The Annals of Applied Statistics

Quantifying alternative splicing from paired-end RNA-sequencing data

David Rossell, Camille Stephan-Otto Attolini, Manuel Kroiss, and Almond Stöcker

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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. We find 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 suboptimal. Further, they have limited flexibility in accounting for technical biases. We propose novel data summaries and a Bayesian modeling framework that overcome these limitations and determine biases in a nonparametric, highly flexible manner. These summaries adapt naturally to the rapid improvements in sequencing technology. We 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. We found a severalfold improvement in estimation mean square error compared popular approaches in simulations, and substantially higher consistency between replicates in experimental data. Our findings indicate the need for adjusting the routine summarization and analysis of alternative splicing RNA-seq studies. We provide a software implementation in the R package casper.

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Ann. Appl. Stat., Volume 8, Number 1 (2014), 309-330.

First available in Project Euclid: 8 April 2014

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Alternative splicing RNA-Seq Bayesian modeling estimation


Rossell, David; Stephan-Otto Attolini, Camille; Kroiss, Manuel; Stöcker, Almond. Quantifying alternative splicing from paired-end RNA-sequencing data. Ann. Appl. Stat. 8 (2014), no. 1, 309--330. doi:10.1214/13-AOAS687.

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Supplemental materials

  • Supplementary material: Supplementary results. In Rossell et al. (2014) we assess the dependence of fragment start and length distributions on gene length, show additional simulation results, assess MCMC convergence and apply the approach to transcripts found de novo.