High-quality sequencing reads are essential for impactful genomic research. Mastering sequencing quality control and preprocessing is key to achieving reliable results.
Addresses adapter contamination, PCR duplicates, and quality degradation.
Improves alignment accuracy and downstream insights.
Guides trimming, filtering, and adjustments to optimize outcomes.
Sequence Quality: Visualize quality scores to identify trimming thresholds.
Nucleotide Bias: Detect artifacts like adapter contamination.
Read Duplication Levels: Identify PCR artifacts or duplications.
Overrepresented K-mers: Spot contamination for trimming strategies.
🦏R Tools: Fastqcr, Rqc, ShortRead, QuasR, Biostrings.
🐍Python Tools: Biopython, Pandas, NumPy, Cutadapt.
🦿Standalone Tools: FastQC, Trimmomatic.
Remove low-quality reads and adapter contamination.
Trim degraded read ends for alignment accuracy.
R: Use QuasR or ShortRead for trimming; visualize results with Rqc or fastqcr.
Python: Use Biopython or Cutadapt; enhance pipelines with Pandas or NumPy.
Stream large files with FastqStreamer() or chunk processing.
Integrate with Nextflow or Snakemake for scalability.
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