ArticlePeerJ2016
PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes.
Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Benchmarking methods for extracting microbial signal from host-dominated metatranscriptomes.Briefings in bioinformatics · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- krepp: a k-mer-based maximum pseudo-likelihood method for estimating read distances and genome-wide phylogenetic placement.Genome biology · 2026Article
- Basic Microbiome Analysis: Analytical Steps from Sampling to Sequencing.Microorganisms · 2026Review
- Review
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Article
- Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review.Foods (Basel, Switzerland) · 2024Review
- MetageNN: a memory-efficient neural network taxonomic classifier robust to sequencing errors and missing genomes.BMC bioinformatics · 2024Article
- Machine learning for microbiologists.Nature reviews. Microbiology · 2024Review
- Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning.Briefings in bioinformatics · 2023Article
- Influence of soil nutrients on the presence and distribution of CPR bacteria in a long-term crop rotation experiment.Frontiers in microbiology · 2023Article
- Binning Metagenomic Contigs Using Unsupervised Clustering and Reference Databases.Interdisciplinary sciences, computational life sciences · 2022Article
- MetaDecoder: a novel method for clustering metagenomic contigs.Microbiome · 2022Article
- BugSplit enables genome-resolved metagenomics through highly accurate taxonomic binning of metagenomic assemblies.Communications biology · 2022Article
- MetaCRS: unsupervised clustering of contigs with the recursive strategy of reducing metagenomic dataset's complexity.BMC bioinformatics · 2022Article
- The evolution of bacterial genome assemblies - where do we need to go next?Microbiome research reports · 2022Article
- Potential Use of Microbial Community Genomes in Various Dimensions of Agriculture Productivity and Its Management: A Review.Frontiers in microbiology · 2022Review
- The endophytome (plant-associated microbiome): methodological approaches, biological aspects, and biotech applications.World journal of microbiology & biotechnology · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into 'bins' representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies 'training' sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have. Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S) software. The new (+) component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4-6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods. Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.
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