1. Use Case 3: Circulating miRNA Changes Associated With Alzheimer’s and Parkinson’s Diseases Wednesday, Nov 5 th, 2014 6:00 – 8:30 pm Organized and Hosted by the Data Management and Resource Repository (DMRR) Data and disease background slides kindly provided by Kendall Jensen, Translational Genomics Research Institute ERCC Data Analysis Workshop 1 Burgos K., et al. (2014) Profiles of Extracellular miRNA in Cerebrospinal Fluid and Serum from Patients with Alzheimer’s and Parkinson’s Diseases Correlate with Disease Status and Features of Pathology. PLoS ONE 9: e94839.

2. Biological Samples to Be Analyzed Patient Number SampleInput File NameBiosample Metadata # in KB #1PD Serum SAMPLE_0045_PD_SER.fastq.gz EXR-0SER0045-BS #1PD Cerebrospinal Fluid SAMPLE_0045_PD_CSF.fastq.gz EXR-0CSF0045-BS #2AD Serum SAMPLE_0116_AD_SER.fastq.gz EXR-0SER0116-BS #2AD Cerebrospinal Fluid SAMPLE_0116_AD_CSF.fastq.gz EXR-0CSF0116-BS #3AD Serum SAMPLE_0330_AD_SER.fastq.gz EXR-0SER0330-BS #3AD Cerebrospinal Fluid SAMPLE_0330_AD_CSF.fastq.gz EXR-0CSF0330-BS #4PD Serum SAMPLE_9824_PD_SER.fastq.gz EXR-0SER9824-BS #4PD Cerebrospinal Fluid SAMPLE_9824_PD_CSF.fastq.gz EXR-0CSF9824-BS Input files are located in the Data Selector in the following Group  Database  Folder: Group: exRNA Metadata Standards Database: Use Case 3: miRNA Changes in Alzheimer’s and Parkinson’s Folder: 1. Inputs (FASTQ) Use Case 3: miRNA Changes in Alzheimer’s and Parkinson’s Disease 2

3. Since we have two patients with each disease, we will examine differential expression of miRNAs between CSF and serum within each patient, and then compare the results. We plan to expand this use case to include all ~400 datasets in the coming weeks. 3 Use Case 3: miRNA Changes in Alzheimer’s and Parkinson’s Disease

4. 4 Use Case 3: Results from Burgos et al These results come from a large number of datasets: AD (n=67 CSF and n=64 SER), PD (n=65 CSF and n=60 SER), and control (n=70 CSF and n=72 SER). We cannot replicate them from just a few samples. Note in the CSF case the strong significance for very minor differences in expression in AD vs. PD. Burgos K., et al. (2014) Profiles of Extracellular miRNA in Cerebrospinal Fluid and Serum from Patients with Alzheimer’s and Parkinson’s Diseases Correlate with Disease Status and Features of Pathology. PLoS ONE 9: e94839.

5. Genboree Workbench – Getting Started Getting Started –http://genboree.org/theCommons/projects/pub lic-commons/wiki/Getting_startedhttp://genboree.org/theCommons/projects/pub lic-commons/wiki/Getting_started Genboree Workbench Icons Explanation –http://genboree.org/theCommons/projects/pub lic-commons/wiki/genboree_iconshttp://genboree.org/theCommons/projects/pub lic-commons/wiki/genboree_icons FAQs –http://genboree.org/theCommons/ezfaq/index/ public-commonshttp://genboree.org/theCommons/ezfaq/index/ public-commons 5

6. Genboree Workbench – Create Database Create a Genboree Workbench Database –http://genboree.org/theCommons/ezfaq/show/ public-commons?faq_id=491http://genboree.org/theCommons/ezfaq/show/ public-commons?faq_id=491 hg19 6 Note: - You will be using this newly created Genboree Workbench Database to hold the output of tool runs. This will be the database that we’re referring to when we say ‘your database’. Note: - You will be using this newly created Genboree Workbench Database to hold the output of tool runs. This will be the database that we’re referring to when we say ‘your database’.

7. 7 Running the Pipeline: Select Input Files Note: You will input (1) fastq file per tool run. So, for each fastq file you wish to analyze, you will need to repeat the process shown on the next 3 slides. Note: You will input (1) fastq file per tool run. So, for each fastq file you wish to analyze, you will need to repeat the process shown on the next 3 slides.

8. 8 Running the Pipeline: Select Output Database Drag Your newly created database to Output Targets.

