Dataset Information

Identification of Genome Sequences of Polyphosphate-Accumulating Organisms by Machine Learning.

ABSTRACT: In the field of sewage treatment, the identification of polyphosphate-accumulating organisms (PAOs) usually relies on biological experiments. However, biological experiments are not only complicated and time-consuming, but also costly. In recent years, machine learning has been widely used in many fields, but it is seldom used in the water treatment. The present work presented a high accuracy support vector machine (SVM) algorithm to realize the rapid identification and prediction of PAOs. We obtained 6,318 genome sequences of microorganisms from the publicly available microbial genome database for comparative analysis (MBGD). Minimap2 was used to compare the genomes of the obtained microorganisms in pairs, and read the overlap. The SVM model was established using the similarity of the genome sequences. In this SVM model, the average accuracy is 0.9628 ± 0.019 with 10-fold cross-validation. By predicting 2,652 microorganisms, 22 potential PAOs were obtained. Through the analysis of the predicted potential PAOs, most of them could be indirectly verified their phosphorus removal characteristics from previous reports. The SVM model we built shows high prediction accuracy and good stability.

SUBMITTER: Liu B

PROVIDER: S-EPMC7848102 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of Genome Sequences of Polyphosphate-Accumulating Organisms by Machine Learning.

Liu Bohan B Nan Jun J Zu Xuehui X Zhang Xinhui X Xiao Qiliang Q

Frontiers in cell and developmental biology 20210118

In the field of sewage treatment, the identification of polyphosphate-accumulating organisms (PAOs) usually relies on biological experiments. However, biological experiments are not only complicated and time-consuming, but also costly. In recent years, machine learning has been widely used in many fields, but it is seldom used in the water treatment. The present work presented a high accuracy support vector machine (SVM) algorithm to realize the rapid identification and prediction of PAOs. We ob ...[more]

PMID: 33537313

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Project description:Laboratory-scale sequencing batch reactors (SBRs) as models for activated sludge processes were used to study enhanced biological phosphorus removal (EBPR) from wastewater. Enrichment for polyphosphate-accumulating organisms (PAOs) was achieved essentially by increasing the phosphorus concentration in the influent to the SBRs. Fluorescence in situ hybridization (FISH) using domain-, division-, and subdivision-level probes was used to assess the proportions of microorganisms in the sludges. The A sludge, a high-performance P-removing sludge containing 15.1% P in the biomass, was comprised of large clusters of polyphosphate-containing coccobacilli. By FISH, >80% of the A sludge bacteria were beta-2 Proteobacteria arranged in clusters of coccobacilli, strongly suggesting that this group contains a PAO responsible for EBPR. The second dominant group in the A sludge was the Actinobacteria. Clone libraries of PCR-amplified bacterial 16S rRNA genes from three high-performance P-removing sludges were prepared, and clones belonging to the beta-2 Proteobacteria were fully sequenced. A distinctive group of clones (sharing >/=98% sequence identity) related to Rhodocyclus spp. (94 to 97% identity) and Propionibacter pelophilus (95 to 96% identity) was identified as the most likely candidate PAOs. Three probes specific for the highly related candidate PAO group were designed from the sequence data. All three probes specifically bound to the morphologically distinctive clusters of PAOs in the A sludge, exactly coinciding with the beta-2 Proteobacteria probe. Sequential FISH and polyphosphate staining of EBPR sludges clearly demonstrated that PAO probe-binding cells contained polyphosphate. Subsequent PAO probe analyses of a number of sludges with various P removal capacities indicated a strong positive correlation between P removal from the wastewater as determined by sludge P content and number of PAO probe-binding cells. We conclude therefore that an important group of PAOs in EBPR sludges are bacteria closely related to Rhodocyclus and Propionibacter.

Dataset Information

Identification of Genome Sequences of Polyphosphate-Accumulating Organisms by Machine Learning.

Publications

Identification of Genome Sequences of Polyphosphate-Accumulating Organisms by Machine Learning.

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