Quality Control (DNA/RNA)

Quality Control for DNA and RNA Sequencing Data

Essential preprocessing workflows for quality control, trimming, and contamination removal of DNA and RNA sequencing data.

Description

MetaGEAR provides two quality control workflows for different types of sequencing data:

  • qc_dna - Quality control for DNA metagenomic sequencing data
  • qc_rna - Quality control for RNA sequencing (metatranscriptomic) data

Both workflows follow the same general process and accept identical parameters. The key difference is that qc_rna includes an additional rRNA removal step to filter out ribosomal RNA sequences, which is essential for metatranscriptomic analysis.

Common Processing Steps

Both workflows include:

  • Quality assessment - Initial quality metrics and statistics
  • Adapter trimming - Removal of sequencing adapters
  • Quality trimming - Removal of low-quality bases
  • Contamination removal - Removal of host and other contaminant sequences using Kneaddata
  • Final quality assessment - Post-processing quality metrics

RNA-Specific Processing

The qc_rna workflow includes one additional step:

  • rRNA removal - Removal of ribosomal RNA sequences (essential for metatranscriptomic data)

Parameters

Required Parameters

Parameter Type Description
--input File path Input .csv file for quality control (same format for both workflows)

Global Parameters

Parameter Required Default Description
--outdir No results Output directory
--help No - Display help information
--debug No false Enable debug mode
--config No - Custom configuration file

Syntax 

# For DNA sequencing data
metagear qc_dna --input INPUT_FILE [GLOBAL_OPTIONS]

# For RNA sequencing data
metagear qc_rna --input INPUT_FILE [GLOBAL_OPTIONS]

Examples

Basic Usage 

# Run DNA quality control with default settings
metagear qc_dna --input samples.csv

# Run RNA quality control with default settings
metagear qc_rna --input rna_samples.csv

# Run with custom output directory
metagear qc_dna --input samples.csv --outdir dna_qc_results
metagear qc_rna --input rna_samples.csv --outdir rna_qc_results

# Enable debug mode for troubleshooting
metagear qc_dna --input samples.csv --debug
metagear qc_rna --input rna_samples.csv --debug

Preview Mode 

# Generate DNA QC script without executing
metagear qc_dna --input samples.csv --preview

# Generate RNA QC script without executing
metagear qc_rna --input rna_samples.csv --preview

This will create a metagear_qc_dna.sh or metagear_qc_rna.sh script that can be reviewed and executed manually.

Input Format

Both workflows use identical input CSV file formats. The input CSV file should contain sample information with the following columns:

sample,fastq_1,fastq_2
SAMPLE-01,/path/to/sample1_R1.fastq.gz,/path/to/sample1_R2.fastq.gz
SAMPLE-02,/path/to/sample2_R1.fastq.gz,/path/to/sample2_R2.fastq.gz
SAMPLE-03,/path/to/sample3_R1.fastq.gz,/path/to/sample3_R2.fastq.gz

Column Descriptions

  • sample: Unique sample identifier
  • fastq_1: Path to forward reads (R1) FASTQ file
  • fastq_2: Path to reverse reads (R2) FASTQ file

Output

Both workflows generate identical output directory structures. The workflow generates the following outputs in the specified directory structure:

outdir/
├── fastqc/                      # Raw and clean read quality reports
│   ├── {sample}_fastqc.html     # Individual FastQC HTML reports (raw)
│   ├── {sample}_fastqc.zip      # FastQC data files (raw)
│   ├── {sample}_clean_fastqc.html   # Individual FastQC HTML reports (clean)
│   └── {sample}_clean_fastqc.zip    # FastQC data files (clean)
├── trimgalore/                  # Adapter and quality trimmed reads
│   ├── {sample}_1_val_1.fq.gz   # Trimmed forward reads
│   ├── {sample}_2_val_2.fq.gz   # Trimmed reverse reads
│   ├── {sample}_1.fastq.gz_trimming_report.txt  # Trimming reports
│   └── {sample}_2.fastq.gz_trimming_report.txt
├── kneaddata/                   # Host/contamination removal results
│   ├── {sample}_paired_1.fastq.gz  # Final cleaned forward reads
│   ├── {sample}_paired_2.fastq.gz  # Final cleaned reverse reads
│   ├── {sample}_kneaddata.log       # Detailed processing log
│   └── {sample}_kneaddata_stats.csv # Read count statistics
├── multiqc/                     # Consolidated quality control reports
│   ├── multiqc_report.html      # Main QC summary report
│   ├── multiqc_data/            # Parsed statistics and data
│   └── multiqc_plots/           # Static plot images
└── pipeline_info/               # Pipeline execution metadata
    ├── execution_report.html    # Nextflow execution report
    ├── execution_timeline.html  # Processing timeline
    └── execution_trace.txt      # Resource usage tracking

Key Output Files

Final cleaned reads for downstream analysis:

  • kneaddata/{sample}_paired_1.fastq.gz - Host-decontaminated forward reads
  • kneaddata/{sample}_paired_2.fastq.gz - Host-decontaminated reverse reads

Quality assessment reports:

  • multiqc/multiqc_report.html - Comprehensive QC summary across all samples
  • fastqc/{sample}_clean_fastqc.html - Post-processing quality metrics per sample
  • kneaddata/{sample}_kneaddata_stats.csv - Read count statistics (raw, trimmed, decontaminated, final)

Processing logs and intermediate files:

  • trimgalore/{sample}_*_trimming_report.txt - Adapter trimming statistics
  • kneaddata/{sample}_kneaddata.log - Detailed contamination removal log
  • trimgalore/{sample}_*_val_*.fq.gz - Quality and adapter trimmed reads (intermediate)

RNA-Specific Considerations

For qc_rna workflows, the output files contain RNA-seq data with rRNA sequences removed. This additional processing step means:

  • Processing time is typically longer due to the rRNA removal step
  • Final read counts may be significantly lower due to rRNA filtering
  • The cleaned reads are optimized for metatranscriptomic functional analysis

Prerequisites

Before running either workflow:

  1. Install databases: Run metagear download_databases first
  2. Prepare input file: Ensure all FASTQ file paths are correct and accessible
  3. Check disk space: Quality control can generate substantial intermediate files
  4. For RNA workflows: Ensure rRNA reference databases are available and properly configured

Notes

General Notes

  • Both workflows automatically detect paired-end vs single-end reads
  • Host contamination databases must be properly configured for your sample type
  • Intermediate files are preserved for quality assessment and troubleshooting
  • Processing time depends on input file sizes and available computational resources

DNA-Specific Notes (qc_dna)

  • Optimized for metagenomic DNA sequencing data
  • Focuses on host DNA contamination removal
  • Suitable for taxonomic profiling and functional analysis

RNA-Specific Notes (qc_rna)

  • Optimized for metatranscriptomic RNA sequencing data
  • Includes rRNA removal which is crucial for downstream functional analysis
  • Processing time is typically longer due to the additional rRNA removal step
  • rRNA removal may significantly reduce final read counts (this is expected and beneficial)
  • Host contamination removal may be less stringent for certain RNA sample types

Choosing Between qc_dna and qc_rna

  • Use qc_dna for:
    • Metagenomic DNA sequencing data
    • Whole genome shotgun sequencing
    • Amplicon sequencing data
  • Use qc_rna for:
    • Metatranscriptomic RNA sequencing data
    • Any RNA-seq data where rRNA removal is needed
    • Functional gene expression analysis

← Back to Workflows Overview