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Assessing the Impact of Data Preprocessing on Analyzing Next Generation Sequencing Data.

ABSTRACT: Data quality control and preprocessing are often the first step in processing next-generation sequencing (NGS) data of tumors. 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, by comparing data analysis results of preprocessing with Cutadapt, FastP, Trimmomatic, and raw sequencing data, we found that the frequency of mutation detection had some fluctuations and differences, and human leukocyte antigen (HLA) typing directly resulted in erroneous results. We think that our research had demonstrated the impact of data preprocessing steps on downstream data analysis results. We hope that it can promote the development or optimization of better data preprocessing methods, so that downstream information analysis can be more accurate.

SUBMITTER: He B

PROVIDER: S-EPMC7409520 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Assessing the Impact of Data Preprocessing on Analyzing Next Generation Sequencing Data.

He Binsheng B Zhu Rongrong R Yang Huandong H Lu Qingqing Q Wang Weiwei W Song Lei L Sun Xue X Zhang Guandong G Li Shijun S Yang Jialiang J Tian Geng G Bing Pingping P Lang Jidong J

Frontiers in bioengineering and biotechnology 20200730

Data quality control and preprocessing are often the first step in processing next-generation sequencing (NGS) data of tumors. 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, by comparing data analysis results of preprocessing with Cutadapt, FastP, Trimmomatic, and raw sequencing data, we found that the frequency of mutation detection had some fluctuations and differences, and human leukocyte ...[more]

PMID: 32850708

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