Evidence map›Paper›PMID 24859245›Full record

ArticleMolecular biology and evolution2014

An experimentally determined evolutionary model dramatically improves phylogenetic fit.

Jesse D Bloom

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers.

0numbers the graph read from it
0cells of the map it votes in
138citing 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

138 citing papers in PubMed.

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  11. Competition between Der1 and ERAD-M substrates controls Hrd1 complex function.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  12. ΦX174 bacteriophage viability predicted by protein biophysical modeling.bioRxiv : the preprint server for biology · 2025
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78 more citing papers are in PubMed but not listed here.

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

1 author.

Jesse D BloomDivision of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA jbloom@fhcrc.org.

Funding

Translational Bioimaging Core Shared ResourceP30CA015704 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Marian Esvelt · 1985 to 2026
$296.4M
Mapping epistasis in molecular evolution: the influenza nucleoproteinR01GM102198 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BLOOM, JESSE D · 2012 to 2016
$1.6M
NCI NIH HHS P30 CA015704NIGMS NIH HHS R01 GM102198
6 · The paper itself

Abstract

All modern approaches to molecular phylogenetics require a quantitative model for how genes evolve. Unfortunately, existing evolutionary models do not realistically represent the site-heterogeneous selection that governs actual sequence change. Attempts to remedy this problem have involved augmenting these models with a burgeoning number of free parameters. Here, I demonstrate an alternative: Experimental determination of a parameter-free evolutionary model via mutagenesis, functional selection, and deep sequencing. Using this strategy, I create an evolutionary model for influenza nucleoprotein that describes the gene phylogeny far better than existing models with dozens or even hundreds of free parameters. Emerging high-throughput experimental strategies such as the one employed here provide fundamentally new information that has the potential to transform the sensitivity of phylogenetic and genetic analyses.

Indexed as

Models, GeneticAlgorithmsCodonEvolution, MolecularHigh-Throughput Nucleotide SequencingMolecular Sequence DataMutation RateOrthomyxoviridaePhylogenyViral Core ProteinsCodonViral Core Proteinscodon modeldeep mutational scanninginfluenzanucleoproteinphylogeneticssubstitution model

Identifiers

PMID24859245
PMCPMC4104320

What OpenQuestion holds

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LicenceCC BY
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.