DNA copy number profiling using single-cell sequencing.
Currently, there is a lack of software for detecting copy number variations and constructing copy number profile for the whole genome from single-cell DNA sequencing data, which are often of low coverage and high technical noises. Here we introduce a new toolkit, SCNV, which features an efficient bin-free segmentation approach and provides the highest resolution possible for breakpoint detection and the subsequent copy number calling. SCNV can auto-tune parameters based on a set of normal cells from the same batch to adjust for the technical noise level of the data, facilitating its application to data gathered from different platforms and different studies.
H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
Xuefeng
X
Wang
Department of Statistics, University of California, Davis, CA.
Hao
H
Chen
Wharton School Statistics Department, University of Pennsylvania.
Nancy R
NR
Zhang
D019295:Computational Biology / Q000379:methods; D056915:DNA Copy Number Variations; D004273:DNA, Neoplasm / Q000235:genetics; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D011003:Ploidies; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data; D059010:Single-Cell Analysis / Q000379:methods / Q000706:statistics & numerical data; D012984:Software
D004273:DNA, Neoplasm