Evidence map›Paper›PMID 39429886›Full record

ArticleiMeta2024

iNAP 2.0: Harnessing metabolic complementarity in microbial network analysis.

Xi Peng, Kai Feng, Xingsheng Yang, Qing He, Bo Zhao, Tong Li, Shang Wang, Ye Deng

Abstract read
In one paragraph

Article in iMeta, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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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

19 citing papers in PubMed.

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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.

Xi PengCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.
Kai FengCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.
Xingsheng YangCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.
Qing HeCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.
Bo ZhaoCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.
Tong LiCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.
Shang WangCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.
Ye DengCAS Key Laboratory for Environmental Biotechnology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences (CAS) Beijing China.ORCID 0000-0002-7584-0632

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Indexed as

metabolic complementaritymetabolic networkmetagenome‐assembled genomes

Identifiers

PMID39429886
PMCPMC11487609

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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.