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GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data

Keyan Zhao12, Zhi-xiang Lu12, Juw Won Park12, Qing Zhou3 and Yi Xing12*

Author Affiliations

1 Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CHS 33-228, 650 Charles E. Young Drive South, Los Angeles, CA 90095, USA

2 Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA

3 Department of Statistics, University of California, Los Angeles, 8125 Math Sciences Building, Los Angeles, CA 90095, USA

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Genome Biology 2013, 14:R74  doi:10.1186/gb-2013-14-7-r74

Published: 22 July 2013

Additional files

Additional file 1:

Supplementary Methods and Supplementary Figures S1-S9.

Format: PDF Size: 6.7MB Download file

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Additional file 2:

Supplementary table S1. sQTL exons (FDR ≤0.1) and information of the most proximal sQTL SNPs.

Format: XLS Size: 58KB Download file

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Additional file 3:

Supplementary table S2. HapMap3 samples used for RT-PCR validation of sQTLs.

Format: XLS Size: 24KB Download file

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Additional file 4:

Supplementary table S3. Primers and RT-PCR results for validation of sQTLs.

Format: XLS Size: 38KB Download file

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Additional file 5:

Supplementary table S4. The individual exon inclusion levels for RT-PCR validation of sQTLs.

Format: XLS Size: 69KB Download file

This file can be viewed with: Microsoft Excel Viewer

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Additional file 6:

Supplementary table S5. Primers used for constructing minigene splicing reporters.

Format: XLS Size: 26KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data