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Figure 1 | Genome Medicine

Figure 1

From: Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine

Figure 1

Somatic mutation detection in tumor samples. DNA-sequence reads from a tumor sample are aligned to a reference genome (shown in gray). Single-nucleotide differences between reads and the reference genome indicate germline single-nucleotide variants (SNVs; green circles), somatic SNVs (red circles), or sequencing errors (black diamonds). (a) In a pure tumor sample, a location containing mismatches or single nucleotide substitutions in approximately half of the reads covering the location indicates a heterozygous germline SNV or a heterozygous somatic SNV - assuming that there is no copy number aberration at the locus. Algorithms for detecting SNVs distinguish true SNVs from sequencing errors by requiring multiple reads with the same single-letter substitution to be aligned at the position (gray boxes). (b) As tumor purity decreases, the fraction of reads containing somatic mutations decreases: cancerous and normal cells, and the reads originating from each, are shown in blue and orange, respectively. The number of reads reporting a somatic mutation decreases with tumor purity, diminishing the signal to distinguish true somatic mutations from sequencing errors. In this example, only one heterozygous somatic SNV and one hetererozygous germline SNV are detected (gray boxes) as the mutation in the middle set of aligned reads is not distinguishable from sequencing errors.

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