Genomic data is the foundation of bioengineering, but it can also be prone to errors, biases, and contamination. Quality control (QC) is the process of checking and improving the accuracy, reliability, and consistency of genomic data before using it for analysis or engineering. In this article, you will learn how to perform QC on genomic data using some common tools and methods.
QC on genomic data usually involves four main steps: assessing the quality of raw reads, trimming and filtering low-quality reads, aligning reads to a reference genome, and evaluating the alignment results. Each step can use different software and parameters depending on the type and source of the data, the sequencing platform, and the research question.
In addition to the given information. Following information need more attention. 1. Right QC threshold identification 2. How does the threshold is decided 3. Howmuch the QC control affects the sensitivity and precision of the analysis 4. Howmuch the QC control affects the business outcome 5. QC control process should be compliant with regulatory bodies 6. Is really the QC parameter relevant for the technology (short read Vs long read) 7. The QC assessment formula 8. QC parameters need to align with the regulatory bodies 9. Periodic proficiency tests should be used to ensure the QC parameters and respective threshold are still valid 10. What are mitigation and exception criteria for QC failed cases?
There are many tools available for QC on genomic data, but some of the most widely used ones are FastQC, Trimmomatic, BWA, and SAMtools. FastQC is a tool that generates a comprehensive report on the quality of raw reads, including metrics such as base quality, read length, GC content, and duplication rate. Trimmomatic is a tool that trims and filters low-quality reads based on various criteria, such as quality score, adapter sequences, and read length. BWA is a tool that aligns reads to a reference genome using an efficient algorithm that can handle large and complex genomes. SAMtools is a tool that manipulates and analyzes the alignment results, such as sorting, indexing, flagging, and calculating statistics.
FastQC: is a widely used tool for quality control of high-throughput sequence data. It provides a detailed analysis of sequence quality, including per-base sequence quality scores, sequence length distribution, GC content, overrepresented sequences, and more. Picard Tools: offers a suite of command-line tools for working with high-throughput sequencing data generated by platforms such as Illumina and Ion Torrent. It includes tools for various QC tasks such as alignment assessment, duplicate marking, and data format conversion. Qualimap: is a tool for assessing the quality of aligned sequencing data. It generates various QC metrics such as coverage distribution, mapping quality, and GC bias, and provides interactive visualizations.
QC on genomic data can pose some challenges, especially when dealing with complex or novel genomes, mixed or contaminated samples, or low-coverage or low-quality data. Some of the common challenges are: choosing an appropriate reference genome, detecting and removing contaminants, identifying and correcting errors or biases, and optimizing the QC parameters for the specific data and analysis. To overcome these challenges, you may need to use additional tools or methods, such as BLAST, Kraken, Picard, or GATK, or consult with experts or guidelines.
QC on genomic data can have many benefits for your bioengineering projects, such as: improving the accuracy and reproducibility of your results, reducing the noise and artifacts in your data, increasing the efficiency and speed of your analysis, and enhancing the confidence and credibility of your findings. QC can also help you identify and solve potential problems or issues in your data or workflow, and provide valuable insights into the characteristics and quality of your data.
To perform QC on genomic data effectively and efficiently, it's important to plan your QC strategy before you start your analysis or engineering. Documenting your QC steps and parameters is essential. You should use multiple tools and methods to cross-check and validate your results, and compare them with existing standards or benchmarks. Visualizing your QC results using plots, graphs, or tables is also important, so that you can interpret them carefully and critically. Additionally, you should keep track of your QC output files and reports, organizing them in a clear and consistent way. Finally, seeking feedback and advice from other bioengineers or bioinformaticians can help you learn from their experiences and best practices.