Detection of ultra-rare mutations by next-generation sequencing

Abstract

Next-generation DNA sequencing promises to revolutionize clinical medicine and basic research. However, while this technology has the capacity to generate hundreds of billions of nucleotides of DNA sequence in a single experiment, the error rate of ~1% results in hundreds of millions of sequencing mistakes. These scattered errors can be tolerated in some applications but become extremely problematic when "deep sequencing" genetically heterogeneous mixtures, such as tumors or mixed microbial populations. To overcome limitations in sequencing accuracy, we have developed a method termed Duplex Sequencing. This approach greatly reduces errors by independently tagging and sequencing each of the two strands of a DNA duplex. As the two strands are complementary, true mutations are found at the same position in both strands. In contrast, PCR or sequencing errors result in mutations in only one strand and can thus be discounted as technical error. We determine that Duplex Sequencing has a theoretical background error rate of less than one artifactual mutation per billion nucleotides sequenced. In addition, we establish that detection of mutations present in only one of the two strands of duplex DNA can be used to identify sites of DNA damage. We apply the method to directly assess the frequency and pattern of random mutations in mitochondrial DNA from human cells.

Fig. 1.

Overview of Duplex Sequencing. (A) Adapter synthesis. A double-stranded, randomized Duplex Tag sequence is appended to a sequencing adapter by copying a degenerate sequence in one strand of the adapter with DNA polymerase. Complete adapter A-tailing is ensured by extended incubation with polymerase and dATP. (B) Duplex Sequencing workflow. Sheared, T-tailed double-stranded DNA is ligated to A-tailed adapters. Because every adapter contains a Duplex Tag on each end, every DNA fragment becomes labeled with two distinct tag sequences (arbitrarily designated α and β in the single fragment shown). PCR amplification with primers containing Illumina flow-cell–compatible tails is carried out to generate families of PCR duplicates. Two types of PCR products are produced from each DNA fragment. Those derived from one strand will have the α tag sequence adjacent to flow cell sequence 1 and the β tag sequence adjacent to flow cell sequence 2. PCR products originating from the complementary strand are labeled reciprocally. (C) Error correction. (i–iii) Sequence reads sharing a unique set of tags are grouped into paired families with members having strand identifiers in either the αβ or βα orientation. Each family pair reflects the amplification of one double-stranded DNA fragment. (i) Mutations (colored spots) present in only one or a few family members represent sequencing mistakes or PCR-introduced errors occurring late in amplification. (ii) Mutations occurring in many or all members of one family in a pair arise from PCR errors during the first round of amplification such as might occur when copying across sites of mutagenic DNA damage. (iii) True mutations (green) present on both strands of a DNA fragment appear in all members of a family pair. Whereas artifactual mutations may co-occur in a family pair with a true mutation, all except those arising during the first round of PCR amplification can be independently identified and discounted when producing (iv) an error-corrected single-strand consensus sequence (SSCS). The sequences obtained from each of the two strands of an individual DNA duplex can then be compared to obtain (v) the duplex consensus sequence (DCS), which eliminates remaining errors that occurred during the first round of PCR.

Fig. 2.

Duplex Sequencing of M13mp2 DNA. (A) Average mutation frequency of M13mp2 DNA as measured by a standard sequencing approach, SSCS, and DCS. Reference value of 3.0 × 10⁻⁶ is from ref. . Note that the axis is plotted on a split-log scale. (B) Single-strand consensus sequences (SSCSs) reveal a large excess of G→A/C→T and G→T/C→A mutations, whereas duplex consensus sequences (DCSs) yield a balanced spectrum. Mutation frequencies are grouped into reciprocal mispairs, as DCS analysis only scores mutations present in both strands of duplex DNA. All significant (P < 0.05) differences between DCS analysis and the literature reference values are noted. (C) Complementary types of mutations should occur at approximately equal frequencies within a DNA fragment population derived from duplex molecules. However, SSCS analysis yields a 15-fold excess of G→T mutations relative to C→A mutations and an 11-fold excess of C→T mutations relative to G→A mutations. All significant (P < 0.05) differences between paired reciprocal mutation frequencies are noted.

Fig. 3.

Effect of DNA damage on mutation spectrum. DNA damage was induced by incubating purified M13mp2 DNA with hydrogen peroxide and FeSO₄. (A) SSCS analysis reveals a further elevation from baseline of G→T mutations, indicating these events to be the artifactual consequence of nucleotide oxidation. All significant (P < 0.05) changes from baseline mutation frequencies are noted. (B) Induced DNA damage had no effect on the overall frequency or spectrum of DCS mutations.

Fig. 4.

Duplex Sequencing of human mitochondrial DNA. (A) Overall mutation frequency as measured by a standard sequencing approach, SSCS, and DCS. (B) Pattern of mutation in human mitochondrial DNA by a standard sequencing approach. The mutation frequency (vertical axis) is plotted for every position in the ∼16-kb mitochondrial genome. Due to the substantial background of technical error, no obvious mutational pattern is discernible by this method. (C) DCS analysis eliminates sequencing artifacts and reveals the true distribution of mitochondrial mutations to include a striking excess adjacent to the mtDNA origin of replication. (D) SSCS analysis yields a large excess of G→T mutations relative to complementary C→A mutations, consistent with artifacts from damaged-induced 8-oxo-G lesions during PCR. All significant (P < 0.05) differences between paired reciprocal mutation frequencies are noted. (E) DCS analysis removes the SSCS strand bias and reveals the true mtDNA mutational spectrum to be characterized by an excess of transitions.