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):
bash
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:
bash
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:
bash
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):
bash
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):
bash
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:
- Run NGS Sniff after basecalling/demultiplexing.
- If adapters/low-quality flagged → auto-run trimming and re-check.
- If contamination above threshold → flag sample for manual review and optional alignment-based confirmation.
- 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