From individual analyses to global repositories, the challenge is no longer building trees, but connecting them.
1. From Individual Trees to Shared Knowledge
In the early days of molecular phylogenetics, every research group built their own tree, published it as a figure, and moved on. The underlying alignments and models were rarely shared.
Reproducibility was limited to retyping data from printed tables.
Fast forward two decades: genome-scale datasets, massive alignments, and cloud storage have transformed this landscape. Today, we can store thousands of phylogenies, gene trees, and species trees in public databases accessible to anyone.
In principle, this means that every published tree can become part of a global, continually updated Tree of Life.
But as with everything in phylogenomics, the reality is more complex. Different databases serve different goals — from storing individual alignments to assembling comprehensive synthetic trees. Some emphasize reproducibility and metadata; others focus on integration across studies.
In this post, we’ll explore the main databases that host genome-wide phylogenies, what makes them powerful, and what still holds them back.
2. The Ecosystem of Phylogenetic Databases
Phylogenetic data don’t live in a single place. Instead, they exist across a distributed ecosystem of databases, each with its own focus:
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TreeBASE – The oldest and most general repository of phylogenetic trees and alignments.
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Open Tree of Life (OTL) – A massive synthetic tree integrating published phylogenies.
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PhylomeDB – A database of complete genome-wide phylogenies (“phylomes”) for selected species.
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Ensembl Compara – A curated collection of gene and species trees across model organisms.
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NCBI Taxonomy & Genome Tree – A standardized, genome-based reference phylogeny for all sequenced species.
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TimeTree – A resource for divergence times across the tree of life.
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Other specialized resources – e.g., OrthoDB, EggNOG, and Open Tree’s TNRS.
Let’s look at each in turn — what they store, how they’re built, and what they’re best suited for.
3. TreeBASE: The Foundational Repository
Overview
TreeBASE (https://treebase.org) is the oldest dedicated phylogenetic database, established in the 1990s with the goal of making phylogenetic data reproducible and reusable.
Researchers can deposit alignments, tree files (Newick, Nexus), and metadata linked to publications.
What It Offers
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Stores both gene-level and species-level trees.
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Provides searchable metadata (taxa, loci, authors, DOI).
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Supports downloading of alignments and trees in standardized formats.
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Integrates with the Open Tree of Life project.
Strengths
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Longest history and broadest scope — thousands of published studies.
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Format standardization using NEXUS and NeXML.
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Encourages transparency and data sharing.
Limitations
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Inconsistent metadata: many old entries lack details on models, partitions, or software versions.
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Not all datasets are genome-wide — many are from pre-genomic era single-locus studies.
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Submission process can be cumbersome, discouraging full participation.
Best For
Archiving and retrieving published phylogenies with underlying alignments — ideal for meta-analyses, benchmarking, or teaching.
4. Open Tree of Life (OTL): The Global Synthesis
Overview
The Open Tree of Life (https://tree.opentreeoflife.org) is an ambitious project to synthesize all published phylogenetic information into a single, comprehensive tree of life.
It combines data from TreeBASE, Dryad, and published trees, reconciled with a unified taxonomy (the Open Tree Taxonomy, OTT).
How It Works
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Integrates thousands of source trees using a graph-based approach.
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Resolves conflicts among trees using a consensus algorithm and manual curation.
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Provides APIs for programmatic access and data downloads.
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Offers tools like the Taxonomic Name Resolution Service (TNRS) for consistent naming.
Strengths
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Scale: includes over 2.4 million taxa.
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Openness: fully open-source, API-accessible, and community-driven.
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Reproducibility: each node can be traced back to its supporting studies.
Limitations
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The synthetic tree is not built directly from genomic data — it integrates published topologies, which may differ in quality.
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Gene-level detail is lost in the synthesis process.
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Rapidly changing taxonomies can cause name inconsistencies.
Best For
Macro-level visualization and integration — a bird’s-eye view of evolutionary relationships across all life, with links back to supporting literature.
5. PhylomeDB: Phylogenomics Done Right
Overview
PhylomeDB (http://phylomedb.org) takes a genome-centered approach. For each species (“seed genome”), it builds a complete collection of gene phylogenies, covering every protein-coding gene and its homologs across other species. Each collection is called a phylome.
Pipeline
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Homology search (BLAST/Smith–Waterman).
