Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing.
Bioinformatics
; 40(8)2024 08 02.
Article
em En
| MEDLINE
| ID: mdl-39133157
ABSTRACT
MOTIVATION Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline. RESULTS:
We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON uses a Gaussian Mixture Model to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high-quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses. AVAILABILITY AND IMPLEMENTATION SEACON is implemented in Python and is freely available open-source from https//github.com/NabaviLab/SEACON and https//doi.org/10.5281/zenodo.12727008.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Análise de Sequência de DNA
/
Alelos
/
Variações do Número de Cópias de DNA
/
Análise de Célula Única
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article