The RNA transcriptome varies in response to cellular differentiation as well as 
environmental factors, and can be characterized by the diversity and abundance 
of transcript isoforms. Differential transcription analysis, the detection of 
differences between the transcriptomes of different cells, may improve 
understanding of cell differentiation and development and enable the 
identification of biomarkers that classify disease types. The availability of 
high-throughput short-read RNA sequencing technologies provides in-depth 
sampling of the transcriptome, making it possible to accurately detect the 
differences between transcriptomes. In this article, we present a new method for 
the detection and visualization of differential transcription. Our approach does 
not depend on transcript or gene annotations. It also circumvents the need for 
full transcript inference and quantification, which is a challenging problem 
because of short read lengths, as well as various sampling biases. Instead, our 
method takes a divide-and-conquer approach to localize the difference between 
transcriptomes in the form of alternative splicing modules (ASMs), where 
transcript isoforms diverge. Our approach starts with the identification of ASMs 
from the splice graph, constructed directly from the exons and introns predicted 
from RNA-seq read alignments. The abundance of alternative splicing isoforms 
residing in each ASM is estimated for each sample and is compared across sample 
groups. A non-parametric statistical test is applied to each ASM to detect 
significant differential transcription with a controlled false discovery rate. 
The sensitivity and specificity of the method have been assessed using simulated 
data sets and compared with other state-of-the-art approaches. Experimental 
validation using qRT-PCR confirmed a selected set of genes that are 
differentially expressed in a lung differentiation study and a breast cancer 
data set, demonstrating the utility of the approach applied on experimental 
biological data sets. The software of DiffSplice is available at 
http://www.netlab.uky.edu/p/bioinfo/DiffSplice.