Downsampling Workflow

Status: ✅ Implemented & Fundamental | Tech Stack: Nextflow, Bash Scripting

Problem Solved / Scientific Goal

The objective is to solve the heterogeneity of NGS datasets. Raw data (FASTQ) can vary enormously in terms of sequencing depth, read length, or average quality between different samples, making it impossible to compare them directly with statistically significant analysis.

The solution is standardization: downsample all input FASTQ to a common target depth, ensuring every comparison is *fair* and statistically valid.

Architecture Implemented

The architecture leverages Nextflow to orchestrate the downsampling process. The workflow must:

  1. Input: Accept a list of raw FASTQ files.
  2. Preliminary Quality Check: Perform a preliminary check on the average read length (crucial for deciding the target depth).
  3. Downsampling: Use tools like fastp or scripts based on Samtools/BWA to sample sequences, creating FASTQ files that all have the same number of reads and standardized quality.

Results & Takeaways

  1. Data Quality: I learned that analysis doesn't start with statistics, but with data preprocessing. The first critical step is always standardizing input variables to avoid methodological biases.
  2. Pre-Analysis Best Practices: Creating this utility transforms a manual and high-risk-of-error step into an automated phase of the workflow.

Tech Stack & Tools Used

  • Nextflow Workflow Management
    • Bash Scripting fastp (Downsampling) Samtools/BWA (Implicit)

GitHub Repository

Check out the code: downsampling on GitHub