RNA-sequencing has revolutionized biomedical research and, in particular, our ability to study gene alternative splicing. The problem has important implications for human health, as alternative splicing may be involved in malfunctions at the cellular level and multiple diseases. However, the high-dimensional nature of the data and the existence of experimental biases pose serious data analysis challenges.
A team led by researchers at Institute for Research in Biomedicine of Barcelona found that the standard data summaries used to study alternative splicing are severely limited, as they ignore a substantial amount of valuable information. Current data analysis methods are based on such summaries and are hence sub-optimal. Further, they have limited flexibility in accounting for technical biases. The team proposes novel data summaries and a Bayesian modeling framework that overcome these limitations and determine biases in a non-parametric, highly flexible manner. These summaries adapt naturally to the rapid improvements in sequencing technology. They provide efficient point estimates and uncertainty assessments. The approach allows to study alternative splicing patterns for individual samples and can also be the basis for downstream analyses. They found a several fold improvement in estimation mean square error compared popular approaches in simulations, and substantially higher consistency between replicates in experimental data. These findings indicate the need for adjusting the routine summarization and analysis of alternative splicing RNA-seq studies.
Availability – A software implementation is available in the R package casper at: http://www.bioconductor.org/packages/release/bioc/html/casper.html