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Multi-perspective quality control of Illumina RNA sequencing data analysis
Author(s) -
Quanhu Sheng,
Kasey C. Vickers,
Shilin Zhao,
Jing Wang,
David C. Samuels,
Olivia I. Koues,
Yu Shyr,
Yan Guo
Publication year - 2016
Publication title -
briefings in functional genomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.22
H-Index - 67
eISSN - 2041-2647
pISSN - 2041-2649
DOI - 10.1093/bfgp/elw035
Subject(s) - rna , rna seq , biology , computational biology , perspective (graphical) , rna editing , genetics , gene , gene expression , computer science , transcriptome , artificial intelligence
Quality control (QC) is a critical step in RNA sequencing (RNA-seq). Yet, it is often ignored or conducted on a limited basis. Here, we present a multi-perspective strategy for QC of RNA-seq experiments. The QC of RNA-seq can be divided into four related stages: (1) RNA quality, (2) raw read data (FASTQ), (3) alignment and (4) gene expression. We illustrate the importance of conducting QC at each stage of an RNA-seq experiment and demonstrate our recommended RNA-seq QC strategy. Furthermore, we discuss the major and often neglected quality issues associated with the three major types of RNA-seq: mRNA, total RNA and small RNA. This RNA-seq QC overview provides comprehensive guidance for researchers who conduct RNA-seq experiments.

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