Assessing the impact of human genome annotation choice on RNA-seq expression estimates

Abstract

BackgroundGenome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus.ResultsWe define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates.ConclusionsBased on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

Mapping the non-standardized biases of ribosome profiling

“…The genome annotation choice may significantly influence the downstream quantification of expression and differential analysis (Zhao and Zhang, 2015), although a simple advice on which database to use is not possible and should be driven by the purpose of the analysis. For research aiming at reproducible and robust gene expression estimates, RefSeq might be preferred (Wu et al, 2013). More exploratory questions may rely on more complex annotations, e.g.…”

Effect of low-expression gene filtering on detection of differentially expressed genes in RNA-seq data

“…We quantify RNA-seq “noise” expression by aligning reads to intergenic regions, which we define as regions complementary to genic regions (including two flanking sequences of lkb at both ends) annotated by Aceview. We use the Aceview annotation because it is considered to be less conservative than other annotations such as Refseq [18]. However, this method highly depends on the completeness of genome annotation.…”

Investigation of factors affecting RNA-seq gene expression calls

“…However, a comprehensive analysis of RNA-seq pipelines, including mapping, quantification, and normalization components should be examined to determine the effect of analysis pipeline on gene expression calls. Second, the specific choice of human genome annotation can largely impact downstream RNA-seq gene expression estimation [13]. Thus, a comprehensive analysis of the effect of genome annotation on the distributions of exonic, intronic, and intergenic region mapping is warranted.…”