Advancements in genomic research have been dramatically propelled by the advent of next-generation sequencing (NGS) technologies, enabling the analysis of genomes, transcriptomes, and epigenomes with unparalleled precision and throughput. Nonetheless, the fidelity and dependability of subsequent analyses are profoundly contingent upon the quality of the raw sequencing data. Consequently, the implementation of robust quality control (QC) measures throughout the NGS workflow assumes paramount significance to ensure the generation of high-quality data right from the outset.
The challenges inherent in achieving optimal NGS data quality control are multifaceted and time-consuming, owing to various factors that can potentially influence the quality of raw sequencing data. These factors encompass the initial quality of the samples, intricacies associated with library preparation, the choice of sequencing platform, and the depth of sequencing. To surmount these challenges, meticulous quality control and pre-processing of sequencing data assume critical roles in identifying and mitigating potential issues that might compromise the accuracy of downstream analyses.
The domain of targeted NGS, which involves the enrichment of specific genomic regions of interest, allows for focused analysis of the desired genetic variants. Within this context, the enforcement of QC measures during sample preparation and the creation of target enrichment libraries assumes paramount importance in ensuring the integrity and accuracy of the sequencing data. These QC measures encompass a comprehensive assessment of the quality of raw reads, detection and elimination of splice contamination, as well as the removal of low-quality reads.
Assessing the Quality of Raw Reads
The initial step in the QC process involves the assessment of various data quality metrics, which furnish comprehensive information about the overall quality of the raw sequencing data. Tools such as FastQC are commonly employed to evaluate crucial metrics such as read length, sequencing depth, base quality, and GC content. By scrutinizing these metrics, researchers can identify potential problems that may exert an adverse impact on downstream analyses, such as the presence of low-quality bases or sequence bias.
Detection and Removal of Adapter Contamination
A prevalent issue encountered during the sequencing process is adapter contamination, which arises when adapter sequences utilized in library preparation remain unremoved within the sequencing data. Adapter contamination can engender false positives and compromise the accuracy of subsequent analyses. To mitigate this problem, tools such as Trimmomatic and Cutadapt are frequently employed to detect and excise adapter sequences from the reads. The elimination of adapter contamination serves to enhance the accuracy of variant calling and other downstream analyses.
Remove Low-Quality Reads
The presence of low-quality reads, characterized by sequencing errors, can exert a substantial influence on the accuracy of downstream analyses. Errors such as base detection errors, phasing errors, and insertion-deletion errors can be effectively mitigated through the elimination of low-quality reads from the dataset. Tools such as Trimmomatic and Cutadapt are deployed to discard reads based on quality score thresholds, thereby augmenting the reliability of subsequent analysis steps.
Sequencing quality control must be performed at each stage of the NGS workflow to ensure the generation of high-quality data. The QC steps can be categorized into three phases: pre-sequencing, in-sequencing, and post-sequencing.
Pre-sequencing QC involves the evaluation of the quality and integrity of the starting material, be it DNA or RNA samples. This phase encompasses the assessment of sample quality, quantification of DNA or RNA, and the detection of potential contaminants. By conducting pre-sequencing QC, researchers can identify and address any issues that might impact the quality of the sequencing data.
During sequencing QC, researchers continuously monitor the sequencing process itself to ensure the production of high-quality data. This involves the assessment of sequencing metrics such as read quality, sequencing depth, and error rate. Additionally, periodic checks are conducted to detect any technical anomalies that may arise during the sequencing run, thereby ensuring the generation of reliable data.
Post-sequencing QC is performed after the completion of the sequencing run. In this phase, the raw sequencing data is meticulously analyzed to identify and eliminate low-quality reads, splice contamination, and other potential artifacts. This step guarantees that the final dataset, intended for downstream analysis, exhibits high quality and is devoid of potential bias or errors.