Multivariate Analysis of Transcript Splicing (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.

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

2/16/2012 Release of MATS 1.1.0, provided Ensembl version of mouse annotation

12/15/2011 Release of MATS 1.0.0, the initial version of MATS

Release: MATS 1.2.0 download

User Guide

User-defined Junction Annotation

Convert Bowtie to Sam

Convert SAM to Input

Python 2.6 or above and corresponding versions of NumPy and SciPy

OPTIONAL: JAGS (Just Another Gibbs Sampler) required only if using the Bayesian method in MATS P value calculation

OPTIONAL: PYGR (Python Graph Database) required only if using the script createJunctionAnnotation.sh

Dummy sample data download

RNA-Seq reads: ESRP1 sample and EV sample

Human exon-exon junction annotation compiled from Ensembl release 57 and UCSC Known Genes (hg19) transcripts (required for mapping RNA-Seq reads to junctions)

Mouse exon-exon junction annotation is compiled from Ensembl release 65 transcripts (required for mapping RNA-Seq reads to junctions)

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)

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)

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

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).