SparseIso: A novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data

Xu Shi, Xiao Wang, Tian Li Wang, Leena Hilakivi-Clarke, Robert Clarke, Jianhua Xuan

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Motivation: Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems. Results: We have developed a novel Bayesian method, SparseIso, to reliably identify spliced isoforms from RNA-seq data. A spike-and-slab prior is incorporated into the Bayesian model to enforce the sparsity for isoform identification, effectively alleviating the problem of overfitting. A Gibbs sampling procedure is further developed to simultaneously identify and quantify transcripts from RNA-seq data. With the sampling approach, SparseIso estimates the joint distribution of all candidate transcripts, resulting in a significantly improved performance in detecting lowly expressed transcripts and multiple expressed isoforms of genes. Both simulation study and real data analysis have demonstrated that the proposed SparseIso method significantly outperforms existing methods for improved transcript assembly and isoform identification.

Original languageEnglish (US)
Pages (from-to)56-63
Number of pages8
Issue number1
StatePublished - Jan 1 2018
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported by National Institutes of Health (NIH) [CA149653, CA164384, CA149147, CA184902, CA148826 and CA187512].

Publisher Copyright:
© 2017 The Author.


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