StrainXpress
StrainXpress reconstructs strain-level genomes from metagenomic sequencing reads to resolve individual microbial strains in complex communities.
Key Features:
- De Novo Assembly: Employs de novo assembly without requiring reference sequences to reconstruct genomes from metagenomic reads.
- Strain-Level Resolution: Differentiates strains of the same species by resolving minor genetic variations to enable strain-specific genome reconstruction.
- Handling High Complexity Data: Reconstructs genomes from samples containing over 1,000 strains, demonstrating scalability to high-complexity metagenomes.
- Performance on Poorly Covered Strains: Recovers genomes from strains with low sequencing coverage to improve completeness across unevenly sampled populations.
- Superior Assembly Outcomes: Reconstructs a higher amount of strain-specific sequence than comparator methods, with an average improvement of 26.75% (range: 18.51% in the first quartile to 35.05% in the third quartile).
Scientific Applications:
- Clinical microbiology: Enables strain-resolved analysis of antibiotic resistance and virulence factors in clinical samples.
- Environmental microbiology: Supports investigation of microbial interactions and community composition in environmental metagenomes at strain resolution.
- Microbial ecology and pathogenesis: Facilitates studies of microbial ecology and pathogenesis by providing precise strain differentiation within complex communities.
Methodology:
Implements the overlap-layout-consensus (OLC) paradigm by identifying overlaps between sequencing reads, constructing a layout, and generating consensus sequences.
Topics
Details
- License:
- GPL-3.0
- Cost:
- Free of charge
- Tool Type:
- command-line tool
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- C++, Python
- Added:
- 9/28/2022
- Last Updated:
- 11/24/2024
Operations
Publications
Kang X, Luo X, Schönhuth A. StrainXpress: strain aware metagenome assembly from short reads. Nucleic Acids Research. 2022;50(17):e101-e101. doi:10.1093/nar/gkac543. PMID:35776122. PMCID:PMC9508831.
DOI: 10.1093/nar/gkac543
PMID: 35776122
PMCID: PMC9508831
Funding: - Guangdong Provincial Natural Science Foundation: 2018A030310035
- Horizon 2020: 872539, 956229