Characterization of Human Endogenous Retrovirus (HERV) expression within the transcriptomic landscape using RNA-seq is complicated by uncertainty in fragment assignment because of sequence similarity. We present Telescope, a computational software tool that provides accurate estimation of transposable element expression (retrotranscriptome) resolved to specific genomic locations. Telescope directly addresses uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We demonstrate the utility of our approach through single locus analysis of HERV expression in 13 ENCODE cell types. When examined at this resolution, we find that the magnitude and breadth of the retrotranscriptome can be vastly different among cell types. Furthermore, our approach is robust to differences in sequencing technology and demonstrates that the retrotranscriptome has potential to be used for cell type identification. We compared our tool with other approaches for quantifying transposable element (TE) expression, and found that Telescope has the greatest resolution, as it estimates expression at specific TE insertions rather than at the TE subfamily level. Telescope performs highly accurate quantification of the retrotranscriptomic landscape in RNA-seq experiments, revealing a differential complexity in the transposable element biology of complex systems not previously observed. Telescope is available at https://github.com/mlbendall/telescope.

Protein translation is at the heart of cellular metabolism and its in-depth characterization is key for many lines of research. Recently, ribosome profiling became the state-of-the-art method to quantitatively characterize translation dynamics at a transcriptome-wide level. However, the strategy of library generation affects its outcomes. Here, we present a modified ribosome-profiling protocol starting from yeast, human cells and vertebrate brain tissue. We use a DNA linker carrying four randomized positions at its 5′ end and a reverse-transcription (RT) primer with three randomized positions to reduce artifacts during library preparation. The use of seven randomized nucleotides allows to efficiently detect library-generation artifacts. We find that the effect of polymerase chain reaction (PCR) artifacts is relatively small for global analyses when sufficient input material is used. However, when input material is limiting, our strategy improves the sensitivity of gene-specific analyses. Furthermore, randomized nucleotides alleviate the skewed frequency of specific sequences at the 3′ end of ribosome-protected fragments (RPFs) likely resulting from ligase specificity. Finally, strategies that rely on dual ligation show a high degree of gene-coverage variation. Taken together, our approach helps to remedy two of the main problems associated with ribosome-profiling data. This will facilitate the analysis of translational dynamics and increase our understanding of the influence of RNA modifications on translation.

1Characterization of Human Endogenous Retrovirus (HERV) expression within the 2 transcriptomic landscape using RNA-seq is complicated by uncertainty in fragment 3 assignment because of sequence similarity. We present Telescope, a computational 4 software tool that provides accurate estimation of transposable element expression 5 (retrotranscriptome) resolved to specific genomic locations. Telescope directly addresses 6 uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the 7 most probable source transcript as determined within a Bayesian statistical model. We 8 demonstrate the utility of our approach through single locus analysis of HERV expression 9 in 13 ENCODE cell types. When examined at this resolution, we find that the magnitude 10

No abstract