High-resolution coverage analysis detects and quantifies alternative mRNA processing events

Author(s): Francesco Dossena

Affiliation(s): University of Milan and Human Technopole



Gene expression is regulated at multiple levels, starting with transcription and maturation of RNA species in the nucleus, and continuing with protein synthesis in the cytoplasm. Post-transcriptional gene regulation (PTGR) mechanisms, such as splicing, alternative polyadenylation (APA), mRNA decay, or translational control, play a crucial role in ensuring correct protein synthesis. The resolution provided by RNA-seq technologies can be leveraged to go beyond traditional count-based methods and uncover valuable information for identifying and quantifying PTGR events. For example, several methods that exploit such high resolution have been proposed for Ribo-seq data analysis, enabling precise observation of translation dynamics. Moreover, the spread of long-read sequencing methods, such as the full-length poly(A) and mRNA sequencing (FLAM-seq), opens new possibilities to study PTGR mechanisms at the single transcript level. In this project, we are using RNA-seq and Ribo-seq data to develop a computational strategy based on single-nucleotide resolution transcriptome-wide coverage profiles. This approach aims at detecting differentially expressed features and link them to PTGR mechanisms. We are implementing statistical methods that can reliably perform feature selection, promote sparsity of changes, and extract coherent variation observed in biological replicates, and we here showcase some example results of our preliminary strategy. Among the PTGR mechanisms, alternative mRNA cleavage has been identified as an important source of alternative transcripts. While these isoforms often differ only in the length of their 3'UTR, cleavage events can also generate stable mRNA fragments with undefined biological roles. We are using a combination of data from short and long-reads sequencing protocols such as FLAM-seq and Oxford Nanopore coupled with Cap Analysis of Gene Expression (ONT-CAGE) to identify cleavage events and quantify expression, capping, and polyadenylation status of the produced mRNA fragments. We have identified hundreds of novel cleavage events and their associated fragments, enabling investigation into their cellular functions. In summary, we propose a set of data analysis solutions integrating short and long-read technologies to quantify different mRNA regulatory processes, fully exploiting the high resolution provided by RNA sequencing protocols.