What to Do After FastQC: A Guide to Post-QC Analysis
Once you've run FastQC on your sequencing data, you've taken a crucial step in ensuring its quality. However, the analysis isn't over. FastQC provides you with a detailed snapshot of your data's health, but it doesn't tell you what to do next.
This guide aims to answer the question: What do you do after FastQC? We'll delve into various scenarios based on the results you obtain, providing a roadmap for proceeding with your analysis.
1. Understanding Your FastQC Report
Before we jump into specific actions, let's first understand what different aspects of a FastQC report signify:
- Basic Statistics: These provide information about the read length, total sequence count, GC content, and more. This gives you a general overview of your data.
- Per base sequence quality: This plot helps you identify potential issues like low quality bases at the beginning or end of reads.
- Per sequence GC content: This plot helps identify biases in GC content distribution.
- Per base N content: This plot shows the distribution of 'N' bases (unknown bases) across your reads, which can be indicative of low-quality sequencing.
- Sequence duplication levels: This plot helps identify potential over-representation of specific sequences, which could be due to PCR biases.
- Adapter content: This plot helps detect the presence of adapter sequences that might need to be removed.
2. Interpreting FastQC Findings
Now, let's explore scenarios you might encounter and how to address them:
Scenario 1: Your data looks good!
If your FastQC report reveals no major issues, congratulations! You're ready to proceed with your downstream analysis, such as mapping your reads to a reference genome or performing differential gene expression analysis.
Scenario 2: You encounter issues
If your FastQC report shows potential problems, don't panic! There are ways to address most issues. Here are some common scenarios and their solutions:
- Low quality bases at the beginning/end of reads: Consider trimming these bases using tools like Trimmomatic or Cutadapt.
- High adapter content: Remove adapters using tools like Trimmomatic or Cutadapt.
- High N content: Investigate the cause of the high N content. Is it due to poor sequencing or a specific region of your genome? You may need to consider re-sequencing or filtering out reads with high N content.
- Sequence duplication: Investigate the reason for the duplication. Are there PCR biases? You might need to apply a correction method for PCR duplicates.
- Uneven GC content: This could affect downstream analysis, particularly mapping. Tools like fastq_quality_trimmer can help adjust for GC content biases.
3. Post-QC Analysis: The Next Steps
Once you've addressed the issues identified in your FastQC report, you can proceed with your downstream analysis. Common steps include:
- Read mapping: If your analysis involves a reference genome, align your reads using tools like Bowtie2 or STAR.
- Variant calling: If you're looking for genetic variations, tools like GATK or FreeBayes can be used.
- Gene expression analysis: Analyze gene expression using tools like DESeq2 or edgeR.
4. Always Double Check!
It's crucial to re-run FastQC after any pre-processing steps to ensure your data has been successfully corrected.
5. Don't Overlook the User Guide:
FastQC comes with a comprehensive user guide that provides detailed information about interpreting the reports and troubleshooting specific issues. Don't hesitate to consult it if you have any doubts!
Conclusion
FastQC is a powerful tool for evaluating the quality of your sequencing data. By understanding its reports and taking appropriate action based on the findings, you can ensure the reliability and accuracy of your downstream analysis. Remember, clean and high-quality data is essential for obtaining robust and meaningful results!