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Multiple sequence alignment (MUSCLE, MAFFT).
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Alignment trimming.
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Phylogenetic inference (PhyML or IQ-TREE).
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Orthology and paralogy assignment using tree reconciliation.
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Storage of gene trees, alignments, and orthology predictions.
Strengths
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Fully automated and standardized.
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Provides gene-level and species-level trees.
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Includes duplication, loss, and orthology information.
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Cross-linked to Ensembl and UniProt.
Limitations
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Limited to selected species groups (e.g., vertebrates, fungi, plants).
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High computational demands mean updates lag behind the latest genomes.
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Some gene families remain unresolved due to complex duplication histories.
Best For
Gene family evolution, orthology detection, and comparative genomics using pre-computed, quality-controlled trees.
6. Ensembl Compara: Orthology Meets Reference Genomics
Overview
Ensembl Compara (https://ensembl.org/info/genome/compara) is part of the Ensembl genome browser ecosystem, maintained by EMBL-EBI. It provides phylogenetic trees and orthology predictions across model organisms and high-quality reference genomes.
Pipeline
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Orthology and paralogy assignments via GeneTrees pipeline (Ensembl’s internal workflow).
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Phylogenetic inference using TreeBeST and later IQ-TREE/RAxML for accuracy.
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Integration with genome annotations and synteny information.
Strengths
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Seamlessly linked with genome browsers — you can visualize gene trees directly within Ensembl.
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Updated with each Ensembl release.
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Provides confidence scores for orthology/paralogy calls.
Limitations
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Focused on well-annotated genomes (mostly vertebrates, some plants and fungi).
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Does not include non-model or draft assemblies.
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Less emphasis on conflict visualization or tree uncertainty.
Best For
Model organism comparative genomics, gene evolution analysis, and functional annotation studies.
7. NCBI Taxonomy and Genome Tree: The Official Reference
Overview
The NCBI Taxonomy is not merely a naming system — it’s the backbone of nearly all biological databases.
Recently, NCBI has expanded it with a Genome Tree (https://www.ncbi.nlm.nih.gov/genome/tree), built from universal single-copy marker genes across thousands of genomes.
How It Works
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Marker selection (usually ~120 bacterial or ~50 archaeal proteins).
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Multiple sequence alignment and concatenation.
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Tree inference using maximum likelihood.
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Regular updates as new genomes are added.
Strengths
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Standardized and curated — used as the reference tree in GenBank, RefSeq, and GTDB.
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Scalable and automated.
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Provides backbone for taxonomic classification.
Limitations
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Limited marker set — not a full phylogenomic reconstruction.
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Conservative in scope — includes only reference-quality assemblies.
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Tree topology sometimes constrained by existing taxonomy.
Best For
Reference-level taxonomic placement and integration with other NCBI resources (BLAST, GenBank, RefSeq).
8. TimeTree: The Chronology of Life
Overview
TimeTree (http://www.timetree.org) focuses on one key dimension of evolution — time.
It compiles published divergence time estimates across taxa and provides consensus estimates and confidence intervals.
Features
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Query any pair of species to get estimated divergence times and references.
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Visualize timetrees with geological context.
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Download time-calibrated phylogenies for use in comparative analyses.
Strengths
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User-friendly, web-based, and well-cited.
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Aggregates published molecular clock studies.
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Emphasizes temporal calibration and uncertainty.
Limitations
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Based on heterogeneous sources (different methods, models, fossil calibrations).
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Not a primary phylogenetic inference database.
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May not reflect the most recent genomic data.
Best For
Evolutionary timing, macroevolutionary rate studies, and educational visualization.
9. Other Notable Databases
| Database | Focus | Highlights |
|---|---|---|
| OrthoDB | Hierarchical catalog of orthologs | Integrates genome data across taxa, useful for comparative genomics |
| EggNOG | Orthology and functional annotation | Combines phylogenetic orthology with GO and KEGG annotation |
| PANTHER | Gene families and pathways | Links evolutionary relationships with biological function |
| GTDB (Genome Taxonomy Database) | Genome-based bacterial and archaeal taxonomy | Replaces traditional taxonomy with standardized, tree-based classification |
Each of these fills a niche — from orthology to taxonomy — and together they illustrate how phylogenetic data now permeate every level of genomics infrastructure.
