In a clinical genetics lab, a technician is staring at a screen, scanning fresh sequencing data—tens of millions of reads, a line of colored peaks representing one of the four DNA nucleotides—searching for a faint somatic variant hidden in the heterogeneous mess that is a cancer genome. Occasionally, there’s a small bump overlapping the peak, indicating the detection of a different nucleotide in that same position. The lab tech squints, trying to assess if that little bump is indeed a real mutation or just a sequencing artifact. If incorrect, the answer could influence a child’s diagnosis and treatment, which could mean the difference between life and death.

That single uncertainty, distinguishing a true mutation from a technical artifact amid the chaos of cancer genomes, defines much of

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