Discovery of tissue-specific exons using comprehensive human exon microarrays

Additional data file 1:

Approximately 15 μl of PCR product were separated on a 2.5% agarose gel stained with ethidium bromide. Primers were designed to well annotated exons that flank the PSR identified as a potential novel tissue-specific exon by the Splicing Index. Primer sequences are available in Additional data file 9.

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Additional data file 2:

The three biological replicates of each individual tissue were compared to each of the other tissues using the Splicing Index algorithm. The Splicing Index compares gene-level normalized probeset intensities using a Student t-test. (a) The number of probesets that have significantly different inclusion rates between the two tissues. Probesets with p values less than 0.05 were considered significant. (b) The number of significantly different probesets normalized by the number of genes used in that comparison. In order for a gene to be included in each of the pair-wise comparisons, 50% of the Ensembl/RefSeq supported exons were required to have DABG p values less than 0.05 in a minimum of two out of the three biological replicates of each tissue in that comparison. In all, 9085 of 12,139 genes (74.8%) showed evidence of differential alternative splicing in at least one tissue comparison.

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Additional data file 3:

The number of genes, number of detected genes, and number of genes displaying differential exon expression is graphed for several of the largest classes of drugable genes. Gene classes are sorted from left to right by the percentage of genes exhibiting differential alternative splicing.

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Additional data file 4:

See Additional data file 1 for details. Sequences of the primers used in the RT-PCR are available in Additional data file 10.

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Additional data file 5:

(a) A histogram of the Pearson correlation coefficient of signal intensity across the 16 tissues for probesets belonging to the same exon cluster and probesets randomly selected from different exon clusters. (b) A table showing the median and average correlation and the total number and percent of exon clusters with correlations less than different values.

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Additional data file 6:

The robustness of the gene-level estimation method to alternative splicing was analyzed by simulating exon skipping events. The intensity of each probeset that was used in the gene-level estimate was systematically substituted for the background level and the gene-level estimate was re-computed. The deviation of the altered gene-level estimate from the original gene-level estimate was determined for every transcript cluster in each tissue sample. (a) The median percent deviation was calculated by taking the median of the difference of the altered and the original gene-level estimates divided by the original gene-level estimate all multiplied by 100. (b) A graph illustrating the median percent deviation of gene-level estimates for transcript clusters with 5 or more, 10 or more, and 20 or more probesets used in the calculation of the gene-level estimate.

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Additional data file 7:

Chip design information.

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Additional data file 8:

RNA sample information.

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Additional data file 9:

RT-PCR primer sequences.

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Additional data file 10:

Additional RT-PCR primers.

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Additional data file 11:

Normalization control genes.

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