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Genome Res. 2017 Nov;27(11):1916-1929. doi: 10.1101/gr.218032.116. Epub 2017 Aug 30.

The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology.

Author information

1
Program in Molecular Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
2
Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
3
Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
4
Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
5
Division of Gastroenterology, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
6
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA.
7
Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.
8
Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.

Abstract

Mobile element insertions (MEIs) represent ∼25% of all structural variants in human genomes. Moreover, when they disrupt genes, MEIs can influence human traits and diseases. Therefore, MEIs should be fully discovered along with other forms of genetic variation in whole genome sequencing (WGS) projects involving population genetics, human diseases, and clinical genomics. Here, we describe the Mobile Element Locator Tool (MELT), which was developed as part of the 1000 Genomes Project to perform MEI discovery on a population scale. Using both Illumina WGS data and simulations, we demonstrate that MELT outperforms existing MEI discovery tools in terms of speed, scalability, specificity, and sensitivity, while also detecting a broader spectrum of MEI-associated features. Several run modes were developed to perform MEI discovery on local and cloud systems. In addition to using MELT to discover MEIs in modern humans as part of the 1000 Genomes Project, we also used it to discover MEIs in chimpanzees and ancient (Neanderthal and Denisovan) hominids. We detected diverse patterns of MEI stratification across these populations that likely were caused by (1) diverse rates of MEI production from source elements, (2) diverse patterns of MEI inheritance, and (3) the introgression of ancient MEIs into modern human genomes. Overall, our study provides the most comprehensive map of MEIs to date spanning chimpanzees, ancient hominids, and modern humans and reveals new aspects of MEI biology in these lineages. We also demonstrate that MELT is a robust platform for MEI discovery and analysis in a variety of experimental settings.

PMID:
28855259
PMCID:
PMC5668948
DOI:
10.1101/gr.218032.116
[Indexed for MEDLINE]
Free PMC Article

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