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.