Evidence map›Paper›PMID 35758818›Full record

ArticleBioinformatics (Oxford, England)2022

QuCo: quartet-based co-estimation of species trees and gene trees.

Maryam Rabiee, Siavash Mirarab

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Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

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2 authors.

Maryam RabieeDepartment of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
Siavash MirarabDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA.ORCID 0000-0001-5410-1518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationPhylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.

resultsWe introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees. AVAILABILITY AND IMPLEMENTATION: QuCo is available on https://github.com/maryamrabiee/quco. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Genetic SpeciationModels, GeneticAnimalsLikelihood FunctionsPhylogeny

Identifiers

PMID35758818
PMCPMC9235488

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.