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Tangram: a comprehensive toolbox for mobile element insertion detection
BACKGROUND: Mobile elements (MEs) constitute greater than 50% of the human genome as a result of repeated insertion events during human genome evolution. Although most of these elements are now fixed in the population, some MEs, including ALU, L1, SVA and HERV-K elements, are still actively duplicat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180832/ https://www.ncbi.nlm.nih.gov/pubmed/25228379 http://dx.doi.org/10.1186/1471-2164-15-795 |
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author | Wu, Jiantao Lee, Wan-Ping Ward, Alistair Walker, Jerilyn A Konkel, Miriam K Batzer, Mark A Marth, Gabor T |
author_facet | Wu, Jiantao Lee, Wan-Ping Ward, Alistair Walker, Jerilyn A Konkel, Miriam K Batzer, Mark A Marth, Gabor T |
author_sort | Wu, Jiantao |
collection | PubMed |
description | BACKGROUND: Mobile elements (MEs) constitute greater than 50% of the human genome as a result of repeated insertion events during human genome evolution. Although most of these elements are now fixed in the population, some MEs, including ALU, L1, SVA and HERV-K elements, are still actively duplicating. Mobile element insertions (MEIs) have been associated with human genetic disorders, including Crohn’s disease, hemophilia, and various types of cancer, motivating the need for accurate MEI detection methods. To comprehensively identify and accurately characterize these variants in whole genome next-generation sequencing (NGS) data, a computationally efficient detection and genotyping method is required. Current computational tools are unable to call MEI polymorphisms with sufficiently high sensitivity and specificity, or call individual genotypes with sufficiently high accuracy. RESULTS: Here we report Tangram, a computationally efficient MEI detection program that integrates read-pair (RP) and split-read (SR) mapping signals to detect MEI events. By utilizing SR mapping in its primary detection module, a feature unique to this software, Tangram is able to pinpoint MEI breakpoints with single-nucleotide precision. To understand the role of MEI events in disease, it is essential to produce accurate individual genotypes in clinical samples. Tangram is able to determine sample genotypes with very high accuracy. Using simulations and experimental datasets, we demonstrate that Tangram has superior sensitivity, specificity, breakpoint resolution and genotyping accuracy, when compared to other, recently developed MEI detection methods. CONCLUSIONS: Tangram serves as the primary MEI detection tool in the 1000 Genomes Project, and is implemented as a highly portable, memory-efficient, easy-to-use C++ computer program, built under an open-source development model. |
format | Online Article Text |
id | pubmed-4180832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41808322014-10-03 Tangram: a comprehensive toolbox for mobile element insertion detection Wu, Jiantao Lee, Wan-Ping Ward, Alistair Walker, Jerilyn A Konkel, Miriam K Batzer, Mark A Marth, Gabor T BMC Genomics Methodology Article BACKGROUND: Mobile elements (MEs) constitute greater than 50% of the human genome as a result of repeated insertion events during human genome evolution. Although most of these elements are now fixed in the population, some MEs, including ALU, L1, SVA and HERV-K elements, are still actively duplicating. Mobile element insertions (MEIs) have been associated with human genetic disorders, including Crohn’s disease, hemophilia, and various types of cancer, motivating the need for accurate MEI detection methods. To comprehensively identify and accurately characterize these variants in whole genome next-generation sequencing (NGS) data, a computationally efficient detection and genotyping method is required. Current computational tools are unable to call MEI polymorphisms with sufficiently high sensitivity and specificity, or call individual genotypes with sufficiently high accuracy. RESULTS: Here we report Tangram, a computationally efficient MEI detection program that integrates read-pair (RP) and split-read (SR) mapping signals to detect MEI events. By utilizing SR mapping in its primary detection module, a feature unique to this software, Tangram is able to pinpoint MEI breakpoints with single-nucleotide precision. To understand the role of MEI events in disease, it is essential to produce accurate individual genotypes in clinical samples. Tangram is able to determine sample genotypes with very high accuracy. Using simulations and experimental datasets, we demonstrate that Tangram has superior sensitivity, specificity, breakpoint resolution and genotyping accuracy, when compared to other, recently developed MEI detection methods. CONCLUSIONS: Tangram serves as the primary MEI detection tool in the 1000 Genomes Project, and is implemented as a highly portable, memory-efficient, easy-to-use C++ computer program, built under an open-source development model. BioMed Central 2014-09-16 /pmc/articles/PMC4180832/ /pubmed/25228379 http://dx.doi.org/10.1186/1471-2164-15-795 Text en © Wu et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Wu, Jiantao Lee, Wan-Ping Ward, Alistair Walker, Jerilyn A Konkel, Miriam K Batzer, Mark A Marth, Gabor T Tangram: a comprehensive toolbox for mobile element insertion detection |
title | Tangram: a comprehensive toolbox for mobile element insertion detection |
title_full | Tangram: a comprehensive toolbox for mobile element insertion detection |
title_fullStr | Tangram: a comprehensive toolbox for mobile element insertion detection |
title_full_unstemmed | Tangram: a comprehensive toolbox for mobile element insertion detection |
title_short | Tangram: a comprehensive toolbox for mobile element insertion detection |
title_sort | tangram: a comprehensive toolbox for mobile element insertion detection |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180832/ https://www.ncbi.nlm.nih.gov/pubmed/25228379 http://dx.doi.org/10.1186/1471-2164-15-795 |
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