RNA sequencing is a recent technology which has seen an explosion of methods 
addressing all levels of analysis, from read mapping to transcript assembly to 
differential expression modeling. In particular the discovery of isoforms at the 
transcript assembly stage is a complex problem and current approaches suffer 
from various limitations. For instance, many approaches use graphs to construct 
a minimal set of isoforms which covers the observed reads, then perform a 
separate algorithm to quantify the isoforms, which can result in a loss of 
power. Current methods also use ad-hoc solutions to deal with the vast number of 
possible isoforms which can be constructed from a given set of reads. Finally, 
while the need of taking into account features such as read pairing and sampling 
rate of reads has been acknowledged, most existing methods do not seamlessly 
integrate these features as part of the model. We present Montebello, an 
integrated statistical approach which performs simultaneous isoform discovery 
and quantification by using a Monte Carlo simulation to find the most likely 
isoform composition leading to a set of observed reads. We compare Montebello to 
Cufflinks, a popular isoform discovery approach, on a simulated data set and on 
46.3 million brain reads from an Illumina tissue panel. On this data set 
Montebello appears to offer a modest improvement over Cufflinks when considering 
discovery and parsimony metrics. In addition Montebello mitigates specific 
difficulties inherent in the Cufflinks approach. Finally, Montebello can be 
fine-tuned depending on the type of solution desired.