Transcription start site signal profiling improves transposable element RNA expression analysis at locus-level.

The transcriptional activity of Transposable Elements (TEs) has been involved in numerous pathological processes, including neurodegenerative diseases such as amyotrophic lateral sclerosis and frontotemporal lobar degeneration. The TE expression analysis from short-read sequencing technologies is, however, challenging due to the multitude of similar sequences derived from singular TEs subfamilies and the exaptation of TEs within longer coding or non-coding RNAs. Specialised tools have been developed to quantify the expression of TEs that either relies on probabilistic re-distribution of multimapper count fractions or allow for discarding multimappers altogether. Until now, the benchmarking across those tools was largely limited to aggregated expression estimates over whole TEs subfamilies. Here, we compared the performance of recently published tools (SQuIRE, TElocal, SalmonTE) with simplistic quantification strategies (featureCounts in unique, fraction and random modes) at the individual loci level. Using simulated datasets, we examined the false discovery rate and the primary driver of those false positive hits in the optimal quantification strategy. Our findings suggest a high false discovery number that exceeds the total number of correctly recovered active loci for all the quantification strategies, including the best performing tool