Evaluating the impact of next generation sequencing data preprocessing on data analysis

Data quality control and preprocessing are often the first step in processing next-generation sequencing (NGS) data. Not only can it help us evaluate the quality of sequencing data but it can also help us obtain high-quality data for downstream data analysis. However, we rarely explain the impact of data preprocessing on the results of the analysis. By comparing the data of Cutadapt, FastP and Trimmomatic pretreatment with original sequencing data, the results of mutation detection and human leukocyte antigen (HLA) typing showed that the data obtained by different treatment methods had different fluctuations in the detection frequency of mutations. HLA typing directly resulted in erroneous results. We hope that the research in this study demonstrates the impact of data preprocessing steps on downstream data analysis results, which can promote the development or optimization of better data preprocessing methods, so that downstream information analysis can be more accurate.