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Protocols

DAWG specifications

Information


Unique identifier OMICS_19895
Name DAWG
Alternative name DNA Assembly with Gaps
Software type Application/Script
Interface Command line interface
Restrictions to use None
Output data Genotype or Sequence, Individual Relationship.
Output format FASTA, FASTQ, PHYLIP, NEXUS, CLUSTAL
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, C++, Perl
Computer skills Advanced
Version 1.2
Stability No
Maintained No

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Versioning


No version available

Maintainer


This tool is not available anymore.

Additional information


https://popmodels.cancercontrol.cancer.gov/gsr/packages/dawg/

Publication for DNA Assembly with Gaps

DAWG citations

 (19)
library_books

Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study

2018
Wellcome Open Res
PMCID: 5930550
PMID: 29774245
DOI: 10.21956/wellcomeopenres.15526.r32391

[…] ws for short insertions and deletions (INDELs), gene loss and horizontal gene transfer events which occur in real populations but are usually not included in phylogenetic models. In parallel, we used DAWG v1.2 to simulate evolution of intergenic regions (defined as sequence not annotated as a CDS). We identified a phylogeny ( ), originally produced by Kremer et al. from a core genome alignment o […]

library_books

Solving the master equation for Indels

2017
BMC Bioinformatics
PMCID: 5427538
PMID: 28494756
DOI: 10.1186/s12859-017-1665-1

[…] get larger, making the problem particularly acute for genomic alignments where indels can be much larger than in proteins.A number of excellent, realistic sequence simulators are available including DAWG [], INDELible [], and indel-Seq-Gen []. […]

library_books

gmos: Rapid Detection of Genome Mosaicism over Short Evolutionary Distances

2016
PLoS One
PMCID: 5112998
PMID: 27846272
DOI: 10.1371/journal.pone.0166602

[…] The running time and memory analysis of gmos was performed on closely related sequences simulated using Dawg [] and compared to an alignment-free tool, alfy []. We chose alfy for comparison because it uses a similar indexing approach to gmos for computing exact matches and therefore is the best referenc […]

library_books

General continuous time Markov model of sequence evolution via insertions/deletions: local alignment probability computation

2016
BMC Bioinformatics
PMCID: 5039815
PMID: 27677569
DOI: 10.1186/s12859-016-1167-6

[…] to enumerate such histories efficiently. In this paper, we presented the results of computing local alignment probabilities (or multiplication factors) under a space-homogeneous model implemented in Dawg [], mainly in order to avoid excessive presentational complications. At least theoretically, however, the computational methods (in Additional file ) could be extended to space-heterogeneous situ […]

library_books

General continuous time Markov model of sequence evolution via insertions/deletions: are alignment probabilities factorable?

2016
BMC Bioinformatics
PMCID: 5026781
PMID: 27638547
DOI: 10.1186/s12859-016-1105-7

[…] ty of a given MSA. In section S8, some examples are given to illustrate models with factorable and non-factorable alignment probabilities. The former category includes the indel evolutionary model of Dawg [] and the “long indel” model [], among others. In section R9, we discuss the merits, possible uses and extensions, as well as some outstanding issues, of the results in this study. In Table , we […]

call_split

Characterization of multiple sequence alignment errors using complete likelihood score and position shift map

2016
BMC Bioinformatics
PMCID: 4799563
PMID: 26992851
DOI: 10.1186/s12859-016-0945-5
call_split See protocol

[…] We created simulated MSAs via Dawg (version 1.2-RELEASE) [] as explained in the following. First, out of a 36-species phylogenetic tree [], we extracted three sub-trees of mammalian species, namely, the trees of 12 primates, 15 ma […]

Citations

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DAWG institution(s)
Department of Genetics, University of Georgia, Athens, GA, USA
DAWG funding source(s)
Supported by an NSF Predoctoral Fellowship and NIH Grant 5R01 GM48528-06.

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