Evidence map›Paper›PMID 37083735›Full record

ArticlePLoS biology2023

iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria.

Simon Roux, Antonio Pedro Camargo, Felipe H Coutinho, Shareef M Dabdoub, Bas E Dutilh, Stephen Nayfach, Andrew Tritt

Open access · goldAbstract read
In one paragraph

Article in PLoS biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 286 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
286citing papers in PubMed, 4 pooled it
101.4field-weighted citation impact, top 1% of its field
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

286 citing papers in PubMed, 4 syntheses or guidelines pooled it, 409 citations in OpenAlex.

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226 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

7 authors at 4 institutions in 4 countries.

Simon RouxDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.ORCID 0000-0002-5831-5895
Antonio Pedro CamargoDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
Felipe H CoutinhoInstituto de Ciencias del Mar (ICM-CSIC), Barcelona, Spain.
Shareef M DabdoubDivision of Biostatistics and Computational Biology, University of Iowa College of Dentistry, Iowa City, Iowa, United States of America.
Bas E DutilhInstitute of Biodiversity, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University, Jena, Germany.
Stephen NayfachDOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
Andrew TrittApplied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
Lawrence Berkeley National Laboratory · USInstitut de Ciències del Mar · ESUniversity of Iowa · USUtrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.

Indexed as

ArchaeaVirusesBacteriaGenome, ViralMachine LearningMetagenomeMetagenomics

Identifiers

PMID37083735
PMCPMC10155999
OpenAlexW4366602379

What OpenQuestion holds

Textmetadata
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.