ArticleNucleic acids research2017
sCNAphase: using haplotype resolved read depth to genotype somatic copy number alterations from low cellularity aneuploid tumors.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Tracing cancer evolution and heterogeneity using Hi-C.Nature communications · 2023Article
- Accucopy: accurate and fast inference of allele-specific copy number alterations from low-coverage low-purity tumor sequencing data.BMC bioinformatics · 2021Article
- Article
- Deep whole-genome sequencing of 3 cancer cell lines on 2 sequencing platforms.Scientific reports · 2019Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate identification of copy number alterations is an essential step in understanding the events driving tumor progression. While a variety of algorithms have been developed to use high-throughput sequencing data to profile copy number changes, no tool is able to reliably characterize ploidy and genotype absolute copy number from tumor samples that contain less than 40% tumor cells. To increase our power to resolve the copy number profile from low-cellularity tumor samples, we developed a novel approach that pre-phases heterozygote germline single nucleotide polymorphisms (SNPs) in order to replace the commonly used 'B-allele frequency' with a more powerful 'parental-haplotype frequency'. We apply our tool-sCNAphase-to characterize the copy number and loss-of-heterozygosity profiles of four publicly available breast cancer cell-lines. Comparisons to previous spectral karyotyping and microarray studies revealed that sCNAphase reliably identified overall ploidy as well as the individual copy number mutations from each cell-line. Analysis of artificial cell-line mixtures demonstrated the capacity of this method to determine the level of tumor cellularity, consistently identify sCNAs and characterize ploidy in samples with as little as 10% tumor cells. This novel methodology has the potential to bring sCNA profiling to low-cellularity tumors, a form of cancer unable to be accurately studied by current methods.
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