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gargammel: a sequence simulator for ancient DNA

SUMMARY: Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact...

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Detalles Bibliográficos
Autores principales: Renaud, Gabriel, Hanghøj, Kristian, Willerslev, Eske, Orlando, Ludovic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408798/
https://www.ncbi.nlm.nih.gov/pubmed/27794556
http://dx.doi.org/10.1093/bioinformatics/btw670
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author Renaud, Gabriel
Hanghøj, Kristian
Willerslev, Eske
Orlando, Ludovic
author_facet Renaud, Gabriel
Hanghøj, Kristian
Willerslev, Eske
Orlando, Ludovic
author_sort Renaud, Gabriel
collection PubMed
description SUMMARY: Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact their analyses in silico, we have developed gargammel, a package that simulates ancient DNA fragments given a set of known reference genomes. Our package simulates the entire molecular process from post-mortem DNA fragmentation and DNA damage to experimental sequencing errors, and reproduces most common bias observed in ancient DNA datasets. AVAILABILITY AND IMPLEMENTATION: The package is publicly available on github: https://grenaud.github.io/gargammel/ and released under the GPL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-54087982017-05-03 gargammel: a sequence simulator for ancient DNA Renaud, Gabriel Hanghøj, Kristian Willerslev, Eske Orlando, Ludovic Bioinformatics Applications Notes SUMMARY: Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact their analyses in silico, we have developed gargammel, a package that simulates ancient DNA fragments given a set of known reference genomes. Our package simulates the entire molecular process from post-mortem DNA fragmentation and DNA damage to experimental sequencing errors, and reproduces most common bias observed in ancient DNA datasets. AVAILABILITY AND IMPLEMENTATION: The package is publicly available on github: https://grenaud.github.io/gargammel/ and released under the GPL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2017-02-15 2016-11-21 /pmc/articles/PMC5408798/ /pubmed/27794556 http://dx.doi.org/10.1093/bioinformatics/btw670 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Renaud, Gabriel
Hanghøj, Kristian
Willerslev, Eske
Orlando, Ludovic
gargammel: a sequence simulator for ancient DNA
title gargammel: a sequence simulator for ancient DNA
title_full gargammel: a sequence simulator for ancient DNA
title_fullStr gargammel: a sequence simulator for ancient DNA
title_full_unstemmed gargammel: a sequence simulator for ancient DNA
title_short gargammel: a sequence simulator for ancient DNA
title_sort gargammel: a sequence simulator for ancient dna
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408798/
https://www.ncbi.nlm.nih.gov/pubmed/27794556
http://dx.doi.org/10.1093/bioinformatics/btw670
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