Calibrating genomic and allelic coverage bias in single-cell sequencing

Cheng-Zhong Zhang, Viktor A. Adalsteinsson, Joshua Francis, Hauke Cornils, Joonil Jung, Cecile Maire, Keith L. Ligon, Matthew Meyerson () and J. Christopher Love ()
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Cheng-Zhong Zhang: Dana-Farber Cancer Institute
Viktor A. Adalsteinsson: Cancer Program, Broad Institute of Harvard and MIT
Joshua Francis: Dana-Farber Cancer Institute
Hauke Cornils: Dana-Farber Cancer Institute
Joonil Jung: Cancer Program, Broad Institute of Harvard and MIT
Cecile Maire: Dana-Farber Cancer Institute
Keith L. Ligon: Dana-Farber Cancer Institute
Matthew Meyerson: Dana-Farber Cancer Institute
J. Christopher Love: Cancer Program, Broad Institute of Harvard and MIT

Nature Communications, 2015, vol. 6, issue 1, 1-10

Abstract: Abstract Artifacts introduced in whole-genome amplification (WGA) make it difficult to derive accurate genomic information from single-cell genomes and require different analytical strategies from bulk genome analysis. Here, we describe statistical methods to quantitatively assess the amplification bias resulting from whole-genome amplification of single-cell genomic DNA. Analysis of single-cell DNA libraries generated by different technologies revealed universal features of the genome coverage bias predominantly generated at the amplicon level (1–10 kb). The magnitude of coverage bias can be accurately calibrated from low-pass sequencing (∼0.1 × ) to predict the depth-of-coverage yield of single-cell DNA libraries sequenced at arbitrary depths. We further provide a benchmark comparison of single-cell libraries generated by multi-strand displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC). Finally, we develop statistical models to calibrate allelic bias in single-cell whole-genome amplification and demonstrate a census-based strategy for efficient and accurate variant detection from low-input biopsy samples.

Date: 2015
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DOI: 10.1038/ncomms7822

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