A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.

ABSTRACT: Identification of protein-coding and non-coding transcripts is paramount for understanding their biological roles. Computational approaches have been addressing this task for over a decade; however, generalized and high-performance models are still unreliable. This benchmark study assessed the performance of 24 tools producing >55 models on the datasets covering a wide range of species. We have collected 135 small and large transcriptomic datasets from existing studies for comparison and identified the potential bottlenecks hampering the performance of current tools. The key insights of this study include lack of standardized training sets, reliance on homogeneous training data, gradual changes in annotated data, lack of augmentation with homology searches, the presence of false positives

SUBMITTER: Singh D 

PROVIDER: S-EPMC9757047 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature