bioRxiv2018
Long non-coding RNAs (lncRNAs) emerge as important regulators of various biological processes. Many lncRNAs with tumor-suppressor or oncogenic functions in cancer have been discovered. While many studies have exploited public resources such as RNA-Seq data in The Cancer Genome Atlas (TCGA) to study lncRNAs in cancer, it is crucial to choose the optimal method for accurate expression quantification of lncRNAs. In this benchmarking study, we compared the performance of pseudoalignment methods Kallisto and Salmon, and alignment-based methods HTSeq, featureCounts, and RSEM, in lncRNA quantification, by applying them to a simulated RNA-Seq dataset and a pan-cancer RNA-Seq dataset from TCGA. We observed that full transcriptome annotation, including both protein coding and noncoding RNAs, greatly improves the specificity of lncRNA expression quantification. Pseudoalignment-based methods detect more lncRNAs than alignment-based methods and correlate highly with simulated ground truth. On the contrary, alignment-based methods tend to underestimate lncRNA expression or even fail to capture lncRNA signal in the ground truth. These underestimated genes include cancer-relevant lncRNAs such as TERC and ZEB2-AS1. Overall, 10-16% of lncRNAs can be detected in the samples, with antisense and lincRNAs the two most abundant categories. A higher proportion of antisense RNAs are detected than lincRNAs. Moreover, among the expressed lncRNAs, more antisense RNAs are discordant from ground truth than lincRNAs when measured by alignment-based methods, indicating that antisense RNAs are more susceptible to mis-quantification. In addition, the lncRNAs with fewer transcripts, less than three exons, and lower sequence uniqueness tend to be more discordant. In summary, pseudoalignment methods Kallisto or Salmon in combination with the full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.\n\nAUTHOR SUMMARYLong non-coding RNAs (lncRNAs) emerge as important regulators of various biological processes. Our benchmarking work on both simulated RNA-Seq dataset and pan-cancer dataset provides timely and useful recommendations for wide research community who are studying lncRNAs, especially for those who are exploring public resources such as TCGA RNA-Seq data. We demonstrate that using full transcriptome annotation in RNA-Seq analysis is strongly recommended as it greatly improves the specificity of lncRNA quantification. Whats more, pseudoalignment methods Kallisto and Salmon outperform alignment-based methods in lncRNA quantification. It is worth noting that the default workflow for TCGA RNA-Seq data stored in Genomic Data Commons (GDC) data portal uses HTSeq, an alignment-based method. Thus, reanalyzing the data might be considered when checking gene expression in TCGA datasets. In summary, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.