Dataset Information

Quantification of designer nuclease induced mutation rates: a direct comparison of different methods.

ABSTRACT: Designer nucleases are broadly applied to induce site-specific DNA double-strand breaks (DSB) in genomic DNA. These are repaired by nonhomologous end joining leading to insertions or deletions (in/dels) at the respective DNA-locus. To detect in/del mutations, the heteroduplex based T7-endonuclease I -assay is widely used. However, it only provides semi-quantitative evidence regarding the number of mutated alleles. Here we compared T7-endonuclease I- and heteroduplex mobility assays, with a quantitative polymerase chain reaction mutation detection method. A zinc finger nuclease pair specific for the human adeno-associated virus integration site 1 (AAVS1), a transcription activator-like effector nuclease pair specific for the human DMD gene, and a zinc finger nuclease- and a transcription activator-like effector nuclease pair specific for the human CCR5 gene were explored. We found that the heteroduplex mobility assays and T7-endonuclease I - assays detected mutations but the relative number of mutated cells/alleles can only be estimated. In contrast, the quantitative polymerase chain reaction based method provided quantitative results which allow calculating mutation and homologous recombination rates in different eukaryotic cell types including human peripheral blood mononuclear cells. In conclusion, our quantitative polymerase chain reaction based mutation detection method expands the array of methods for in/del mutation detection and facilitates quantification of introduced in/del mutations for a genomic locus containing a mixture of mutated and unmutated DNA.

SUBMITTER: Ehrke-Schulz E

PROVIDER: S-EPMC4934480 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Quantification of designer nuclease induced mutation rates: a direct comparison of different methods.

Ehrke-Schulz Eric E Bergmann Thorsten T Schiwon Maren M Doerner Johannes J Saydaminova Kamola K Lieber Andre A Ehrhardt Anja A

Molecular therapy. Methods & clinical development 20160706

Designer nucleases are broadly applied to induce site-specific DNA double-strand breaks (DSB) in genomic DNA. These are repaired by nonhomologous end joining leading to insertions or deletions (in/dels) at the respective DNA-locus. To detect in/del mutations, the heteroduplex based T7-endonuclease I -assay is widely used. However, it only provides semi-quantitative evidence regarding the number of mutated alleles. Here we compared T7-endonuclease I- and heteroduplex mobility assays, with a quant ...[more]

PMID: 27419195

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Project description:Somatic nucleotide mutations can contribute to cancer cell survival, proliferation, and pathogenesis. Although research has focused on identifying which mutations are "drivers" versus "passengers," quantifying the proliferative effects of specific variants within clinically relevant contexts could reveal novel aspects of cancer biology. To enable researchers to estimate these cancer effects, we developed cancereffectsizeR, an R package that organizes somatic variant data, facilitates mutational signature analysis, calculates site-specific mutation rates, and tests models of selection. Built-in models support effect estimation from single nucleotides to genes. Users can also estimate epistatic effects between paired sets of variants, or design and test custom models. The utility of cancer effect was validated by showing in a pan-cancer dataset that somatic variants classified as likely pathogenic or pathogenic in ClinVar exhibit substantially higher effects than most other variants. Indeed, cancer effect was a better predictor of pathogenic status than variant prevalence or functional impact scores. In addition, the application of this approach toward pairwise epistasis in lung adenocarcinoma showed that driver mutations in BRAF, EGFR, or KRAS typically reduce selection for alterations in the other two genes. Companion reference data packages support analyses using the hg19 or hg38 human genome builds, and a reference data builder enables use with any species or custom genome build with available genomic and transcriptomic data. A reference manual, tutorial, and public source code repository are available at https://townsend-lab-yale.github.io/cancereffectsizeR. Comprehensive estimation of cancer effects of somatic mutations can provide insights into oncogenic trajectories, with implications for cancer prognosis and treatment.SignificanceAn R package provides streamlined, customizable estimation of underlying nucleotide mutation rates and of the oncogenic and epistatic effects of mutations in cancer cohorts.

Dataset Information

Quantification of designer nuclease induced mutation rates: a direct comparison of different methods.

Publications

Quantification of designer nuclease induced mutation rates: a direct comparison of different methods.

Similar Datasets

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