📊 Quality control of sequencing data 💡

Sequencing data is the backbone of modern genomics research, but its quality can make or break your analysis. 🛠️ Before diving into downstream tasks, it’s crucial to perform quality control (QC). QC ensures that the data is clean and free from artifacts like low-quality reads, adapters, or contaminants. 🧫 First, use tools like FastQC to check for basic metrics such as sequence length distribution, GC content, and adapter presence. 📊 If issues arise, trim the sequences using Trimmomatic or cutadapt to remove unwanted parts. 💻 For deeper analysis, consider aligning your data to a reference genome and examining alignment statistics. A high mapping rate indicates good data quality. 🌟 Always visualize your results with tools like MultiQC to get an overview. Proper QC not only improves accuracy but also saves computational resources. 🏆 By mastering this step, you lay a solid foundation for reliable genomic discoveries! ✅