Publicly available RNA-seq data is routinely used for retrospective analysis to 
elucidate new biology. Novel transcript discovery enabled by joint analysis of 
large collections of RNA-seq data sets has emerged as one such analysis. Current 
methods for transcript discovery rely on a '2-Step' approach where the first 
step encompasses building transcripts from individual data sets, followed by the 
second step that merges predicted transcripts across data sets. To increase the 
power of transcript discovery from large collections of RNA-seq data sets, we 
developed a novel '1-Step' approach named Pooling RNA-seq and Assembling Models 
(PRAM) that builds transcript models from pooled RNA-seq data sets. We 
demonstrate in a computational benchmark that 1-Step outperforms 2-Step 
approaches in predicting overall transcript structures and individual splice 
junctions, while performing competitively in detecting exonic nucleotides. 
Applying PRAM to 30 human ENCODE RNA-seq data sets identified unannotated 
transcripts with epigenetic and RAMPAGE signatures similar to those of recently 
annotated transcripts. In a case study, we discovered and experimentally 
validated new transcripts through the application of PRAM to mouse hematopoietic 
RNA-seq data sets. We uncovered new transcripts that share a differential 
expression pattern with a neighboring gene Pik3cg implicated in human 
hematopoietic phenotypes, and we provided evidence for the conservation of this 
relationship in human. PRAM is implemented as an R/Bioconductor package.