Benchmarking tRNA-Seq quantification approaches by realistic tRNA-Seq data simulation identifies two novel approaches with higher accuracy

Smith, T. S.; Monti, M.; Willis, A. E.; Kalmar, L.

doi:10.1101/2023.12.13.571582

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bioRxiv · 10.1101/2023.12.13.571582

Benchmarking tRNA-Seq quantification approaches by realistic tRNA-Seq data simulation identifies two novel approaches with higher accuracy

Smith, T. S.·Monti, M.·Willis, A. E.·Kalmar, L.

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Abstract

Quantification of transfer RNA (tRNA) using illumina sequencing based tRNA-Seq is complicated due to their degree of redundancy and extensive modifications. As such, no tRNA-Seq method has become well established, while various approaches have been proposed to quantify tRNAs from sequencing reads. Here, we use realistic tRNA-Seq simulations to benchmark tRNA-Seq quantification approaches, including two novel approaches. We demonstrate that these novel approaches are consistently the most accurate, using data simulated to mimic five different tRNA-Seq methods. This simulation-based benchmarking also identifies specific shortfalls for each quantification approach and suggests that up to 13% of the variance observed between cell lines in real tRNA-Seq data could be due to systematic differences in quantification accuracy.

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Smith, T. S., Monti, M., Willis, A. E., Kalmar, L.. 2023-12-14. Benchmarking tRNA-Seq quantification approaches by realistic tRNA-Seq data simulation identifies two novel approaches with higher accuracy. https://doi.org/10.1101/2023.12.13.571582

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