9. 9 Running the Pipeline: Select Tool

10. 10 Running the Pipeline: Submit Job

11. 11 Post-processing: Select Input Files Note: These zip files will be in your database, but will be in the folder that you named: Files/smallRNAseqPipeline/[your analysis name]/ Note: These zip files will be in your database, but will be in the folder that you named: Files/smallRNAseqPipeline/[your analysis name]/

12. 12 Post-processing: Select Output Database Note: Drag Your newly created database to Output Targets. Note: Drag Your newly created database to Output Targets.

13. 13 Post-processing: Select Tool

14. 14 Post-processing: Submit Job

15. 15 Post-processing: Begin Analysis (Excel) Note: The processed files to the left will be in your database, but will be in the folder that you named: Files/processPipelineRuns/[your analysis name]/ Note: The processed files to the left will be in your database, but will be in the folder that you named: Files/processPipelineRuns/[your analysis name]/

16. 16 inputclippedrRNAnot_rRNAgenome miRNA sense miRNA antisense tRNA sense tRNA antisense piRNA sense piRNA antisense snoRNA sense snoRNA antisense miRNA plantVirus sense 045_PD_CSF9667953892362514594527464173590266888434482349761191114161593186 045_PD_SER8446256499306773401442590533525103516132134829136143512786 116_AD_CSF179729611545455020622831339226774773046909292412155054351095333331198 116_AD_SER385482136571033349213322182252036714151706328135547011152493125 330_AD_CSF111802309226454110297981234756651985165061813021346712976527815185 330_AD_SER9030373715636822101474946221423179421333625553761724363710192262 824_PD_CSF652211361437455957685547977395233366657111267928982816505256 824_PD_SER92017543401814494906290690824862348376021012976853378514145395 inputclippedrRNAnot_rRNAgenome miRNA sense miRNA antisense tRNA sense tRNA antisense piRNA sense piRNA antisense snoRNA sense snoRNA antisense miRNA plantVirus sense 045_PD_CSF164%151%25%126%100%15.0%0.0001%39.8%0.0003%0.0189%0.0003%0.0270%0.0000%0.0015% 045_PD_SER240%142%21%121%100%0.1464%0.0009%0.0382%0.0008%0.0039%0.0041%0.0014%0.0001%0.0223% 116_AD_CSF240%207%28%179%100%9.24%0.0032%28.8%0.0005%0.0146%0.0004%0.0045%0.0000%0.0026% 116_AD_SER153%145%13%132%100%56.1%0.0002%13.0%0.0002%0.0186%0.0044%0.0208%0.0037%0.0050% 330_AD_CSF168%139%17%122%100%24.8%0.0000%45.4%0.0001%0.0195%0.0010%0.0418%0.0001%0.0028% 330_AD_SER213%169%52%117%100%50.4%0.0013%12.7%0.0000%0.0103%0.0009%0.0241%0.0000%0.0062% 824_PD_CSF165%155%15%140%100%16.9%0.0000%32.1%0.0002%0.0209%0.0004%0.0128%0.0001%0.0014% 824_PD_SER370%137%20%117%100%33.7%0.0004%5.2%0.0002%0.0136%0.0034%0.0569%0.0002%0.0159% not_rRNAgenome Mapped Fraction Unmapped Fraction 045_PD_CSF7464173590266879.1%20.9% 045_PD_SER4259053352510382.8%17.2% 116_AD_CSF13392267747730455.8%44.2% 116_AD_SER3322182252036775.9%24.1% 330_AD_CSF8123475665198581.9%18.1% 330_AD_SER4946221423179485.6%14.4% 824_PD_CSF5547977395233371.2%28.8% 824_PD_SER2906908248623485.5%14.5% Use Case 3: Pipeline Results – miRNA and Unmapped Read Fractions

17. 17 Use Case 3: Pipeline Results KJ_miRNA_Quantifications_RPM.txt

18. 18 Use Case 3: Processed Pipeline Results Sample 045 Sample 824 Alzheimer’s Sample 116 Sample 330 log10(Fold Change CSF/SER) Parkinson’s R Spearman = 0.70 R Spearman = 0.37 Can you determine what is going on? Look back at the data in slides 16 and 17.

19.  We analyzed a subset of 8 samples from a very large compendium of datasets from Alzheimer’s and Parkinson’s disease patients.  Sample 045-serum is probably a bad sample consisting mainly of mRNA fragments.  Serum contains more small RNA than cerebrospinal fluid. Use Case 3: Summary 19

20. Supplemental Slides See KJ_diagnosticPlots.pdf in the processed pipeline results directory.

21. Heatmap for Different Samples

22. Library Size (Mapped Reads)

23. rRNA Signal

24. Read Count and Reads Per Million for Samples

25. Density Plot for Samples