Comparing NGS Sniff with Other NGS QC Tools

NGS Sniff Tutorial: From Raw Reads to Rapid Insights

Overview

NGS Sniff is a lightweight command-line utility for quickly inspecting next-generation sequencing (NGS) data to find common issues and get immediate metrics without running full-scale pipelines. This tutorial walks through a minimal, practical workflow: loading raw FASTQ files, running basic checks, interpreting results, and using outputs to guide next steps.

Prerequisites

• A Unix-like environment (Linux or macOS).
• NGS Sniff installed (assume binary available on PATH).
• FASTQ or compressed FASTQ (.fastq, .fastq.gz) files ready.
• Basic familiarity with the shell.

1. Quick sanity check

Run NGS Sniff on a single FASTQ to get immediate summary statistics (read count, average length, base composition, quality overview):

ngs-sniff sample_R1.fastq.gz

What to expect:

• Total reads and reads retained (if subsampling used).
• Mean/median read length.
• Per-base A/C/G/T percentages.
• Quality score distribution summary.

Use this to confirm file integrity (non-zero reads, expected read length) and obvious adapter/contamination signals (e.g., abnormal base composition at ends).

2. Paired-end mode

For paired-end data, provide both files to get paired-read concordance and insert-size hints:

ngs-sniff -1 sample_R1.fastq.gz -2 sample_R2.fastq.gz

Key outputs:

• Paired read counts and orphan rates.
• Per-read-pair length summaries.
• Early indicators of adapter overlap or large insert-size variability.

High orphan or discordant rates suggest sample prep or demultiplexing issues.

3. Subsampling for speed

For very large files, use subsampling to produce representative results quickly:

ngs-sniff –sample 0.01 sample_R1.fastq.gz

Interpretation:

• 1% subsample gives fast approximations for composition and quality.
• Use full data only when you need precise counts or rare-event detection.

4. Detecting adapters and overrepresented sequences

NGS Sniff reports enriched k-mers and common prefixes/suffixes. Look for:

• Short sequences matching known adapter motifs.
• Overrepresented k-mers indicating contamination (ribosomal, phiX, index bleed).

If adapters are reported, run a trimming step (example with fastp):

fastp -i sample_R1.fastq.gz -I sample_R2.fastq.gz -o trimmed_R1.fastq.gz -O trimmed_R2.fastq.gz

Then re-run NGS Sniff to confirm removal.

5. Quality score issues and filtering recommendations

NGS Sniff flags low average quality or heavy 3’ decline. Actions:

• If overall quality is acceptable but 3’ tails drop, trim bases with a tool like fastp or Trimmomatic.
• If per-base quality is universally low, consider re-sequencing or deeper filtering; downstream alignments will suffer.

Example trimming (fastp):

fastp -i sample_R1.fastq.gz -I sample_R2.fastq.gz -o trimmed_R1.fastq.gz -O trimmed_R2.fastq.gz –trim_front1 3 –cut_right_mean_quality 20

6. Small contamination and index bleed

If NGS Sniff shows low-level but consistent foreign k-mers:

• Cross-check against common contaminants (phiX, bacterial rRNA).
• Use alignment-based checks (e.g., bwa mem to suspected contaminant) on a subsample.
• Consider stricter demultiplexing or additional clean-up steps.

7. Integration into pipelines

NGS Sniff’s concise JSON or text outputs can be parsed to gate downstream steps. Typical integration pattern:

1. Run NGS Sniff after basecalling/demultiplexing.
2. If adapters/low-quality flagged → auto-run trimming and re-check.
3. If contamination above threshold → flag sample for manual review and optional alignment-based confirmation.
4. Otherwise proceed to alignment/assembly.

Automation example (pseudo):

• Exit code 0: pass; submit to aligner.
• Exit code 1: requires trimming; run fastp then re-check.
• Exit code 2: contamination; hold for manual review.

8. Interpreting an example report (quick guide)

• Read count << expected: check file corruption or demultiplexing.
• Read length mismatch: possible mixed libraries or wrong files.
• High A/T or G/C bias at ends: adapter or primer sequence.
• Sharp drop in quality after position X: trim after X.
• Overrepresented sequence mapping to phiX: common spike-in—can be filtered.

9. Best practices

• Always run a quick sniff step immediately after demultiplexing.
• Use subsampling for everyday checks and full-data runs for final QC.
• Combine k-mer signals with quality metrics for robust decisions.
• Store NGS Sniff reports (JSON) for traceability and pipeline audits.

10. Troubleshooting checklist

• Zero reads: verify file path, compression, and integrity (zcat