Supplemental Data Set 1. Read Statistics of RNA-Seq Data and Computational Analysis of Transcriptome-Wide AS and Gene Expression.

(A) Alignment statistics of all RNA-seq reads derived from Illumina sequencing.
(B) - (E) Event based alternative splicing analysis based on comparisons of WT vs. lba1 upf3-1 double mutant (B), WT vs. lba1 (C) and WT vs. upf3-1 (D) single mutants, and Mock vs. CHX treatment (E) datasets. For each AS event and comparison, p and Q values from testing AS variant ratio changes in either one (up) or the other (down) direction as well as the minimum (min) values are provided. Furthermore, rankings within each list according to the p values are provided.
(F) Combined list of alternative splicing analyses (based on single comparisons displayed in (B) - (E)) allowing comparison of different datasets on a single event basis. Use matrix in columns R-U to analyze differential gene expression (GE) or AS pattern changes (TE) for the indicated comparisons by changing thresholds for FDR or p values. The matrix allows considering single tests as well as combinations, and both maximum (MAX THRESHOLDS) and minimum (MIN THRESHOLDS) cut-off values can be set. Number of significant changes with given settings are displayed under “SIGNIFICANCE COUNTS”. Results for single events can be viewed and sorted using columns A-P, with values “1” and “0” indicating “FALSE” and “TRUE”, respectively, for fulfilling the criteria set in the matrix shown in columns R-U. Note that the logic provided in this spreadsheet only works if sorting in sheets (B) - (E) is unchanged.
(G) Splice Index Score (Percent spliced in, PSI) of all tested alternative splicing events in all samples and replicates (R) analyzed.
(H) Table for internal lookup to compute differential gene expression data. Sorting of this table must not be changed. For analyzing differential gene expression use sheet (I).
(I) Differential gene expression analysis for all genes and samples. Numbers provide p values. Matrix in columns P-AB can be used to enter gene types and cut-off values for the individual samples, displaying the total number of genes (“COUNT”) fulfilling the set criteria.
Further information and a detailed description of the computational pipeline are provided in Supplemental Methods.