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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
Published on: May 23, 2018
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PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation.
Maxwell A Sherman1, Alison R Barton1, Michael A Lodato2
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Nucleic Acids Research
|November 30, 2017
Summary
PaSD-qc is a new statistical tool that assesses single-cell whole-genome sequencing (scWGS) library quality. It identifies biases from amplification, improving variant calling and data analysis for genetic heterogeneity studies.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Single-cell whole-genome sequencing (scWGS) reveals genetic heterogeneity in cells.
- Whole-genome amplification (WGA) in scWGS introduces biases, complicating downstream analyses.
- Standardized quality assessment for scWGS libraries is crucial for reliable results.
Purpose of the Study:
- To introduce PaSD-qc, a statistical method and software package for evaluating scWGS library quality.
- To provide metrics for assessing amplification uniformity, amplicon characteristics, and sample consistency.
- To identify genomic regions with aberrant read density due to copy alterations or amplification issues.
Main Methods:
- Utilizes a modified power spectral density (PSD) approach.
- Analyzes amplification uniformity, amplicon size distribution, and autocovariance.
- Assesses inter-sample consistency and identifies chromosomes with aberrant read-density profiles.
Main Results:
- PaSD-qc provides standardized metrics for comparing scWGS sample quality.
- The tool can identify chromosomal copy number variations and regions of poor amplification.
- It aids in selecting high-quality libraries from low-coverage data for deep sequencing.
Conclusions:
- PaSD-qc offers a robust method for scWGS data quality control.
- The software enhances the reliability of variant calling and genomic analyses.
- PaSD-qc facilitates improved understanding of genetic heterogeneity through better scWGS data.
