mirnaQC

mirnaQC evaluates miRNA-seq data quality using 34 distinct parameters to provide comprehensive assessments beyond PhredScore-based filters.

Key Features:

• Automated protocol and species detection: Automatically infers library preparation protocol and species from input data.
• Extensive quality parameters: Evaluates 34 distinct quality parameters, including microRNA yield, degradation products such as rRNA fragments, and adapter-dimer quantification, alongside PhredScore-based metrics.
• Reference-based ranking system: Ranks quality attributes by percentile using a reference distribution derived from over 36,000 publicly available miRNA-seq datasets.
• Flexible input formats: Accepts FASTQ files and SRA accessions as input.
• Assessment of technical artifacts: Detects and reports technical artifacts related to library preparation, sequencing processes, contamination, and yield issues.
• Advanced visualizations: Produces Principal Component Analysis (PCA) and heatmaps for dataset-wide visualization of quality patterns and underlying issues.

Scientific Applications:

• miRNA-seq quality assessment: Provides comprehensive quality metrics to support reliable interpretation of microRNA sequencing experiments.
• Public dataset benchmarking: Enables contextual comparison of samples against a large reference compendium of public miRNA-seq datasets.
• Technical artifact identification: Facilitates detection and characterization of library preparation-, sequencing-, and contamination-related issues in miRNA-seq data.

Methodology:

Automatically infers protocol and species, computes 34 quality parameters (including microRNA yield, rRNA fragments, adapter-dimers, and PhredScore metrics), compares metrics to a >36,000-sample reference distribution to produce percentile ranks, and generates PCA and heatmaps from FASTQ files or SRA accessions.