metaSNV

metaSNV performs single nucleotide variant (SNV) detection and population-genetic analyses from metagenomic read alignments to reference genomes.


Key Features:

  • Input Flexibility: Accepts nucleotide sequence alignments in standard SAM/BAM format as input.
  • Comprehensive SNV Calling: Performs SNV calling at both individual-sample and dataset-wide levels.
  • Statistical Analysis: Reports allele frequencies and nucleotide diversity for each species in the dataset.
  • Comparative Metrics: Computes distance measures that enable comparison of genomic variation across metagenomic samples and tracking of strain-specific variants over time.
  • Efficiency and Storage Optimization: Benchmarked against MIDAS, showing faster processing times and a reduced storage footprint without compromising result accuracy.

Scientific Applications:

  • Microbial ecology: Enables SNV-based characterization of genetic diversity and population structure across thousands of bacterial and archaeal species.
  • Evolutionary biology and population genetics: Supports analyses of allele frequency distributions, nucleotide diversity, genetic distances, and fixation indices within microbial communities.
  • Longitudinal metagenomics and strain tracking: Facilitates detection and temporal tracking of strain-specific variants in environmental and host-associated microbiomes.

Methodology:

Uses nucleotide sequence alignments to reference genomes (SAM/BAM) for SNV calling and computes allele frequencies, nucleotide diversity, distance measures and fixation indices across samples.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux, Mac
Programming Languages:
Shell, C++, Python
Added:
5/19/2018
Last Updated:
1/15/2019

Operations

Data Inputs & Outputs

Nucleic acid sequence analysis

Publications

Costea PI, Munch R, Coelho LP, Paoli L, Sunagawa S, Bork P. metaSNV: A tool for metagenomic strain level analysis. PLOS ONE. 2017;12(7):e0182392. doi:10.1371/journal.pone.0182392. PMID:28753663. PMCID:PMC5533426.

PMID: 28753663
PMCID: PMC5533426
Funding: - European Research Council: 669830

Documentation