Impact of sequencing technologies on long non-coding RNA computational identification

The correct annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs), is still a critial challenge in genome analyses due to their highly heterogeneous characteristics. Due to this heterogeneity, transcriptome data sources can be an important factor that might affect lncRNA annotation quality. Long-read technologies now bring the potential to improve the quality of transcriptome annotation, specially in genome entities that are not “classic” coding genes. However, there is a gap regarding benchmarking studies that test if the direct use of lncRNA predictors in long-reads makes more precise identification of these transcripts. Considering that lncRNA identification tools were not trained with these reads, our study want to address: how is the performance of these tools? Are they also able to efficiently identify lncRNAs? For this, we used short and long-read data from human and selected plants transcriptomes to test our questions. We can provide evidence of where and how to make potential better approaches for the lncRNA annotation by understanding these issues.