Controlling for contamination in re-sequencing studies with a reproducible web-based phylogenetic approach
Apr 1, 2014·
B Dickins, B Rebolledo-Jaramillo, MS-W Su, IM Paul, D Blankenberg, N Stoler, KD Makova, A Nekrutenko
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0 min read
Abstract
Polymorphism discovery is a routine application of next-generation sequencing technology where multiple samples are sent to a service provider for library preparation, subsequent sequencing, and bioinformatic analyses. The decreasing cost and advances in multiplexing approaches have made it possible to analyze hundreds of samples at a reasonable cost. However, because of the manual steps involved in the initial processing of samples and handling of sequencing equipment, cross-contamination remains a significant challenge. It is especially problematic in cases where polymorphism frequencies do not adhere to diploid expectation, for example, heterogeneous tumor samples, organellar genomes, as well as during bacterial and viral sequencing. In these instances, low levels of contamination may be readily mistaken for polymorphisms, leading to false results. Here we describe practical steps designed to reliably detect contamination and uncover its origin, and also provide new, Galaxy-based, readily accessible computational tools and workflows for quality control. All results described in this report can be reproduced interactively on the web as described at http://usegalaxy.org/contamination.
Type
Publication
BioTechniques
Authors
Ian Paul, MD, MSc, graduated from Penn State College of Medicine and completed his residency training at Duke University Medical Center in Durham, N.C. In addition to his responsibilities as a general pediatrician at Penn State Pediatrics and in the newborn nursery, Dr. Paul spends much of his time working on a variety of research projects and clinical trials that seek to improve health care for children and their families. His current research includes studies to prevent obesity and help breastfeeding newborns.
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
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.