Evidence map›Paper›PMID 35172890›Full record

ArticleMicrobiome2022

METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks.

Zhichao Zhou, Patricia Q Tran, Adam M Breister, Yang Liu, Kristopher Kieft, Elise S Cowley, Ulas Karaoz, Karthik Anantharaman

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 343 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
343citing papers in PubMed, 1 pooled it
49.0field-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

343 citing papers in PubMed, 1 synthesis or guideline pooled it, 591 citations in OpenAlex.

  1. Pooled it
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  10. Genomic insights from a long-read assembly of a deep-sea Chromohalobacter israelensis strain from Santos Basin pockmarks field.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2026
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283 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

8 authors at 3 institutions in 2 countries.

Zhichao ZhouDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Patricia Q TranDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Adam M BreisterDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Yang LiuArchaeal Biology Center, Institute for Advanced Study, Shenzhen University, Shenzhen, Guangdong, 518060, China.
Kristopher KieftDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Elise S CowleyDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Ulas KaraozEarth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Karthik AnantharamanDepartment of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA. karthik@bact.wisc.edu.ORCID 0000-0002-9584-2491
University of Wisconsin–Madison · USLawrence Berkeley National Laboratory · USShenzhen University · CN

Funding

Research Training for Computation and Informatics in Biology and MedicineT15LM007359 · NLM · UNIVERSITY OF WISCONSIN-MADISON · PI Mark W. Craven, Colin Noel Dewey · 2002 to 2026
$22.6M
Integrated Training For Physician-ScientistsT32GM140935 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI Anna Huttenlocher, Jeniel E Nett · 2021 to 2026
$6.5M
NIGMS NIH HHS T32 GM140935NLM NIH HHS T15 LM007359
6 · The paper itself

Abstract

backgroundAdvances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.

resultsWe present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric "MW-score" (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.

conclusionMETABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC . Video abstract.

Indexed as

Genome, MicrobialMicrobiotaHumansLakesMetagenomeMetagenomicsBiogeochemistryFunctional traitsMetabolic potentialMetagenome-assembled genomesMicrobial functional networksMicrobiome

Identifiers

PMID35172890
PMCPMC8851854
OpenAlexW4220951604

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.