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Mobile Element Locator Tool MELT

Allows users to discover and study mobile element insertions (MEIs) in whole genome sequencing (WGS) projects. MELT includes a robust MEI discovery algorithm and a suite of MEI analysis tools. Several run modes were developed to provide flexibility in implementation, including the single-sample (MELT-Single) mode, which are useful for discovering and annotating MEIs in a relatively small number of genomes and the multiple-sample (MELT-Split) mode and the multiple-sample (MELTSGE) automated mode which are engineered for population-scale studies involving hundreds or thousands of genomes.

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2 user reviews

2 user reviews

Robert P. Ruggiero's avatar image Robert P. Ruggiero's country flag

Robert P. Ruggiero

We have been using MELT on non-model genomes and it's been great (http://journal.frontiersin.org/article/10.3389/fgene.2017.00044/full).
MELT is easy to work with and gives consistent reliable results.

Daniel Webber's avatar image

Daniel Webber

MELT was easy to install and run on SGE! It called our Alu and L1 RIPs accurately to the bp. It does a great job at integrating prior calls, and its genotyping is consistent between samples. It is more accurate then the other tools that we have used for calling transposable elements.

MELT forum

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MELT versioning

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No versioning.

MELT classification

MELT specifications

Software type:
Package/Module
Restrictions to use:
Academic or non-commercial use
Operating system:
Unix/Linux
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes
Interface:
Command line interface
Biological technology:
Illumina
Programming languages:
Java
Version:
2.0.2
Requirements:
Bowtie2
Conda:
https://bioconda.github.io/recipes/melt/README.html

MELT support

Documentation

Maintainers

Additional information

http://melt.igs.umaryland.edu/manual.php

Credits

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Publications

Institution(s)

Program in Molecular Medicine, University of Maryland School of Medicine, Baltimore, MD, USA; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA; Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA; Division of Gastroenterology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA

Funding source(s)

Supported by grants: F31 HG009223, T32 DK067872, R01 CA166661, and R01 HG002898.

Link to literature

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