SeqSQC: A Bioconductor Package for Evaluating the Sample Quality of Next-generation Sequencing Data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) is crucial for identifying genetic variants in diseases.
• Sample quality issues (e.g., gender mismatch, relatedness) significantly impact NGS analysis.
• Existing tools often lack specialized capabilities for sample quality control in large-scale NGS data.

Purpose of the Study:

• To develop an automated and efficient tool for identifying and cleaning problematic samples from NGS data.
• To address the limitations of existing methods in handling large sample sizes and variant counts.
• To provide intuitive visualization for expedited sample quality assessment.

Main Methods:

• Development of SeqSQC, a Bioconductor package.
• Implementation of efficient data storage and access mechanisms.
• Integration of interactive plots for data visualization.

Main Results:

• SeqSQC automates and accelerates sample cleaning for NGS data of any scale.
• The package facilitates the identification of problematic samples, including gender mismatches and population outliers.
• Interactive visualizations aid in the intuitive assessment of sample quality.

Conclusions:

• SeqSQC provides a specialized solution for sample quality control in NGS data.
• The tool enhances the reliability of downstream genetic analyses by ensuring sample integrity.
• SeqSQC is a valuable resource for researchers working with large-scale genomic datasets.