Abstract
Duplex sequencing was originally developed to detect rare nucleotide polymorphisms normally obscured by the noise of high-throughput sequencing. Here we describe a new, streamlined, reference-free approach for the analysis of duplex sequencing data. We show the approach performs well on simulated data and precisely reproduces previously published results and apply it to a newly produced dataset, enabling us to type low-frequency variants in human mitochondrial DNA. Finally, we provide all necessary tools as stand-alone components as well as integrate them into the Galaxy platform. All analyses performed in this manuscript can be repeated exactly as described at http://usegalaxy.org/duplex.
| Original language | English (US) |
|---|---|
| Article number | 180 |
| Journal | Genome biology |
| Volume | 17 |
| Issue number | 1 |
| DOIs | |
| State | Published - Aug 26 2016 |
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All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
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}
Streamlined analysis of duplex sequencing data with Du Novo. / Stoler, Nicholas; Arbeithuber, Barbara; Guiblet, Wilfried; Makova, Kateryna D.; Nekrutenko, Anton.
In: Genome biology, Vol. 17, No. 1, 180, 26.08.2016.Research output: Contribution to journal › Article
TY - JOUR
T1 - Streamlined analysis of duplex sequencing data with Du Novo
AU - Stoler, Nicholas
AU - Arbeithuber, Barbara
AU - Guiblet, Wilfried
AU - Makova, Kateryna D.
AU - Nekrutenko, Anton
PY - 2016/8/26
Y1 - 2016/8/26
N2 - Duplex sequencing was originally developed to detect rare nucleotide polymorphisms normally obscured by the noise of high-throughput sequencing. Here we describe a new, streamlined, reference-free approach for the analysis of duplex sequencing data. We show the approach performs well on simulated data and precisely reproduces previously published results and apply it to a newly produced dataset, enabling us to type low-frequency variants in human mitochondrial DNA. Finally, we provide all necessary tools as stand-alone components as well as integrate them into the Galaxy platform. All analyses performed in this manuscript can be repeated exactly as described at http://usegalaxy.org/duplex.
AB - Duplex sequencing was originally developed to detect rare nucleotide polymorphisms normally obscured by the noise of high-throughput sequencing. Here we describe a new, streamlined, reference-free approach for the analysis of duplex sequencing data. We show the approach performs well on simulated data and precisely reproduces previously published results and apply it to a newly produced dataset, enabling us to type low-frequency variants in human mitochondrial DNA. Finally, we provide all necessary tools as stand-alone components as well as integrate them into the Galaxy platform. All analyses performed in this manuscript can be repeated exactly as described at http://usegalaxy.org/duplex.
UR - http://www.scopus.com/inward/record.url?scp=84983604677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84983604677&partnerID=8YFLogxK
U2 - 10.1186/s13059-016-1039-4
DO - 10.1186/s13059-016-1039-4
M3 - Article
C2 - 27566673
AN - SCOPUS:84983604677
VL - 17
JO - Genome Biology
JF - Genome Biology
SN - 1474-7596
IS - 1
M1 - 180
ER -