Impact of Gene Annotation on RNA-seq Data Analysis

RNA-seq has become increasingly popular in transcriptome profiling. One of the major challenges in RNA-seq data analysis is the accurate mapping of junction reads to their genomic origins. To detect splicing sites in short reads, many RNA-seq aligners use reference transcriptome to inform placement of junction reads. However, no systematic evaluation has been performed to assess or quantify the benefits of incorporating reference transcriptome in mapping RNA-seq reads. Meanwhile, there exist multiple human genome annotation databases, including RefGene (RefSeq Gene), Ensembl, and the UCSC annotation database. The impact of the choice of an annotation on estimating gene expression remains insufficiently investigated. In this chapter, we systematically characterized the impact of genome annotation choice on read mapping and gene quantification by analyzing a RNA-seq dataset generated by Illumina’s Human Body Map 2.0 Project. The impact of a gene model on mapping of non-junction reads is different from junction reads. We demonstrated that the choice of a gene model has a dramatic effect on both gene quantification and differential analysis. Our research will help RNA-seq data analysts to make an informed choice of gene model in practical RNA-seq data analysis.