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

BMC Genomics. 2018 Jul 3;19(1):510. doi: 10.1186/s12864-018-4869-5.

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.

Keywords: RNA-seq; TGIRT-seq; k-mer.

MeSH terms

  • Algorithms
  • Area Under Curve
  • High-Throughput Nucleotide Sequencing
  • RNA / chemistry*
  • RNA / metabolism
  • RNA, Ribosomal / chemistry
  • RNA, Ribosomal / metabolism
  • RNA, Transfer / chemistry
  • RNA, Transfer / metabolism
  • ROC Curve
  • Sequence Analysis, RNA / methods*

Substances

  • RNA, Ribosomal
  • RNA
  • RNA, Transfer