The impact of gene annotation choice on the quantification of RNA-seq data

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.

Researchers from the ONJ Cancer Research Institute present results from their comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. They 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. The researchers also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, they demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.

Concordance and differences between gene annotations


(A) Venn diagram showing genes that are common or unique in the Ensembl, RefSeq-NCBI and RefSeq-Rsubread annotations. (B) Boxplots showing the distribution of effective gene lengths (log2 scale) in each annotation. (C) Boxplots showing the differences in effective lengths of common genes between each pair of annotations. Values shown in the plots are the ratio of effective lengths of the same gene from two different annotations (log2 scale). (D) The size of transcriptome calculated from each annotation. Shown are the sum of effective gene lengths in each annotation.

Chisanga D, Liao Y, Shi W. (2021) Impact of gene annotation choice on the quantification of RNA-seq data. bioRXiv [online preprint]. [abstract]

One comment

  1. If I understood well the annotation “ENSEMBL” used in this paper correspond to the _comprehensive_ GENCODE annotation. What about _basic_ GENCODE annotation?

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