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|a Zhang, Cheng-Zhong
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|a Massachusetts Institute of Technology. Department of Chemical Engineering
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|a Koch Institute for Integrative Cancer Research at MIT
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|a Love, John C
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|a Adalsteinsson, Viktor A.
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|a Love, John C
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|a Francis, Joshua
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|a Cornils, Hauke
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|a Jung, Joonil
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|a Maire, Cecile
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|a Ligon, Keith L.
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|a Meyerson, Matthew
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|a Adalsteinsson, Viktor A.
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|a Love, John C
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|a Calibrating genomic and allelic coverage bias in single-cell sequencing
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|b Nature Publishing Group,
|c 2017-10-02T14:11:45Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/111665
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|a 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.
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|a National Cancer Institute (U.S.) (Grant P30-CA14051)
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|a en_US
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|a Article
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|t Nature Communications
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