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Circulating miRNA Changes in Alzheimer's and Parkinson's Diseases

This workshop explores the changes in circulating miRNA associated with Alzheimer's and Parkinson's diseases. The analysis is based on data from patients' biological samples. The results obtained from a large number of datasets are discussed.

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Circulating miRNA Changes in Alzheimer's and Parkinson's Diseases

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  1. ERCC Data Analysis Workshop Use Case 3: Circulating miRNA Changes Associated With Alzheimer’s and Parkinson’s Diseases Organized and Hosted by the Data Management and Resource Repository (DMRR) Wednesday, Nov 5th, 2014 6:00 – 8:30 pm Data and disease background slides kindly provided by Kendall Jensen, Translational Genomics Research Institute 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. PLoSONE 9: e94839.

  2. Use Case3: miRNA Changes in Alzheimer’s and Parkinson’s Disease Biological Samples to Be Analyzed 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)

  3. Use Case3: miRNA Changes in Alzheimer’s and Parkinson’s Disease 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 datasetsin the coming weeks.

  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. PLoSONE 9: e94839.

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

  6. Genboree Workbench – Create Database • Create a Genboree Workbench Database • http://genboree.org/theCommons/ezfaq/show/public-commons?faq_id=491 • hg19 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. 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.

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

  9. Running the Pipeline: Select Tool

  10. Running the Pipeline: Submit Job

  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]/

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

  13. Post-processing: Select Tool

  14. Post-processing: Submit Job

  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]/

  16. Use Case 3: Pipeline Results –miRNA and Unmapped Read Fractions

  17. Use Case 3: Pipeline Results KJ_miRNA_Quantifications_RPM.txt

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

  19. Use Case3: Summary  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.

  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

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