The impact of RNA-seq aligners on gene expression estimation

While numerous RNA-seq data analysis pipelines are available, research has shown that the choice of pipeline influences the results of differentially expressed gene detection and gene expression estimation. Gene expression estimation is a key step in RNA-seq data analysis, since the accuracy of gene expression estimates profoundly affects the subsequent analysis. Generally, gene expression estimation involves sequence alignment and quantification, and accurate gene expression estimation requires accurate alignment. However, the impact of aligners on gene expression estimation remains unclear. We address this need by constructing nine pipelines consisting of nine spliced aligners and one quantifier. We then use simulated data to investigate the impact of aligners on gene expression estimation. To evaluate alignment, we introduce three alignment performance metrics, (1) the percentage of reads aligned, (2) the percentage of reads aligned with zero mismatch (ZeroMismatchPercentage), and (3) the percentage of reads aligned with at most one mismatch (ZeroOneMismatchPercentage). We then evaluate the impact of alignment performance on gene expression estimation using three metrics, (1) gene detection accuracy, (2) the number of genes falsely quantified (FalseExpNum), and (3) the number of genes with falsely estimated fold changes (FalseFcNum). We found that among various pipelines, FalseExpNum and FalseFcNum are correlated. Moreover, FalseExpNum is linearly correlated with the percentage of reads aligned and ZeroMismatchPercentage, and FalseFcNum is linearly correlated with ZeroMismatchPercentage. Because of this correlation, the percentage of reads aligned and ZeroMismatchPercentage may be used to assess the performance of gene expression estimation for all RNA-seq datasets.