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Sensitivity of Noninvasive Prenatal Detection of Fetal Aneuploidy from Maternal Plasma Using Shotgun Sequencing Is Limited Only by Counting Statistics

Overview of attention for article published in PLOS ONE, May 2010
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Title
Sensitivity of Noninvasive Prenatal Detection of Fetal Aneuploidy from Maternal Plasma Using Shotgun Sequencing Is Limited Only by Counting Statistics
Published in
PLOS ONE, May 2010
DOI 10.1371/journal.pone.0010439
Pubmed ID
Authors

H. Christina Fan, Stephen R. Quake

Abstract

We recently demonstrated noninvasive detection of fetal aneuploidy by shotgun sequencing cell-free DNA in maternal plasma using next-generation high throughput sequencer. However, GC bias introduced by the sequencer placed a practical limit on the sensitivity of aneuploidy detection. In this study, we describe a method to computationally remove GC bias in short read sequencing data by applying weight to each sequenced read based on local genomic GC content. We show that sensitivity is limited only by counting statistics and that sensitivity can be increased to arbitrary precision in sample containing arbitrarily small fraction of fetal DNA simply by sequencing more DNA molecules. High throughput shotgun sequencing of maternal plasma DNA should therefore enable noninvasive diagnosis of any type of fetal aneuploidy.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 195 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Indonesia 2 1%
United Kingdom 2 1%
Ireland 1 <1%
Brazil 1 <1%
Sweden 1 <1%
Germany 1 <1%
Canada 1 <1%
Korea, Republic of 1 <1%
Other 2 1%
Unknown 180 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 57 29%
Student > Ph. D. Student 31 16%
Student > Master 24 12%
Other 15 8%
Student > Bachelor 14 7%
Other 34 17%
Unknown 20 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 84 43%
Biochemistry, Genetics and Molecular Biology 38 19%
Medicine and Dentistry 21 11%
Engineering 9 5%
Mathematics 6 3%
Other 15 8%
Unknown 22 11%