McSplicer: A probabilistic model for estimating splice site usage from RNA-seq data

Israa Alqassem, Yash Sonthalia, Erika Klitzke-Feser, Heejung Shim, Stefan Canzar

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Motivation: Alternative splicing removes intronic sequences from pre-mRNAs in alternative ways to produce different forms (isoforms) of mature mRNA. The composition of expressed transcripts gives specific functionalities to cells in a particular condition or developmental stage. In addition, a large fraction of human disease mutations affect splicing and lead to aberrant mRNA and protein products. Current methods that interrogate the transcriptome based on RNA-seq either suffer from short-read length when trying to infer full-length transcripts, or are restricted to predefined units of alternative splicing that they quantify from local read evidence. Results: Instead of attempting to quantify individual outcomes of the splicing process such as local splicing events or full-length transcripts, we propose to quantify alternative splicing using a simplified probabilistic model of the underlying splicing process. Our model is based on the usage of individual splice sites and can generate arbitrarily complex types of splicing patterns. In our implementation, McSplicer, we estimate the parameters of our model using all read data at once and we demonstrate in our experiments that this yields more accurate estimates compared to competing methods. Our model is able to describe multiple effects of splicing mutations using few, easy to interpret parameters, as we illustrate in an experiment on RNA-seq data from autism spectrum disorder patients. Availability and implementation: McSplicer source code is available at and has been deposited in archived format at

Original languageEnglish (US)
Pages (from-to)2004-2011
Number of pages8
Issue number14
StatePublished - Jul 15 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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