Evidence map›Paper›PMID 32458974›Full record

ArticleMolecular biology and evolution2020

Gradients Do Grow on Trees: A Linear-Time O(N)-Dimensional Gradient for Statistical Phylogenetics.

Xiang Ji, Zhenyu Zhang, Andrew Holbrook, Akihiko Nishimura, Guy Baele, Andrew Rambaut, Philippe Lemey, Marc A Suchard

Abstract readEvaluation Study
In one paragraph

Article in Molecular biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

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  13. Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Review
  14. Dispersal history of SARS-CoV-2 in Galicia, Spain.Journal of medical virology · 2024
    Article
  15. Article
  16. Dispersal history of SARS-CoV-2 in Galicia, Spain.medRxiv : the preprint server for health sciences · 2024
    Article
  17. Article
  18. On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2.Proceedings of the National Academy of Sciences of the United States of America · 2024
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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

8 authors.

Xiang JiDepartment of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA.
Zhenyu ZhangDepartment of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA.
Andrew HolbrookDepartment of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA.
Akihiko NishimuraDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Guy BaeleDepartment of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
Andrew RambautInstitute of Evolutionary Biology, Centre for Immunology, Infection and Evolution, University of Edinburgh, Edinburgh, United Kingdom.
Philippe LemeyDepartment of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
Marc A SuchardDepartment of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA.

Funding

Technology CoreU19AI135995 · NIAID · SCRIPPS RESEARCH INSTITUTE, THE · PI Kristian Graugaard Andersen · 2018 to 2026
$32.0M
BAYESIAN MODELING AND DATA INTEGRATION IN INFECTIOUS DISEASE PHYLODYNAMICSR01AI107034 · NIAID · UNIVERSITY OF WASHINGTON · PI MININ, VOLODYMYR · 2013 to 2017
$1.7M
Big Data Predictive Phylogenetics with Bayesian LearningK25AI153816 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOLBROOK, ANDREW JAMES · 2020 to 2024
$532k
NIAID NIH HHS K25 AI153816NIAID NIH HHS R01 AI107034NIAID NIH HHS U19 AI135995Wellcome TrustWellcome Trust 206298/Z/17/Z
6 · The paper itself

Abstract

Calculation of the log-likelihood stands as the computational bottleneck for many statistical phylogenetic algorithms. Even worse is its gradient evaluation, often used to target regions of high probability. Order O(N)-dimensional gradient calculations based on the standard pruning algorithm require O(N2) operations, where N is the number of sampled molecular sequences. With the advent of high-throughput sequencing, recent phylogenetic studies have analyzed hundreds to thousands of sequences, with an apparent trend toward even larger data sets as a result of advancing technology. Such large-scale analyses challenge phylogenetic reconstruction by requiring inference on larger sets of process parameters to model the increasing data heterogeneity. To make these analyses tractable, we present a linear-time algorithm for O(N)-dimensional gradient evaluation and apply it to general continuous-time Markov processes of sequence substitution on a phylogenetic tree without a need to assume either stationarity or reversibility. We apply this approach to learn the branch-specific evolutionary rates of three pathogenic viruses: West Nile virus, Dengue virus, and Lassa virus. Our proposed algorithm significantly improves inference efficiency with a 126- to 234-fold increase in maximum-likelihood optimization and a 16- to 33-fold computational performance increase in a Bayesian framework.

Indexed as

Evolution, MolecularModels, GeneticPhylogenyAlgorithmsFlavivirusLassa virusBayesian inferencelinear-time gradient algorithmmaximum likelihoodrandom-effects molecular clock model

Identifiers

PMID32458974
PMCPMC7530611

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.