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dRep

Reduces the computational time for pair-wise genome set comparisons by orders of magnitude. dRep sequentially applies an inaccurate estimation of genome distance and a slow but accurate measure of average nucleotide identity. It is easy to use, highly customizable, and parallelizable. The tool can perform rapid pairwise genomic comparisons (without de-replication) to enable visualization of the degree of similarity among groups of similar genomes.

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dRep specifications

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Information

Unique identifier	OMICS_16085
Name	dRep
Software type	Package/Module
Interface	Command line interface
Restrictions to use	None
Operating system	Unix/Linux
Programming languages	Python
Computer skills	Advanced
Version	0.3.4
Stability	Stable
Requirements	Mash, Nucmer, CheckM, Prodigal, gANI, Centrifuge
Maintained	Yes

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Documentation

Documentation: http://drep.readthedocs.io/en/master/
Issues: https://github.com/MrOlm/drep/issues

Maintainer

Jillian Banfield <>

Publication for dRep

DRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication.

2017 ISME J

PMCID: 5702732

PMID: 28742071

DOI: 10.1038/ismej.2017.126

dRep in publication

Hospitalized Premature Infants Are Colonized by Related Bacterial Strains with Distinct Proteomic Profiles

PMCID: 5893878

PMID: 29636439

DOI: 10.1128/mBio.00441-18

[…] assignments. in order to identify cases in which the same bacterial strain was present in multiple samples, subspecies groups were further analyzed with the anim algorithm () implemented in drep ()., in order to achieve accurate abundance and replication rate measurements from read mapping, databases of representative genomes were created for each sample. each database was constructed […]

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dRep funding source(s)

Supported by the Sloan Foundation (grant number: G 2012-10-05), the National Institutes of Health (NIH; award reference number 5R01-AI-092531) and by the National Science Foundation Graduate Research Fellowship under Grant No. DGE 1106400.

dRep review

Matt Olm

2017-04-20, 17:53

Desktop

Great