As academic researchers, we know that the analysis of next-generation sequencing (NGS) data can be complex and time-consuming. The quality of NGS data directly affects the accuracy of downstream analyses, making it essential to ensure that high-quality data is generated from the start. This is where quality control (QC) and pre-processing of NGS data come in. In this article, we will discuss the best practices for quality control and pre-processing of NGS data, providing a comprehensive guide for academic researchers.
Introduction
Next-generation sequencing (NGS) technologies have revolutionized genomic research, enabling the analysis of genomes, transcriptomes, and epigenomes with unprecedented resolution and throughput. However, the accuracy of NGS data is dependent on the quality of the raw sequencing data, which can be influenced by various factors, such as sample quality, library preparation, sequencing platform, and sequencing depth. To ensure that accurate results are obtained from NGS data, researchers must conduct quality control (QC) and pre-processing of their data. In this article, we will provide a detailed guide on the best practices for QC and pre-processing of NGS data.
Quality Control of NGS Data
Quality control (QC) is the process of assessing the quality of raw sequencing data to identify any potential problems that may affect downstream analyses. QC involves several steps, including the assessment of data quality metrics, the detection of adapter contamination, and the removal of low-quality reads. To ensure that high-quality data is generated, researchers must perform QC at various stages of the NGS workflow, including after sample preparation, library preparation, and sequencing.
Data Quality Metrics
Assessing the quality of raw sequencing data is an essential step in QC. Quality metrics provide information about the overall quality of the data, such as read length, sequencing depth, base quality, and GC content. Several tools are available to assess data quality metrics, such as FastQC, which provides a comprehensive report of quality metrics for a given set of sequencing reads.
Adapter Contamination
Adapter contamination occurs when adapter sequences used in library preparation are not fully removed from the sequencing data, leading to false positives and reduced accuracy in downstream analyses. Detecting and removing adapter contamination is an important step in QC. Several tools are available to detect adapter contamination, such as Trimmomatic and Cutadapt, which can remove adapter sequences from the reads.
Removal of Low-Quality Reads
Low-quality reads are those that contain sequencing errors, such as base-calling errors, phasing errors, and insertion-deletion errors. These errors can reduce the accuracy of downstream analyses, making it essential to remove low-quality reads from the data. Several tools are available to remove low-quality reads, such as Trimmomatic and Cutadapt, which can remove reads based on quality score thresholds.
Pre-processing of NGS Data
Pre-processing of NGS data involves several steps, including read alignment, transcript quantification, and differential expression analysis. Pre-processing ensures that the data is ready for downstream analyses, such as variant calling, differential expression analysis, and functional annotation.
Read Alignment
Read alignment is the process of mapping the sequencing reads to a reference genome or transcriptome. Several tools are available for read alignment, such as Bowtie, BWA, and STAR, which use different algorithms for mapping reads to the reference. The choice of alignment tool depends on several factors, such as the type of sequencing data, the reference genome, and the downstream analysis.
Transcript Quantification
Transcript quantification is the process of estimating the abundance of transcripts from RNA-seq data. Several tools are available for transcript quantification, such as RSEM, Kallisto, and Salmon, which use different algorithms for estimating transcript abundance. The choice of transcript quantification tool depends on several factors, such as the type of sequencing data, the reference transcriptome, and the downstream analysis.
Differential Expression Analysis
Differential expression analysis is the process of identifying genes that are differentially expressed between two or more conditions. Several tools are available for differential expression analysis, such as DESeq2, edgeR, and limma, which use different statistical models to identify differentially expressed genes. The choice of differential expression analysis tool depends on several factors, such as the type of sequencing data, the experimental design, and the downstream analysis.
Best Practices for QC and Pre-processing of NGS Data
To ensure that high-quality data is generated from NGS experiments, researchers should follow best practices for QC and pre-processing of their data. The following are some of the best practices for QC and pre-processing of NGS data:
Follow Standard Protocols
To ensure reproducibility and comparability of results, researchers should follow standard protocols for sample preparation, library preparation, and sequencing. Standard protocols ensure that the data is of high quality and can be used for downstream analyses.
Conduct QC at Every Stage
QC should be conducted at every stage of the NGS workflow, including sample preparation, library preparation, and sequencing. QC ensures that potential problems are identified and addressed, resulting in high-quality data that can be used for downstream analyses.
Use Multiple QC Tools
To ensure that accurate results are obtained, researchers should use multiple QC tools to assess data quality metrics, detect adapter contamination, and remove low-quality reads. Using multiple QC tools increases the sensitivity and specificity of the QC process, resulting in high-quality data.
Use High-Quality Reference Genomes and Transcriptomes
To ensure accurate read alignment and transcript quantification, researchers should use high-quality reference genomes and transcriptomes. High-quality references ensure that the data is accurately mapped and quantified, resulting in accurate downstream analyses.
Use Standardized Annotation
To ensure comparability of results, researchers should use standardized annotation for downstream analyses, such as variant calling and functional annotation. Standardized annotation ensures that the results can be compared across different studies, resulting in increased scientific understanding.
Use a platform that consolidates these best practices into one place
Understanding how to deploy and run each of these QC tools individually on your data can take time and help from a bioinformatician. If either of these is in short supply for you, consider using a hosted platform (such as Basepair) that makes it easy to use the appropriate QC tool(s) for your specific data type and interpret the results.
Conclusion
Quality control and pre-processing of NGS data are essential steps in ensuring that accurate results are obtained from downstream analyses. Researchers should follow best practices for QC and pre-processing to ensure that high-quality data is generated from their experiments. By using multiple QC tools, following standard protocols, and using high-quality references and standardized annotation, researchers can ensure that their NGS data is of high quality and can be used for downstream analyses.
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