metaSNV protocols

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metaSNV specifications


Unique identifier OMICS_19879
Name metaSNV
Software type Application/Script
Interface Command line interface
Restrictions to use None
Input format SAM, BAM
Operating system Unix/Linux
Programming languages C++, Python, Shell (Bash)
License GNU General Public License version 3.0
Computer skills Advanced
Stability Stable
boost, htslib, samtools, pandas, numpy
Maintained Yes



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Additional information

Publication for metaSNV

metaSNV in pipeline

PMCID: 5740502
PMID: 29242367
DOI: 10.15252/msb.20177589

[…] was performed using qacompute (, resulting in estimations of both horizontal and vertical coverage per sample, per genome., population snps were called using metasnv (costea et al, ), which resulted in 19,221,237 positions over 1,753 genomes., determination of subspecies structure proceeded through the following steps: firstly, the set of samples […]

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metaSNV in publications

PMCID: 5907387
PMID: 29669589
DOI: 10.1186/s40168-018-0450-3

[…] profiling single-nucleotide polymorphisms (snps) for species present in our dog samples and in publicly available human microbiome samples. among the species with high enough coverage using default metasnv parameters [], only for a single species, bacteroides sp. d20 was a minimal overlap in snp space observed between any human and dog strains, due to a single dog sample (the two most abundant […]

PMCID: 5888634
PMID: 29617800
DOI: 10.1093/gbe/evy057

[…] mapped to consensus gdna sequences using bwa (), and filtered for unique mappers with at least 95% similarity and 40 nt. nucleotide variability analysis and snp detection was then performed using metasnv () with filtering parameters m = 1, d = 0.1, b = 1 and c = 1., automated editing event detection was carried out using custom python scripts (available […]

PMCID: 5740502
PMID: 29242367
DOI: 10.15252/msb.20177589

[…] (sd = 6%) of assigned relative abundance per sample; fig b)., to delineate population structure corresponding to subspecies (referred to as metagenomic subspecies, mgss, in the following), we used metasnv (costea et al, ) to compute a matrix of genomic allele distances between all samples that allowed variant calling for any given species (), and identified clusters by applying […]

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metaSNV institution(s)
Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Department of Biology, Institute of Microbiology, ETH Zurich, Zurich, Switzerland; Department of Biology, Ecole normale supérieure, Paris, France; Max-Delbruck-Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Wurzburg, Wurzburg, Germany; Molecular Medicine Partnership Unit, Heidelberg, Germany
metaSNV funding source(s)
Supported by the European Molecular Biology Laboratory and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MicrobioS, grant agreement No. 669830).

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