PaSD-qc

PaSD-qc evaluates amplification bias and library quality in single-cell whole-genome sequencing (scWGS) data arising from whole-genome amplification by applying power spectral density analysis to inform quality control.


Key Features:

  • Power Spectral Density Estimation: Employs a modified power spectral density approach to analyze scWGS library properties.
  • Amplification Uniformity and Amplicon Size Distribution: Assesses amplification uniformity and estimates amplicon size distribution across single-cell libraries.
  • Autocovariance and Inter-Sample Consistency: Measures autocovariance in read-density and compares libraries for inter-sample consistency.
  • Quality Assessment Metrics: Produces comprehensive metrics to compare single-cell samples and identify aberrant read-density profiles per chromosome.
  • Chromosomal and Subchromosomal Analysis: Identifies potential chromosomal copy number variations and regions of poor amplification at chromosomal and subchromosomal scales.
  • Variant Calling Support: Provides library-property insights that can be used to refine variant calling strategies.
  • Library Selection from Low-Coverage Data: Facilitates selection of high-quality libraries from low-coverage scWGS data for downstream deep sequencing.

Scientific Applications:

  • Genetic Heterogeneity Analysis: Enables more reliable assessment of genetic heterogeneity in normal and diseased cells by flagging amplification artifacts.
  • Protocol Comparison: Supports comparative evaluation of different scWGS and whole-genome amplification protocols via quantitative QC metrics.
  • Downstream Analysis Optimization: Improves accuracy of downstream analyses, including variant calling and selection of libraries for deep sequencing, by identifying high-quality input data.

Methodology:

Applies a modified power spectral density estimation to read-density profiles to assess amplification uniformity, estimate amplicon size distribution, measure autocovariance, evaluate inter-sample consistency, and detect chromosomes with aberrant read-density.

Topics

Details

Tool Type:
library
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Python
Added:
8/9/2018
Last Updated:
12/10/2018

Operations

Publications

Sherman MA, Barton AR, Lodato MA, Vitzthum C, Coulter ME, Walsh CA, Park PJ. PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation. Nucleic Acids Research. 2017;46(4):e20-e20. doi:10.1093/nar/gkx1195. PMID:29186545. PMCID:PMC5829578.

Documentation