Technology-specific error signatures in the 1000 Genomes Project data |
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Authors: | Michael Nothnagel Alexander Herrmann Andreas Wolf Stefan Schreiber Matthias Platzer Reiner Siebert Michael Krawczak Jochen Hampe |
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Institution: | 1.Institute of Medical Informatics and Statistics,Christian-Albrechts University,Kiel,Germany;2.Department of Internal Medicine I,University Hospital Schleswig-Holstein, Christian-Albrechts University,Kiel,Germany;3.Genome Analysis, Leibniz Institute for Age Research, Fritz Lipmann Institute,Jena,Germany;4.Institute of Human Genetics,University Hospital Schleswig-Holstein, Christian-Albrechts University,Kiel,Germany |
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Abstract: | Next-generation sequencing (NGS) will likely facilitate a better understanding of the causes and consequences of human genetic
variability. In this context, the validity of NGS-inferred single-nucleotide variants (SNVs) is of paramount importance. We
therefore developed a statistical framework to assess the fidelity of three common NGS platforms. Using aligned DNA sequence
data from two completely sequenced HapMap samples as included in the 1000 Genomes Project, we unraveled remarkably different
error profiles for the three platforms. Compared to confirmed HapMap variants, newly identified SNVs included a substantial
proportion of false positives (3–17%). Consensus calling by more than one platform yielded significantly lower error rates
(1–4%). This implies that the use of multiple NGS platforms may be more cost-efficient than relying upon a single technology
alone, particularly in physically localized sequencing experiments that rely upon small error rates. Our study thus highlights
that different NGS platforms suit different practical applications differently well, and that NGS-based studies require stringent
data quality control for their results to be valid. |
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