About MATS
We develop MATS (Multivariate Analysis of Transcript
Splicing), a Bayesian statistical framework for flexible
hypothesis testing of differential alternative splicing patterns
on RNA-Seq data. MATS uses a multivariate uniform prior to model
the between-sample correlation in exon splicing patterns, and a
Markov chain Monte Carlo (MCMC) method coupled with a
simulation-based adaptive sampling procedure to calculate the P
value and false discovery rate (FDR) of differential alternative
splicing. Importantly, the MATS approach is applicable to almost
any type of null hypotheses of interest, providing the
flexibility to identify differential alternative splicing events
that match a given user-defined pattern. MATS can assess the
statistical significance that the absolute difference in the
exon inclusion levels of an exon between two conditions
exceeds any given user-defined threshold. It can also be used to
detect exons with the extreme "switch-like" differential
alternative splicing pattern.
Updates
- 3/5/2012
- Release of MATS 1.2.0, added a new method to calculate P values by likelihood-ratio test, which is ~100x faster than the Bayesian method
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- 2/16/2012
- Release of MATS 1.1.0, provided Ensembl version of mouse annotation
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- 12/15/2011
- Release of MATS 1.0.0, the initial version of MATS
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Software Download
Documentation
Pre-requisites
- OPTIONAL: JAGS (Just Another Gibbs Sampler) required only if using the Bayesian method in MATS P value calculation
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Test dataset
Annotations
- Mouse exon-exon junction annotation is compiled from Ensembl release
65 transcripts (required for mapping RNA-Seq reads to junctions)
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- Annotation for all human genes and their exons compiled from Ensembl Release 57 and UCSC Known Genes (hg19) transcripts (required for both generating the MATS input files and detecting alternative splicing events from the mapping results)
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- Annotation for all mouse genes and their exons compiled from Ensembl Release 65 transcripts (required for both generating the MATS input files and detecting alternative splicing events from the mapping results)
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Citation
Shen S., Park JW., Huang J., Dittmar KA., Lu ZX., Zhou
Q., Carstens RP., Xing Y. MATS: A Bayesian Framework for
Flexible Detection of Differential Alternative Splicing from
RNA-Seq Data.
Nucleic Acids Research, 2012,
doi: 10.1093/nar/gkr1291
Contact
Correspondences regarding the MATS algorithm and running
the MATS source code should be directed to Prof. Yi Xing (yxing at ucla.edu) and Shihao Shen (shihao-shen at uiowa.edu).