Evidence map›Paper›PMID 32082281›Full record

ArticleFrontiers in microbiology2020

FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies.

Arkadiy I Garber, Kenneth H Nealson, Akihiro Okamoto, Sean M McAllister, Clara S Chan, Roman A Barco, Nancy Merino

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 211 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
211citing papers in PubMed, 1 pooled it
19.9field-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

211 citing papers in PubMed, 1 synthesis or guideline pooled it, 431 citations in OpenAlex.

  1. Pooled it
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  8. DistinguishingmSystems · 2026
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  9. Complete genome sequences of metal-oxidizing bacteriaMicrobiology resource announcements · 2026
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151 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 2 countries.

Arkadiy I GarberDepartment of Earth Sciences, University of Southern California, Los Angeles, CA, United States.
Kenneth H NealsonDepartment of Earth Sciences, University of Southern California, Los Angeles, CA, United States.
Akihiro OkamotoInternational Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan.
Sean M McAllisterSchool of Marine Science and Policy, University of Delaware, Newark, DE, United States.
Clara S ChanDepartment of Earth Sciences, University of Delaware, Newark, DE, United States.
Roman A BarcoDepartment of Earth Sciences, University of Southern California, Los Angeles, CA, United States.
Nancy MerinoDepartment of Earth Sciences, University of Southern California, Los Angeles, CA, United States.
University of Southern California · USUniversity of Delaware · USLife Science Institute · JPNational Institute for Materials Science · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth's crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in

Indexed as

hidden Markov model (HMM) databaseiron gene regulationiron oxidationiron reductioniron storageiron transportmagnetosomesiderophore

Identifiers

PMID32082281
PMCPMC7005843
OpenAlexW3003366856

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.