A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes.
IF: 8.713
Cited by: 4


Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor's evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive (FP) mutations when sequencing picogram quantities of DNA. Data pooling to increase the confidence in the discovered mutations, moves the discovery back in the past to a common ancestor. Here we report a robust WGS and analysis pipeline (DigiPico/MutLX) that virtually eliminates all F results while retaining an excellent proportion of true positives. Using our method, we identified, for the first time, a hyper-mutation (kataegis) event in a group of ∼30 cancer cells from a recurrent ovarian carcinoma. This was unidentifiable from the bulk WGS data. Overall, we propose DigiPico/MutLX method as a powerful framework for the identification of clone-specific variants at an unprecedented accuracy.


Spatial Transcriptomics
cancer biology
machine learning
variant calling
whole genome amplified

MeSH terms

Genetic Variation
Genome, Human
Ovarian Neoplasms
Sequence Analysis, DNA
Whole Genome Sequencing


KaramiNejadRanjbar, Mohammad
Sharifzadeh, Sahand
Wietek, Nina C
Artibani, Mara
El-Sahhar, Salma
Sauka-Spengler, Tatjana
Yau, Christopher
Tresp, Volker
Ahmed, Ahmed A

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