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Abstract MotivationAdvances 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. ResultsWe 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 implementationSEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008.
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A unified hypothesis-free feature extraction framework for diverse epigenomic data
Abstract MotivationEpigenetic assays using next-generation sequencing have furthered our understanding of the functional genomic regions and the mechanisms of gene regulation. However, a single assay produces billions of data points, with limited information about the biological process due to numerous sources of technical and biological noise. To draw biological conclusions, numerous specialized algorithms have been proposed to summarize the data into higher-order patterns, such as peak calling and the discovery of differentially methylated regions. The key principle underlying these approaches is the search for locally consistent patterns. ResultsWe propose L0 segmentation as a universal framework for extracting locally coherent signals for diverse epigenetic sources. L0 serves to compress the input signal by approximating it as a piecewise constant. We implement a highly scalable L0 segmentation with additional loss functions designed for sequencing epigenetic data types including Poisson loss for single tracks and binomial loss for methylation/coverage data. We show that the L0 segmentation approach retains the salient features of the data yet can identify subtle features, such as transcription end sites, missed by other analytic approaches. Availability and implementationOur approach is implemented as an R package “l01segmentation” with a C++ backend. Available at https://github.com/boooooogey/l01segmentation.
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- Award ID(s):
- 2238125
- PAR ID:
- 10576798
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics Advances
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2635-0041
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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