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Table 1 Whole genome amplification: recent improvements

From: Eleven grand challenges in single-cell data science

Recent improvements of whole genome amplification (WGA) methods promise to reduce amplification biases and errors, while scaling throughput to larger cell numbers:
1. Improved coverage uniformity for multiple displacement amplification (MDA) has been achieved using droplet microfluidics-based methods (eWGA [28]; sd-MDA [29]; ddMDA [30]). A second approach has been to couple the Φ29 DNA polymerase to a primase to reduce priming bias [31].
2. One way to reduce the amplification error rate of the polymerase chain reaction (PCR)-based methods (including multiple annealing and looping-based amplification cycles (MALBAC)) would be to employ a thermostable polymerase (necessary for use in PCR) with proof-reading activity similar to Φ29 DNA polymerase, but we are not aware of any PCR DNA polymerases with a fidelity in the range of Φ29 DNA polymerase [32].
3. Three newer methods use an entirely different approach: they randomly insert transposons into the whole genome and then leverage these as priming sites for amplification and library preparation. Transposon Barcoded (TnBC) library preparation (with a PCR amplification, [33]) and direct library preparation (DLP) (with a shallow library without any amplification, [34]) allow only for copy number variation (CNV) calling, but DLP scales up to 80,000 single cells [35]. Linear—as opposed to exponential—Amplification via Transposon Insertion (LIANTI, [36]) also addresses amplification errors: all copies are generated based on the original genomic DNA through in vitro transcription. With errors unique to individual barcoded copies, the authors report a false positive rate that is even lower than for MDA [36].