global Standards:</strong> **ggtree** has become the <em>de facto</em> global standard for tree annotation, cited in thousands of studies across high-impact journals. Recognized as a "representative work" for the 10th anniversary of <em>Methods in Ecology and Evolution</em>, it provides a high-level abstraction that allows for infinite extensibility in mapping omics data onto evolutionary histories.
The two methods introduced in 2018 have since evolved from specialized phylogenetic tools into universal visualization standards:
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<li><strong>Method 1 (Topological Mapping):</strong> Focused on mapping data directly onto tree structures, this paradigm evolved into [**ggtangle**](https://github.com/YuLab-SMU/ggtangle) for universal tidy-network visualization.</li>
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<li><strong>Method 2 (Coordinate Alignment):</strong> Focused on reconciling disparate data layers with tree topology, this logic provided the foundational architecture for [**aplot**](https://cran.r-project.org/package=aplot), the global standard for multi-layer plot alignment.</li>
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-**Method 1 (Topological Mapping):** Focused on mapping data directly onto tree structures, this paradigm evolved into [**ggtangle**](https://github.com/YuLab-SMU/ggtangle) for universal tidy-network visualization.
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-**Method 2 (Coordinate Alignment):** Focused on reconciling disparate data layers with tree topology, this logic provided the foundational architecture for [**aplot**](https://cran.r-project.org/package=aplot), the global standard for multi-layer plot alignment.
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<br>
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<strong>Expanding the Ecosystem:</strong> These principles were further extended to address specialized biological data types and relational structures:
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<li><strong>Molecular Context (**ggmsa**):</strong> Integrating sequence-level information is critical for understanding the molecular basis of evolution. [**ggmsa**](https://bioconductor.org/packages/ggmsa) provides a modular grammar for multiple sequence alignment (MSA) visualization, enabling the seamless overlay of structural and genomic conservation data onto phylogenetic trees.</li>
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<li><strong>Relational Flow (**ggflow**):</strong> Beyond static structures, biological evolution and research protocols involve directional transitions. [**ggflow**](https://github.com/YuLab-SMU/ggflow) introduces a grammar for visualizing flowcharts and process transitions, allowing researchers to document analytical workflows or evolutionary state-change paths within the same ecosystem.</li>
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<li><strong>Spatial & Layered Complexity ([**ggtreeExtra**](https://bioconductor.org/packages/ggtreeExtra) & [**ggtreeSpace**](https://github.com/YuLab-SMU/ggtreeSpace)):</strong> **ggtreeExtra** handles massive multi-omics layers in complex layouts, while **ggtreeSpace** explores the geometric mapping of evolutionary distances.</li>
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</ul>
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-**Molecular Context (ggmsa):** Integrating sequence-level information is critical for understanding the molecular basis of evolution. [**ggmsa**](https://bioconductor.org/packages/ggmsa) provides a modular grammar for multiple sequence alignment (MSA) visualization, enabling the seamless overlay of structural and genomic conservation data onto phylogenetic trees.
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-**Relational Flow (ggflow):** Beyond static structures, biological evolution and research protocols involve directional transitions. [**ggflow**](https://github.com/YuLab-SMU/ggflow) introduces a grammar for visualizing flowcharts and process transitions, allowing researchers to document analytical workflows or evolutionary state-change paths within the same ecosystem.
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-**Spatial & Layered Complexity (ggtreeExtra & ggtreeSpace):****ggtreeExtra** handles massive multi-omics layers in complex layouts, while **ggtreeSpace** explores the geometric mapping of evolutionary distances.
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<br>
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<strong>Programmable Reproducibility:</strong> Our work in <em>iMeta</em> (2022) established the **ggtree** object—a programmable structure that ensures analytical reproducibility by encapsulating trees, data, and visualization directives.
By establishing the **definitive global infrastructure** for tree-structured biological data, our work has moved the field beyond simple visualization into a new paradigm of **programmable knowledge synthesis**. Our contributions address the core challenges of data fragmentation and multi-scale integration, providing the rigorous analytical foundations (e.g., [**treeio**](/contribution-tree-data), [**ggtree**](/contribution-tree-data)) required for modern systems biology and evolutionary discovery across thousands of species.</a>
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By establishing the **definitive global infrastructure** for tree-structured biological data, our work has moved the field beyond simple visualization into a new paradigm of **programmable knowledge synthesis**. Our contributions address the core challenges of data fragmentation and multi-scale integration, providing the rigorous analytical foundations (e.g., **treeio**, **ggtree**) required for modern systems biology and evolutionary discovery across thousands of species.</a>
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Translating massive multi-omics datasets into biological intelligence requires a deep understanding of the functional architecture of life. Our team has established a comprehensive **Semantic Knowledge Mining Framework**—led by the global standard [**clusterProfiler**](/contribution-knowledge-mining)—that bridges the gap between raw data and actionable insights through knowledge quantification, pioneering comparative theme analysis, and the integration of spatial/epigenomic logic.
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Translating massive multi-omics datasets into biological intelligence requires a deep understanding of the functional architecture of life. Our team has established a comprehensive **Semantic Knowledge Mining Framework**—led by the global standard **clusterProfiler**—that bridges the gap between raw data and actionable insights through knowledge quantification, pioneering comparative theme analysis, and the integration of spatial/epigenomic logic.
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</a>
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Scientific discovery is increasingly driven by the ability to see the invisible within biological complexity. We have pioneered a **universal grammar for scientific visualization** that transcends specific coding systems, enabling researchers to synthesize relationships across genomic, molecular, and cellular scales through an integrated ecosystem of methodological and semantic tools.
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