Evidence map›Paper›PMID 38674735›Full record

ArticleMicroorganisms2024

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential.

Raphael D Isokpehi, Yungkul Kim, Sarah E Krejci, Vishwa D Trivedi

Open access · goldAbstract read
In one paragraph

Article in Microorganisms, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.3field-weighted citation impact, top 13% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
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

4 authors at 1 institution in 1 country.

Raphael D IsokpehiOyster Microbiome Project, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL 32114, USA.ORCID 0000-0002-6877-0840
Yungkul KimOyster Microbiome Project, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL 32114, USA.ORCID 0000-0002-0320-6202
Sarah E KrejciOyster Microbiome Project, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL 32114, USA.ORCID 0000-0003-2874-8822
Vishwa D TrivediOyster Microbiome Project, College of Science, Engineering and Mathematics, Bethune-Cookman University, Daytona Beach, FL 32114, USA.ORCID 0000-0001-8753-1880
Bethune-Cookman University · US

Funding

H3ABioNet: a sustainable African Bioinformatics Network for H3AfricaU41HG006941 · NHGRI · UNIVERSITY OF CAPE TOWN · PI MULDER, NICOLA · 2012 to 2017
$14.8M
NHGRI NIH HHS U41 HG006941NIH HHS U41HG006941
6 · The paper itself

Abstract

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included

Indexed as

archaeaArcobacteraceaebacteriabioinformaticsdata investigationsdenitrificationecological traitgenomesKEGG Orthologymicrobial ecologysynthetic denitrifying communitiesvisual analytics

Identifiers

PMID38674735
PMCPMC11052009
OpenAlexW4394832219

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.