A new whole-genome amplification method, MALBAC, enables accurate detection of single nucleotide variations (SNVs) and copy number variations (CNVs) in single human cells, and allows direct measurement of mutation rates. MALBAC reduces amplification bias, achieving high genome coverage (up to 93%) with 25x sequencing depth. It outperforms other methods like MDA in genome coverage uniformity. The method involves preamplification with random primers, followed by exponential amplification using PCR. MALBAC provides more accurate SNV detection and lower false positive rates compared to MDA. It also allows the identification of newly acquired SNVs in a cancer cell line, revealing a mutation rate of ~2.5 nucleotides per cell generation. The study shows that transitions are not favored over transversions in newly acquired SNVs, suggesting a unique mutation mechanism. MALBAC enables precise characterization of genomic variations, shedding light on the heterogeneity and dynamics of single-cell genomes. The method is applicable to various single-cell samples, including prenatal testing, circulating tumor cells, and forensic specimens. The results highlight the importance of single-cell genomics in understanding cancer evolution and genetic diversity.A new whole-genome amplification method, MALBAC, enables accurate detection of single nucleotide variations (SNVs) and copy number variations (CNVs) in single human cells, and allows direct measurement of mutation rates. MALBAC reduces amplification bias, achieving high genome coverage (up to 93%) with 25x sequencing depth. It outperforms other methods like MDA in genome coverage uniformity. The method involves preamplification with random primers, followed by exponential amplification using PCR. MALBAC provides more accurate SNV detection and lower false positive rates compared to MDA. It also allows the identification of newly acquired SNVs in a cancer cell line, revealing a mutation rate of ~2.5 nucleotides per cell generation. The study shows that transitions are not favored over transversions in newly acquired SNVs, suggesting a unique mutation mechanism. MALBAC enables precise characterization of genomic variations, shedding light on the heterogeneity and dynamics of single-cell genomes. The method is applicable to various single-cell samples, including prenatal testing, circulating tumor cells, and forensic specimens. The results highlight the importance of single-cell genomics in understanding cancer evolution and genetic diversity.