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Limitations of alignment-free tools in total RNA-seq quantification

Wu, Douglas C.; Yao, Jun; Ho, Kevin S.; Lambowitz, Alan M.; Wilke, Claus O.

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July 3, 2018

10/ggdfr2

PMID: 29969991 PMCID: PMC6042521

Abstract:

BACKGROUND: Alignment-free RNA quantification tools have significantly increased the speed of RNA-seq analysis. However, it is unclear whether these state-of-the-art RNA-seq analysis pipelines can quantify small RNAs as accurately as they do with long RNAs in the context of total RNA quantification. RESULT: We comprehensively tested and compared four RNA-seq pipelines for accuracy of gene quantification and fold-change estimation. We used a novel total RNA benchmarking dataset in which small non-coding RNAs are highly represented along with other long RNAs. The four RNA-seq pipelines consisted of two commonly-used alignment-free pipelines and two variants of alignment-based pipelines. We found that all pipelines showed high accuracy for quantifying the expression of long and highly-abundant genes. However, alignment-free pipelines showed systematically poorer performance in quantifying lowly-abundant and small RNAs. CONCLUSION: We have shown that alignment-free and traditional alignment-based quantification methods perform similarly for common gene targets, such as protein-coding genes. However, we have identified a potential pitfall in analyzing and quantifying lowly-expressed genes and small RNAs with alignment-free pipelines, especially when these small RNAs contain biological variations.

Automatic Tags

Area Under Curve; ROC Curve; RNA-seq; Algorithms; RNA; Sequence Analysis, RNA; High-Throughput Nucleotide Sequencing; k-mer; RNA, Ribosomal; RNA, Transfer; TGIRT-seq

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