10. The Challenges of Phylogenetic Data Integration
Despite this rich ecosystem, integrating genome-wide phylogenies remains difficult. The main barriers are not technological but conceptual.
a. Heterogeneity of Input Data
Different studies use different:
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Gene sets (mitochondrial vs. nuclear vs. transcriptomic)
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Taxon sampling
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Models and partitioning schemes
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Tree-building algorithms
This makes direct comparison or synthesis non-trivial. Two trees may share taxa but differ in structure simply because they use different data sources or model assumptions.
b. Inconsistent Metadata and Standards
Metadata about alignments, models, software versions, and parameters are often incomplete or inconsistent.
Efforts like MIAPA (Minimum Information About a Phylogenetic Analysis) aim to standardize this, but adoption remains limited.
c. Taxonomic Instability
Species names and identifiers change frequently.
Without consistent identifiers (like NCBI TaxIDs or OTT IDs), cross-database integration can fail silently.
d. Provenance and Reproducibility
Even when trees are available, reconstructing exactly how they were generated can be impossible without the full pipeline.
Linking phylogenies to their computational workflows (e.g., via workflow managers or containers) is an emerging best practice.
11. The Advantages of Centralized Phylogenomic Resources
Despite these challenges, the benefits of shared phylogenetic databases are profound:
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Reproducibility: Others can rerun analyses or test alternative models.
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Data reuse: Large-scale meta-analyses become feasible — e.g., studying gene family evolution across hundreds of studies.
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Interoperability: Databases like OTL and Ensembl link phylogenies directly to functional and ecological data.
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Education and outreach: Publicly accessible trees foster understanding of biodiversity and evolution.
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Cross-validation: Independent datasets can be compared, revealing robust patterns or inconsistencies.
The future of phylogenetics depends on making trees as discoverable and reusable as genome sequences themselves.
12. Emerging Directions: Linked, FAIR, and Living Trees
a. FAIR Principles
Modern phylogenetic databases increasingly adhere to FAIR principles — data should be Findable, Accessible, Interoperable, and Reusable.
This means persistent identifiers (DOIs, ORCIDs), open APIs, machine-readable formats, and linked metadata.
b. Linked Data and Knowledge Graphs
Efforts are underway to represent phylogenetic information as linked data, connecting taxa, genes, publications, and traits through semantic web technologies.
Projects like the OpenBioLink initiative and OToL Graph Store are early examples.
c. Continuous Integration and Versioning
Future databases may adopt version-controlled phylogenies, where trees are updated automatically as new genomes are added — similar to how software evolves.
Imagine a “GitHub for Trees” where each update documents what changed and why.
d. Cloud-Connected Analysis
Instead of downloading data, researchers will increasingly run analyses directly within cloud databases, using standardized APIs and scalable compute resources.
13. The Big Picture: Toward a Living Tree of Life
The ultimate goal of these efforts is not just to store phylogenies, but to create a dynamic, living Tree of Life — one that updates continuously as new genomes are sequenced and analyses are improved.
Imagine:
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Clicking any node to see which genes support it, what models were used, and how confident we are.
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Tracing a gene’s evolutionary path across thousands of species, annotated with function, structure, and expression.
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Watching the tree evolve as new species are added and outdated analyses are replaced.
Such a system would transform evolutionary biology into a real-time, data-integrated science, bridging genomics, ecology, and paleontology.
14. Conclusion: Sharing Trees, Sharing Understanding
Genome-wide phylogenies are not just outputs — they’re data assets in their own right.
Storing them properly, annotating them thoroughly, and linking them transparently are now essential for the progress of the field.
Databases like TreeBASE, Open Tree of Life, PhylomeDB, Ensembl Compara, and others each contribute to this vision from different angles.
Together, they move us toward a more unified, open, and interconnected view of life’s history.
But this vision depends on us — the researchers — to share, annotate, and curate our trees responsibly.
After all, every tree we publish today is a branch on a much larger structure — the ever-growing, ever-updating Tree of Life.
Next Up:
In the final post — “The Road Ahead: What the Next Decade Holds for Phylogenomics” — we’ll look at the frontiers shaping the future: the Earth BioGenome Project, AI-assisted phylogenetics, reticulate evolution, and what it means to build a living, evolving understanding of life itself.
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