Fingerprints of a message: integrating positional information on the transcriptome

Abstract

The recent explosion of high-throughput sequencing methods applied to RNA molecules is allowing us to go beyond the description of sequence variants and their relative abundances, as measured by RNA-seq. We can now probe for RNA engagement in polysomes, for ribosomes, RNA binding proteins and microRNAs binding sites, for RNA secondary structure and for RNA methylation. These descriptors produce a steadily growing multidimensional array of positional information on RNA sequences, whose effective integration only would bring to decipher the regulatory interplay occurring between proteins, RNAs and their modifications on the transcriptome. This interplay ultimately dictates the degree of mRNA availability to translation, and thus the occurrence of cell phenotypes. However, several issues in data presentation are slowing down effective integration. A standardization effort for new dataset types produced should be urgently undertaken to solve these issues. Providing uniformed experimental details along with datasets processed to be directly usable and employing shared formats would greatly simplify integration efforts, strengthening hypotheses stemming from correlative observations and eventually bringing to mechanistic understanding.

Editorial: Multi-omic data integration

“…Methodological approaches point with a novel emphasis at the importance of molecules' spatial localization in the omic context. From polysome and ribosome profiling, RNA, and miRNA binding sites annotation and standardization (Dassi and Quattrone, 2014 ), to networks including 3D molecules' proximity thanks to Chromosome Conformation Capture (3C) and its omic version Hi-C (Merelli et al, 2015 ), spatial representation contributes with an important layer of information in this added multi-omic complexity.…”