Reads QC ‐ strategies for reads processing

Read quality Control is fundamental when examining reads before mapping them. Here, it is executed with standard tools, FastQC and MultiQC, to ensure standard data processing.

Tips and preprocessing steps

Reads merging into one file

Reads merging is suitable for small datasets with only a few samples, as the steps are straightforward and manageable. However, handling this process becomes more critical as the number of samples increases. In large-scale sequencing projects, where hundreds or thousands of samples may be involved, the following points are crucial:

• The automated job list ensures that merging tasks are organized and reproducible.
• Computational efficiency is achieved by parallelizing the merging steps, making the process scalable to high-throughput needs.
• Unified sample files reduce complexity in downstream analyses and minimize storage redundancy.

I have created a script that handles read merging based on pattern so that the merged files can be used more efficiently in this pipeline.