Accounting for uncertainty in DNA sequencing data. Issue 2 (February 2015)

• Record Type: Journal Article
• Authors: O'Rawe, Jason A.; Ferson, Scott; Lyon, Gholson J.
• Is Part Of: Trends in genetics. Volume 31:Issue 2(2015)
• Page Start: 61
• Page End: 66
• Publication Date: 2015-02
• Subjects: DNA sequencing -- uncertainty -- sequence errors -- uncertainty accounting; Genetics -- Periodicals; 576.5
• DOI: 10.1016/j.tig.2014.12.002
• Languages: English
• ISSNs: 0168-9525

Abstract

Highlights: We discuss types and sources of uncertainty and error in DNA sequencing data. We describe the state of uncertainty quantification in DNA sequencing analysis. Specifying/propagating uncertainties can be done with existing computational tools. DNA sequencing analyses benefit from robust and honest uncertainty quantifications. Abstract : Science is defined in part by an honest exposition of the uncertainties that arise in measurements and propagate through calculations and inferences, so that the reliabilities of its conclusions are made apparent. The recent rapid development of high-throughput DNA sequencing technologies has dramatically increased the number of measurements made at the biochemical and molecular level. These data come from many different DNA-sequencing technologies, each with their own platform-specific errors and biases, which vary widely. Several statistical studies have tried to measure error rates for basic determinations, but there are no general schemes to project these uncertainties so as to assess the surety of the conclusions drawn about genetic, epigenetic, and more general biological questions. We review here the state of uncertainty quantification in DNA sequencing applications, describe sources of error, and propose methods that can be used for accounting and propagating these errors and their uncertainties through subsequent calculations.