dorsal/arxiv
View SchemaPattern-based phylogenetic distance estimation and tree reconstruction
| Authors | Michael Höhl, Isidore Rigoutsos, Mark A. Ragan |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0605002 |
| URL | https://arxiv.org/abs/q-bio/0605002 |
Abstract
We have developed an alignment-free method that calculates phylogenetic distances using a maximum likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package decaf+py at http://www.bioinformatics.org.au), we have created a dataset of reference trees covering a wide range of phylogenetic distances. Amino acid sequences were evolved along the trees and input to the tested methods; from their calculated distances we infered trees whose topologies we compared to the reference trees. We find our pattern-based method statistically superior to all other tested alignment-free methods on this dataset. We also demonstrate the general advantage of alignment-free methods over an approach based on automated alignments when sequences violate the assumption of collinearity. Similarly, we compare methods on empirical data from an existing alignment benchmark set that we used to derive reference distances and trees. Our pattern-based approach yields distances that show a linear relationship to reference distances over a substantially longer range than other alignment-free methods. The pattern-based approach outperforms alignment-free methods and its phylogenetic accuracy is statistically indistinguishable from alignment-based distances.
{
"annotation_id": "c381e0ba-0f99-453e-819f-f3df14bc469c",
"date_created": "2026-03-02T18:01:34.901000Z",
"date_modified": "2026-03-02T18:01:34.901000Z",
"file_hash": "d73c28c5e0d92c2ff125751bda6dc5f2fceea16825010247502568028a6b1f2a",
"private": false,
"record": {
"abstract": "We have developed an alignment-free method that calculates phylogenetic\ndistances using a maximum likelihood approach for a model of sequence change on\npatterns that are discovered in unaligned sequences. To evaluate the\nphylogenetic accuracy of our method, and to conduct a comprehensive comparison\nof existing alignment-free methods (freely available as Python package decaf+py\nat http://www.bioinformatics.org.au), we have created a dataset of reference\ntrees covering a wide range of phylogenetic distances. Amino acid sequences\nwere evolved along the trees and input to the tested methods; from their\ncalculated distances we infered trees whose topologies we compared to the\nreference trees.\n We find our pattern-based method statistically superior to all other tested\nalignment-free methods on this dataset. We also demonstrate the general\nadvantage of alignment-free methods over an approach based on automated\nalignments when sequences violate the assumption of collinearity. Similarly, we\ncompare methods on empirical data from an existing alignment benchmark set that\nwe used to derive reference distances and trees. Our pattern-based approach\nyields distances that show a linear relationship to reference distances over a\nsubstantially longer range than other alignment-free methods. The pattern-based\napproach outperforms alignment-free methods and its phylogenetic accuracy is\nstatistically indistinguishable from alignment-based distances.",
"arxiv_id": "q-bio/0605002",
"authors": [
"Michael H\u00f6hl",
"Isidore Rigoutsos",
"Mark A. Ragan"
],
"categories": [
"q-bio.QM",
"q-bio.PE"
],
"title": "Pattern-based phylogenetic distance estimation and tree reconstruction",
"url": "https://arxiv.org/abs/q-bio/0605002"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "962723cb-2d25-40ac-9d80-1408df9d0dae",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}