Evidence map›Paper›PMID 39115958›Full record

ArticleGigaScience2024

Efficient phylogenetic tree inference for massive taxonomic datasets: harnessing the power of a server to analyze 1 million taxa.

César Piñeiro, Juan C Pichel

Abstract read
In one paragraph

Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  3. Species identification and genotyping ofMicrobiology spectrum · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

César PiñeiroInformation Retrieval Lab, CITIC, Universidade da Coruña, A Coruña 15008, Spain.ORCID 0000-0001-6490-7128
Juan C PichelCiTIUS, Universidade de Santiago de Compostela, Santiago de Compostela 15782, Spain.ORCID 0000-0001-9505-6493

Funding

European Regional Development FundMinisterio de Ciencia e Innovación PLEC2021-007662
6 · The paper itself

Abstract

backgroundPhylogenies play a crucial role in biological research. Unfortunately, the search for the optimal phylogenetic tree incurs significant computational costs, and most of the existing state-of-the-art tools cannot deal with extremely large datasets in reasonable times.

resultsIn this work, we introduce the new VeryFastTree code (version 4.0), which is able to construct a tree on 1 server using single-precision arithmetic from a massive 1 million alignment dataset in only 36 hours, which is 3 times and 3.2 times faster than its previous version and FastTree-2, respectively. This new version further boosts performance by parallelizing all tree traversal operations during the tree construction process, including subtree pruning and regrafting moves. Additionally, it introduces significant new features such as support for new and compressed file formats, enhanced compatibility across a broader range of operating systems, and the integration of disk computing functionality. The latter feature is particularly advantageous for users without access to high-end servers, as it allows them to manage very large datasets, albeit with an increase in computing time.

conclusionsExperimental results establish VeryFastTree as the fastest tool in the state-of-the-art for maximum likelihood phylogeny estimation. It is publicly available at https://github.com/citiususc/veryfasttree. In addition, VeryFastTree is included as a package in Bioconda, MacPorts, and all Debian-based Linux distributions.

Indexed as

PhylogenySoftwareAlgorithmsClassificationComputational BiologyDatabases, Geneticparallelismperformancephylogeneticsvery large datasets

Identifiers

PMID39115958
PMCPMC11308190

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.