Streamlined analysis of duplex sequencing data with Du Novo
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.
Type
Publication
Genome Biology
Authors
Ph.D. student
My focus is on variation in mitochondrial DNA. I develop methods
and software tools to analyze mitochondrial sequencing data, as
well as data from other non-diploid systems like viral populations,
cancer, and somatic variation.
Authors
Postdoctoral scholar
In Prof. Makova’s lab Barbara was interested in the accurate measurement of rare mutations. Her project was focused on the inheritance of mitochondrial DNA; particularly, analysis of de novo mutations in the germ line, as well as the inheritance of low-level variants.
Authors
Ph.D. student
I am interested in the relationship between genomics and human
physiology. My current project is to relate the different type of
DNA polymerases and their variability to specific mutation patterns.
Authors
Professor
Kateryna is interested in genomics, evolution, and human genetics.
Her lab studies mutations using both computational and experimental
approaches. Additional topics of interest include sex chromosome
evolution and genomics of childhood obesity.
Authors
Prof. Nekrutenko performed postdoctoral work at the University of Chicago where he found his interest in computational genomics. After moving to the Pennsylvania State University as an Assistant Professor, he and his collaborators developed the Galaxy platform for analysis of biological data. He is now a professor of biochemistry and molecular biology and founding member of galaxyproject.org.