1. Eran Yanowski, Eran Hornstein’s: Monitor drug impact on the transcriptome of mouse beta cells (primary and cell-line) using Transeq/RNA-Seq 17.08.15 Report I

2. Overview of basic parameters of your NGS run (per sample): samples origin: Mouse Beta cells, line SR 60

3. FASTQC report for sample 1000_0A (randomly chosen)

4. FASTQC report for sample 1000_0A : Most reads start with 5’ GGG, typical in Transeq procedure

5. FASTQC report for sample : overrepresentation of Ins2 reads

6. Pre-Pipeline data processing  Need to remove 5’GGG sequences  Need to remove reads containing either poly-A or poly-T

7. Reads summary following removal of poly-A & poly-T reads (Mouse beta cells, line)

8. FASTQC report following removal of poly-A & poly-T reads & 5’ GGG trimming: (sample 1000_0A ) ‘normal’ frequency of reads’ 5’ GGG reduced frequency of %A

9. Data processing (pipeline) workflow (done using Mouse_mm9_v1 base repository) 1.If each sample has more than one fastq file (per sequencing read) then fastq files merging-step is performed 2.Transeq Reads pre-processing (5’ GGG trimming & polyA & polyT removal) 3.Processed-reads Mapping (using TopHat) 4.TES reads coverage profile (Transeq protocol QC step) 5.Reads Count (per 3’UTR) (using HTSeq-count) 6.Data Normalization and Differential Gene Expression (using DESeq2) 7.QC: Principal Component Analysis (PCA) & Hierarchical Clustering

10. Reads mapping summary_Exp4_1_ Mouse Beta cells, line

11. Reads mapping summary_Exp4_1_ Mouse Beta cells, line: 23-24% of total reads count were mapped to Ins2/Ins1 genes

12. Reads summary following removal of polyA and polyT reads (Exp4_2_Mouse Primary Beta cells):

13. Reads mapping summary_Exp4_2_ Mouse Primary Beta cells:

14. Reads mapping summary_Exp4_2_ Mouse Primary Beta cells: % of total reads count were mapped to Ins2/Ins1 genes

15. Hierarchical clustering: Mouse beta cells, line Drug Con=1000 Drug con=100

16. Hierarchical clustering: Mouse Primary beta cells Drug Con=1000Drug con=100 Separated by processing day: A/B/C ?

17. Differential gene expression data is assessed by DESeq2 DESeq output is summarized in a single sheet per experiment Genes differentially-expressed during each time series were called by two independent means: I.Using pairwise comparison vs. time zero II.Using a tool named: maSigPro

18. RNA-Seq drug dose response (1000 and 100)/ time series (0, 1, 6 and 12 hrs) gene filtering criteria The data filters used during the last analysis performed (12.08.15): I.maSigPro: NormCounts of genes meeting MaxRawCount>50 served as input; maSigPro output was further filtered against potential outlier genes (flagged by maSigPro) Genes showing FC greater than 1.5 (at least in one of the paired-comparisons); By default maSigPro requires (BH) adjusted p-value <0.05 II.Pairwise comparison criteria: MaxRawCount>50, adjusted-p-value<0.05 and FC greater than 1.5 (at least in one of the paired-comparisons);

19. Exp 4.2: Mouse primary beta cells: Genes meeting criteria: Paired-comparison yields higher 100/1000 gene overlap (than the one obtained with maSigPro) maSigPro filtered outputPairwise-comparison filtered output

20. Exp 4.2: Mouse primary beta cells: Most of maSigPro shared-genes are included in the group of paired shared genes To determine which output is preferred data validation using an orthogonal method is essential

21. Partitioning clustering of genes responsive in both drug concentrations Paired comparison, con=1000, shared_genes partitioning clustering Paired comparison, con=100, shared_genes partitioning clustering Exp 4.2: Mouse primary beta cells:

22. Exp4.1: Mouse Beta Cells, cell line Generally this experiment yielded less significant results When applying the same filters used for the primary beta-cells datasets, very few genes pass; The possibility of using p-value (instead of adjusted-p-value should be tested by the investigator) The level of 100/1000 intersection (shared-genes) is lower here compared to the one observed in the primary cells experiment

23. Venn diagram of unfiltered maSigPro outputs of both the primary (100, 1000) and the cell-line TranSeq datasets Low overall intersection between the primary and the cell-line ‘significant’ genes; Relatively low intersection between the two drug concentrations tested on the beta cell line