Impact of gene annotation choice on the quantification of RNA-seq data

• Published in: BMC bioinformatics Vol. 23; no. 1; p. 107
• Main Authors: Chisanga, David, Liao, Yang, Shi, Wei
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30.03.2022; BioMed Central; BMC
• Subjects: Accuracy; Analysis; Annotations; Automation; Base Sequence; Computational biology; Data analysis; Datasets; DNA microarrays; Exons; Gene annotation; Gene expression; Gene expression quantification; Gene sequencing; Genes; Genomes; Genomics; Humans; Methods; Molecular Sequence Annotation; Normalization; Quality control; Ribonucleic acid; RNA; RNA sequencing; RNA-Seq; Sequence Analysis, RNA - methods; Titration; Whole Exome Sequencing; Australia; Normalization; RNA-seq; Gene annotation; Gene expression quantification
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04644-8

RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods. In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.