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.

PMID: 35776122
PMCID: PMC9508831
Funding: - Guangdong Provincial Natural Science Foundation: 2018A030310035 - Horizon 2020: 872539, 956229