Single-molecule resolution and fidelity to fully characterize haplotypes
The standard genotyping arrays, Sanger and short-read sequencing commonly used in cancer research can identify mutations in a region of interest but fail to reveal their arrangement as haplotypes. Considering that mutations can be several 100–1000 bp apart, even within a single gene, examining single long DNA molecules in samples greatly facilitates haplotype phasing.
Current methods for experimental phasing generally magnify the costs for genotyping or sequencing, require complex designs and take time. Our collaborators at SciLifeLab, Uppsala University and Cancer Center at Karolinska Institute demonstrate that Xdrop phases haplotypes in human cancer samples easier and quicker.
To assess Xdrop in phasing sequence variants, our colleagues sequenced the tumor suppressor gene TP53 in commercial cancer cell lines and samples from chronic lymphocytic leukemia (CLL) patients. With the Samplix primer design tool, they created 5 primer sets to amplify short Detection Sequences at several points in a 70 kb region that included the TP53 gene. This amplification allowed the indirect capture of long DNA molecules that collectively covered the complete length of TP53. The five-plex enrichment produced deep sequencing coverage across the entire gene. Download the scientific poster to compare coverage using a single primer set for enrichment.
Our colleagues generated a 5 kb PacBio library from the DNA enriched with Xdrop and sequenced it on a PacBio RSII instrument. The high fidelity of the consensus sequences allowed them to phase 3 mutations – 913A>T (nonsense), 673-2A>T (splice site), 701A>G (missense) – and the normal wildtype (WT) in a single patient sample. In the figure, each grey bar is a consensus read. Colored lines indicate a mutation and orange boxes show the locations of the 3 mutations. None of the point mutations occurred in the same consensus read. That is, they are present on separate alleles in the heterogenous cell population of a single patient.
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