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Nat Commun. 2015 Apr 16;6:6822. doi: 10.1038/ncomms7822.

Calibrating genomic and allelic coverage bias in single-cell sequencing.

Zhang CZ#1,2, Adalsteinsson VA#2,3,4, Francis J1,2, Cornils H5,6, Jung J2, Maire C1, Ligon KL1,7,8,9,10, Meyerson M1,2,7,11, Love JC2,3,4.

Author information

1
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
2
Cancer Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.
3
Department of Chemical Engineering Cambridge, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
4
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
5
Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
6
Department of Cell Biology, Harvard Medical School, Boston, Massachusetts 02115, USA.
7
Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA.
8
Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.
9
Department of Pathology, Boston Children's Hospital, Boston, Massachusetts 02115, USA.
10
Center for Molecular Oncologic Pathology, Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA.
11
Center for Cancer Genome Discovery, Dana Farber Cancer Institute, Boston, Massachusetts 02215, USA.
#
Contributed equally

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.

PMID:
25879913
PMCID:
PMC4922254
DOI:
10.1038/ncomms7822
[Indexed for MEDLINE]
Free PMC Article

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