MultiQC

MultiQC aggregates and summarizes quality-control metrics from multiple bioinformatics tools to enable detection of global trends, biases, and sample-level outliers in next-generation sequencing (NGS) datasets.

Key Features:

• Result aggregation: Aggregates results from multiple analyses and diverse bioinformatics tools into a single consolidated dataset.
• Cohesive reporting: Compiles outputs into a cohesive HTML report for project-level visualization.
• QC visualization: Visualizes global trends, biases, and quality-control metrics across large sample sets.
• Outlier and batch detection: Facilitates identification of batch effects and outlier samples that may affect downstream analysis.
• Extensibility: Supports extension and customization to incorporate additional tools or project-specific metrics.
• Project-scale assessment: Enables quality-control assessment across large NGS datasets to evaluate many samples collectively.

Scientific Applications:

• NGS project QC: Project-level quality control assessment in next-generation sequencing studies.
• Batch effect identification: Detection of batch effects that can introduce systematic biases in sequencing data.
• Outlier detection: Identification of outlier samples that could compromise data integrity before downstream analyses.
• Comparative QC: Comparative evaluation of QC metrics across samples and outputs from multiple bioinformatics tools to reveal biases and trends.

Methodology:

Aggregates outputs from diverse bioinformatics tools and compiles them into a cohesive HTML report for visualization of QC metrics across samples.