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.(A) 基于Illumina测序获得的全部RNA-seq读段的比对统计结果。 (B)~(E) 基于野生型(Wild Type, WT)与lba1 upf3-1双突变体(B)、野生型(Wild Type, WT)与lba1单突变体(C)、野生型(Wild Type, WT)与upf3-1单突变体(D)以及Mock对照与CHX处理组(E)的数据集比对，开展基于事件的可变剪接(Alternative Splicing, AS)分析。针对每一项可变剪接事件与比对组合，均提供了检测剪接变体比例在正向(up)或反向(down)方向变化时得到的p值与Q值，以及最小值(min值)；此外还提供了各列表中基于p值的排序结果。 (F) 可变剪接分析合并列表（基于(B)~(E)中的单组比对结果），支持在单一事件层面开展不同数据集的对比分析。可通过调整错误发现率(False Discovery Rate, FDR)或p值的阈值，利用R-U列的矩阵对指定比对组合开展差异基因表达(Differential Gene Expression, GE)或可变剪接模式变化(TE)分析。该矩阵支持单次检验与组合检验两种模式，可同时设置最大阈值(MAX THRESHOLDS)与最小阈值(MIN THRESHOLDS)。符合给定设置的显著变化事件的数量将在"SIGNIFICANCE COUNTS"栏下展示。可通过A-P列查看并排序单个事件的结果，其中数值"1"与"0"分别代表未满足与满足R-U列矩阵中设定的筛选标准。请注意：仅当(B)~(E)工作表的排序保持不变时，本表格内置的逻辑方可正常生效。 (G) 全部分析样本与生物学重复(R)中所有被测可变剪接事件的剪接指数得分（内含子剪接百分比，Percent spliced in, PSI）。 (H) 用于计算差异基因表达数据的内部查询表格，不得更改该表格的排序方式。如需开展差异基因表达分析，请使用工作表(I)。 (I) 针对所有基因与样本的差异基因表达分析，数值为p值。可利用P-AB列的矩阵输入各样本对应的基因类型与阈值，展示符合设定标准的基因总数量("COUNT")。 更多信息与计算流程的详细说明请参见Supplemental Methods。