PhyloMagnet

PhyloMagnet screens short-read meta-omics datasets (metagenomes and metatranscriptomes) by combining gene-centric assembly with phylogenetic placement to detect taxa and genes of interest.


Key Features:

  • Gene-Centric Assembly: Performs gene-centric assembly of short reads to target specific genes or taxa without assembling whole genomes.
  • Phylogenetic Placement: Uses phylogenetic placement to position assembled sequences within existing phylogenies for taxonomic and gene identification.
  • High Accuracy and Sensitivity: Benchmarks on an in vitro mock community identified up to 87% of genera present, with a reported false positive rate per single gene tree of 0–23%.
  • Application Versatility: Detects taxonomic labels from metagenome-assembled genomes (MAGs) and aligns phylogenetic placements with transcripts derived from transcriptome assemblies.

Scientific Applications:

  • Microbial ecology and environmental genomics: Screening complex microbial communities to detect taxa and genes from environments such as soil, water, and host-associated microbiomes.
  • Taxonomic profiling: Identifying and placing taxa within phylogenies from metagenomic and metatranscriptomic short-read data.
  • Gene expression and function studies: Aligning phylogenetic placements with transcriptome-derived transcripts to investigate gene expression patterns and functional inference in situ.

Methodology:

Computational steps explicitly include gene-centric assembly of short reads followed by phylogenetic placement of assembled sequences into existing phylogenies, applied to metagenomes, metatranscriptomes, metagenome-assembled genomes (MAGs) and transcriptome-derived transcripts, with benchmarking using an in vitro mock community.

Topics

Details

License:
GPL-3.0
Programming Languages:
Python
Added:
1/9/2020
Last Updated:
11/24/2024

Operations

Publications

Schön ME, Eme L, Ettema TJG. PhyloMagnet: fast and accurate screening of short-read meta-omics data using gene-centric phylogenetics. Bioinformatics. 2019;36(6):1718-1724. doi:10.1093/bioinformatics/btz799. PMID:31647547. PMCID:PMC7703773.

PMID: 31647547
PMCID: PMC7703773
Funding: - Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie ITN project SINGEK: H2020-MSCA-ITN-2015-675752 - European Union’s Horizon 2020 research and innovation programme: 704263 - European Research Council ERC Starting: 310039-PUZZLE_CELL - Swedish Foundation for Strategic Research: SSF-FFL5 - Swedish Research Council: 2015-04959

Documentation