Open AccessDemystification of RNAseq Quality Control
Author(s) -
Dragana Dudić,
Bojana Banović Đeri,
Vesna Pajić,
Gordana Pavlović-Lažetić
Publication year - 2021
Publication title -
journal of information technology and applications
Language(s) - English
Resource type - Journals
eISSN - 2233-0194
pISSN - 2232-9625
DOI - 10.7251/jit2102073d
Subject(s) - preprocessor , computer science , data mining , quality (philosophy) , context (archaeology) , downstream (manufacturing) , control (management) , dna sequencing , computational biology , artificial intelligence , biology , gene , engineering , genetics , paleontology , philosophy , operations management , epistemology
Next Generation Sequencing (NGS) analysis has become a widely used method for studying the structure of DNA and RNA, but complexity of the procedure leads to obtaining error-prone datasets which need to be cleansed in order to avoid misinterpretation of data. We address the usage and proper interpretations of characteristic metrics for RNA sequencing (RNAseq) quality control, implemented in and reported by FastQC, and provide a comprehensive guidance for their assessment in the context of total RNAseq quality control of Illumina raw reads. Additionally, we give recommendations how to adequately perform the quality control preprocessing step of raw total RNAseq Illumina reads according to the obtained results of the quality control evaluation step; the aim is to provide the best dataset to downstream analysis, rather than to get better FastQC results. We also tested effects of different preprocessing approaches to the downstream analysis and recommended the most suitable approach.