ArticleMicrobiome2020
Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes.
Article in Microbiome, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- The microbial dark matter and "wanted list" in worldwide wastewater treatment plants.Microbiome · 2023Pooled it
- Antimicrobial resistance analysis of Escherichia coli isolated from foxes in China.Veterinary research communications · 2026Article
- Overlooked siderophore producers favor ammonium oxidation in global wastewater treatment plants.Microbiome · 2025Article
- Multiple extracellular polymeric substance pathways transcribed byApplied and environmental microbiology · 2025Article
- Comparative transcription profiles ofFrontiers in microbiology · 2025Article
- Exploring interspecific interaction variability in microbiota: A review.Engineering microbiology · 2024Review
- Advancing Source Tracking: Systematic Review and Source-Specific Genome Database Curation of Fecally Shed Prokaryotes.Environmental science & technology letters · 2024Article
- GSearch: ultra-fast and scalable genome search by combining K-mer hashing with hierarchical navigable small world graphs.Nucleic acids research · 2024Article
- Effects of operational parameters on bacterial communities in Hong Kong and global wastewater treatment plants.mSystems · 2024Article
- Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration.Frontiers in immunology · 2024Article
- Unveiling the hidden diversity and functional role of Chloroflexota in full-scale wastewater treatment plants through genome-centric analyses.ISME communications · 2024Article
- Metagenome sequencing and recovery of 444 metagenome-assembled genomes from the biofloc aquaculture system.Scientific data · 2023Article
- Metabolic implications for predatory and parasitic bacterial lineages in activated sludge wastewater treatment systems.Water research X · 2023Article
- Active predation, phylogenetic diversity, and global prevalence of myxobacteria in wastewater treatment plants.The ISME journal · 2023Article
- Metagenomic and machine learning-aided identification of biomarkers driving distinctive Cd accumulation features in the root-associated microbiome of two rice cultivars.ISME communications · 2023Article
- Predation increases multiple components of microbial diversity in activated sludge communities.The ISME journal · 2022Article
- Genome-resolved metagenomics identifies the particular genetic traits of phosphate-solubilizing bacteria in agricultural soil.ISME communications · 2022Article
- Recovery of High Quality Metagenome-Assembled Genomes From Full-Scale Activated Sludge Microbial Communities in a Tropical Climate Using Longitudinal Metagenome Sampling.Frontiers in microbiology · 2022Article
- Genomes of NovelApplied and environmental microbiology · 2021Article
- Charting the complexity of the activated sludge microbiome through a hybrid sequencing strategy.Microbiome · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene that may not truly represent in situ functions.
resultsHere, we present 2045 archaeal and bacterial metagenome-assembled genomes (MAGs) recovered from 1.35 Tb of metagenomic data generated from 114 AS samples of 23 full-scale wastewater treatment plants (WWTPs). We found that the AS MAGs have obvious plant-specific features and that few proteins are shared by different WWTPs, especially for WWTPs located in geographically distant areas. Further, we developed a novel machine learning approach that can distinguish between AS MAGs and MAGs from other environments based on the clusters of orthologous groups of proteins with an accuracy of 96%. With the aid of machine learning, we also identified some functional features (e.g., functions related to aerobic metabolism, nutrient sensing/acquisition, and biofilm formation) that are likely vital for AS bacteria to adapt themselves in wastewater treatment bioreactors.
conclusionsOur work reveals that, although the bacterial species in different municipal WWTPs could be different, they may have similar deterministic functional features that allow them to adapt to the AS systems. Also, we provide valuable genome resources and a novel approach for future investigation and better understanding of the microbiome of AS and other ecosystems. Video Abtract.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.