1. How to get from a pile of unprocessed data to knowledge: The user’s perspective Guido Jenster, Ph.D. Professor of Experimental Urological Oncology Department of Urology Erasmus MC g.jenster@erasmusmc.nl Data analysis and integration

2. Structure of Cancer Research Projects Functional Research Prevention Research Marker Research Technology & Protocols Datasets Bioinformatics & Statistics Models & Biobanks Organization & Management; Education; Outreach Therapy Research

3. Clinical Research Biobanking Experimental Research Imaging DATA GENERATION DATA STORAGE DATA PROCESSING DATA INTEGRATION DATA QUERY VIEWING Prostate Cancer Molecular Medicine NEW KNOWLEDGE

4. Prostate Cancer Molecular Medicine What do we want? Use case: Identify novel fusion genes from DNA and RNA sequencing data PUSH TO START

5. Where is the Red Button? Why is it so difficult to make? Data analysis and integration -Different types of data -Different platforms and their limitations -Different data analysis tools -Limitations in storage and compute power -Analysis and integration is dependent on research question and the needs of the scientist

6. Markers and therapy targets: An inventory of the differences between normal and cancer cells: Markers and therapy targets for prostate cancer Metabolite DNA RNA Protein Morphology Cellular behavior

7. DNAseq Data Analysis B-Allele Frequency DNAseq data Active Chromatin TF Binding Methylation Structural VariationsChromatin Interactions Copy Number Abberations SNVs / InDels Read Barcode Identify Integration Sites

8. RNAseq Data Analysis Alternative splicing & Promoters RNAseq data Differential expression SNVs / InDels Read-Through & Fusion Transcripts Novel Transcripts

9. DNA and RNA analysis platforms DNA level: -Home made array CGH -1M SNP arrays (Illumina) -Ion Proton low pass DNAseq -Ion Proton exome DNAseq -Complete Genomics whole genome DNAseq -FAIREseq, ChIPseq, MeDIPseq, Methylation arrays (Illumina) RNA level: -Home made cDNA and oligo arrays -Affymetrix Exon arrays -Illumina RNAseq (small RNA and mRNA) -Ion Proton RNAseq

10. Where is the Red Button? Why is it so difficult to make? Data analysis and integration -Different types of data -Different platforms and their limitations -Different data analysis tools -Limitations in storage and compute power -Analysis and integration is dependent on research question and the needs of the scientist

11. Clinical Research Biobanking Experimental Research Imaging DATA GENERATION DATA STORAGE DATA PROCESSING DATA INTEGRATION DATA QUERY VIEWING Prostate Cancer Molecular Medicine Where is the Red Button? How to solve the issues? NEW KNOWLEDGE

12. TraIT subdivision into work packages Four data generating work packages Data integration & analysis across the four platforms Shared hardware and professional training & support

13. The TraIT mansion requires good support Open Clinica BMIA TOP desk Alfresco tEPIS Catalogue Workflow Phenotype Database Chipster Galaxy coLIMS XNAT Keosys Logis tranSMART Website Wiki Jira Data storage + CPU power TTP SurfConext

14. Where is the Red Button? How to solve the issues? Adopt, Adapt, Create Data analysis and integration DATA STORAGE & COMPUTE DATA PROCESSING DATA INTEGRATION DATA MINING VIEWING -Own (external hard drives) -Central CSC, CCBC, GEO, ENA -Commercial Clouds -Own pipelines and tools -Commercial programs CLCBio, etc. -Central / Open Source tool platforms -Own (Access) -Commercial (NextBio) -Central Oracle TRC, tranSMART

15. Data Mining: Query & Viewing Tools cBioPortal Between-Study Level Study Level Patient/Sample Level Molecular Level Platform: Where do I get my data from? Level: Which level do I want to mine? Tool: What is the best query & viewing tool?

16. Prostate Cancer Molecular Medicine What do we want? Use case: Identify novel fusion genes from DNA and RNA sequencing data PUSH TO START

17. Andrew Stubbs http://www.erasmusmc.nl/bioinformatica/ Harmen van de Werken http://www.erasmusmc.nl/ccbc/ Please attend the monthly Bridge Meetings: http://www.molmed.nl/http://www.molmed.nl/(MolMed Lectures)