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.
Highlights
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
The fraction of the single-cell’s genome uncovered at a given sequencing depth determines the information content of single-cell sequencing. This measure depends on the uniformity of genome coverage, or the magnitude and spread of whole-genome amplification bias, and is conceptually equivalent to a ‘single-cell DNA library complexity.’
To predict the allele coverage from the locus-level genome coverage, we considered two limiting scenarios: a ‘segregated template model’ (STM) assuming completely independent amplification of homologous chromosomes, and a ‘mixed template model’ (MTM) assuming identical coverage of homologous chromosomes
Summary
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. Current methods to assess the uniformity of WGA rely on either direct visual inspection or various statistical measures of the sequencing coverage at the base level[18,22] or the allele level[5,12] These empirical methods and metrics generally require substantial sequencing (10 Â or greater) and only gauge the deviation of amplified DNA from the ‘uniform’ bulk DNA at a particular sequencing depth. They fail, to characterize the intrinsic non-uniformity resulting from WGA that is independent of sequencing depth (Fig. 1a,b). The nature of the main sources of bias remains poorly characterized (Fig. 1c)
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