Additional file 10: Table S2. of Survey of cryptic unstable transcripts in yeast

Divergent gene-CUT pairs enriched for metabolic process genes. A total of 698 divergent gene-CUT pairs were identified in S288c. The subset of genes in these gene-CUT pairs are enriched for various metabolic processing gene ontologies (GO). P-values are based on the hypergeometric test after Holm-Bonferroni correction using the default background from YeastMine http://yeastmine.yeastgenome.org . The total number of genes in each GO category is listed far right. Table S3. Strains used in this study. A table describing the genotype and mating type of strains used in this study. Table S4. Summary of RNA-seq Read Mapping Results. A table summarizing the read mapping results for each RNA-seq library used in this study. Reported values for total mapped reads corresponds to all uniquely mapped reads after rRNA read removal. Table S5. Fold change conversion to discrete values. The Matlab HMM Toolkit only accepts discrete emission values. Per nucleotide rrp6Δ/WT fold change values were converted to a discrete value according to the table above. Table S6. HMM emission probabilities. The HMM emission probability for each discrete rrp6Δ/WT RNA-seq fold change value (see Table S3) for states 1–10. Because states 2–10 have the same emission probabilities we only show a single iteration of these emission probabilities for simplification. Table S7. HMM transition probabilities. The HMM transition probabilities for states 1–10. Movement through the HM is unidirectional and only two transition probabilities exist for each state. (XLSX 19 kb)

富集于代谢过程基因的发散型基因-CUT对。本研究在S288c菌株中共鉴定得到698个发散型基因-CUT对。上述基因-CUT对所包含的基因子集显著富集于各类代谢过程相关基因本体（GO）术语。P值基于超几何检验计算，并经Holm-Bonferroni多重检验校正，背景数据集取自YeastMine数据库的默认背景（http://yeastmine.yeastgenome.org）。各GO分类下的基因总数列于表格最右侧。
表S3 本研究所用菌株：收录本研究中所用菌株的基因型与交配型信息的表格。
表S4 RNA测序（RNA-seq）读段比对结果汇总：汇总本研究中各RNA-seq文库的读段比对结果的表格。报告的总比对读段数指去除核糖体RNA（rRNA）读段后，所有唯一比对读段的数量。
表S5 折叠变化值向离散值的转换：Matlab隐马尔可夫模型（HMM）工具包仅支持离散发射值。本研究中每核苷酸分辨率的rrp6Δ/WT折叠变化值将按照上表转换为离散值。
表S6 隐马尔可夫模型（HMM）发射概率：针对1~10个状态下的各离散rrp6Δ/WT RNA-seq折叠变化值（见表S3）的HMM发射概率。由于状态2至10的发射概率一致，为简化展示，我们仅给出其中一组发射概率示例。
表S7 隐马尔可夫模型（HMM）转移概率：针对1~10个状态的HMM转移概率。该模型的状态转移为单向性，每个状态仅存在两种转移概率。（XLSX格式，文件大小19 kb）