Global run-on sequencing (GRO-seq) is a recent addition to the series of 
high-throughput sequencing methods that enables new insights into 
transcriptional dynamics within a cell. However, GRO-sequencing presents new 
algorithmic challenges, as existing analysis platforms for ChIP-seq and RNA-seq 
do not address the unique problem of identifying transcriptional units de novo 
from short reads located all across the genome. Here, we present a novel 
algorithm for de novo transcript identification from GRO-sequencing data, along 
with a system that determines transcript regions, stores them in a relational 
database and associates them with known reference annotations. We use this 
method to analyze GRO-sequencing data from primary mouse macrophages and derive 
novel quantitative insights into the extent and characteristics of non-coding 
transcription in mammalian cells. In doing so, we demonstrate that Vespucci 
expands existing annotations for mRNAs and lincRNAs by defining the primary 
transcript beyond the polyadenylation site. In addition, Vespucci generates 
assemblies for un-annotated non-coding RNAs such as those transcribed from 
enhancer-like elements. Vespucci thereby provides a robust system for defining, 
storing and analyzing diverse classes of primary RNA transcripts that are of 
increasing biological interest.