Center for Clinical Genomics and Personalized Medicine at the University of Debrecen, Hungary

1. Center for Clinical Genomics and
Personalized Medicine
2014
Bálint L. Bálint MD, PhD
Head of Laboratory

2. Center for Clinical Genomics and Personalized Medicine
IVDI Building 3-d floor

3. http://genomics.med.unideb.hu

4. Our Team

5. Our Infrastructure
Biobanking
Microarays
NG-Sequencing
Bioinformatics

6. Students coming from
Medical Centre service area
1.45 hours

7. Facts about the University of
Debrecen
 33000
students
 Teaching
 More
in Hungarian and English
than 1500 faculty members
 15
branches
 21
Doctoral Schools
 15%
of Hungarian RnD is located here

8. Brief overview of the region:
Underdeveloped region with large Regional Developmental Funds
University City with long history
Medical Centre that serves 2 mil people
Pharmapolis Cluster:
TEVA (Biogal)
Richter Gedeon new Pharma Plant for Biological drugs (recombinant proteins and
antibodies)
Cell Therapy Unit (already performing clinical studies)
Stem Cell Research Unit (starts 2010)
RCMM twinning with EMBL
Several EMBO members locally
Personalized Health Care Unit (from 2011)
Well organized Medical Centre open to patients from foreign countries
SAP and ISO implementation at the Medical Centre level

9. Our vision is to channel the advances in
genomics towards medical research for the
benefit of the patients in the form of
Personalized Medicine

10. Goals:
 Leader
in the Personalized Medicine in the region
 Implementation
of novel research tools for the benefit
of the patients through clinical research
 Take
the opportunity of the regional grants and use
them to develop tools that can be used on a global
scale
 Taking
care of every spent pence in a very
conservative way

11. Activities to implement our Goals:
 Activities

in two directions:
Clinical Research



Common projects with clinicians
Infrastructure for clinical research
Regional training

From PCR oligo design through Study design till Data analysis

Global gene expression and functional genomics, Epigenetics
01/22/14
11

12. Activities in the field of Clinical Research:
 Genomics
 National
Genomics Technological Platform
 Biomarker
discovery and Biobanking

Partners: Pfizer, Astra Zeneca, Richter Gedeon, Biosystems International,
Roche Hungary

Biobanking: COPD, Schizophrenia, Lung cancer, Leukemias
Tools for Biomarker discovery: UPL based miRNA measurement
(Roche Hungary and Astrid Research)

 Epigenetics


Partners: Diagenode Belgium and Millipore USA
Areas: developing of standards for epigenetic studies and protocols for
HTS and clinical research in the field of epigenetics
01/22/14
12

13. Coordinated from the University of Debrecen:
1. Genomic Research for Human Health Consortium
(GRHH) 2002-2008- FP6 (www.humangenom.hu)
2. National Genomic Technological Platform (NGTP) 2009-2011National Grant
www.genomika.net

14. Core facility:
 Implementation
of novel technologies require expensive infrastructure
 Expensive
infrastructure can be best used if shared by several
research groups and operated by dedicated staff
 Without
streets
knowledge dissemination core facilities are like dead end

15. Detailed Infrastructure
 Laboratories
 Biobank
depositories
 Offices
 QPCR
Instruments
 QPCR
assays (hundreds)
 Capillary
 Deep
sequencer (4 chanell ABI)
Sequencing System (Illumina HiScan SQ)
 Pipetting
robots (4)
 Microarray
 DNA/RNA
systems (1)
QC instruments (Agilent Bioanalyzer, Nanodrop, Qubit)

16. Relevant output
Lifecycles of technologies
Additional
developments
Original
innovation
Distinctive technology / development phase/
time
Both instrument and reagent costs are high
Peak technology / penetretion phase/
Instrument price is high, reagent price is decreasing
Basic technology / spreading phase/
Both instrument and reagent costs are rapidly decreasing
Based on: „Pataki Béla: Technológia menedzsment”

17. Relevant output
Lifecycles of technologies
Distinctive technology / development phase/
time
Both instrument and reagent costs are high
Peak technology / penetretion phase/
Instrument price is high, reagent price is decreasing
Basic technology / spreading phase/
Both instrument and reagent costs are rapidly decreasing
Based on: „Pataki Béla: Technológia menedzsment”

18. L ab
RE
CO
Sequencing and
genomics unit
Epigenetic unit
b
D La
Rn
Se
b
e La
rvic
Biobanking unit
Protein and cell
engeneereing unit
b
D La
Rn

19. Sequencing and genomics unit
Instruments:
•Capillary sequencer
•Affymetrix
•Illumina NGS
•Server system
La b
RE
CO
Wet lab people:
Post docs:
•Beata Scholtz
•Szilárd Póliska
Technicians:
•Sipos Lilla
•Erzsébet Mátyás
Bioinformatics support team:
Endre Barta (leader)
Attila Horvath (system administrator)
Laszlo Steiner (biomathematics and bio statistics)
Gergely Nagy (bioinformatics)

20. NGS Sequencing
Illumina Sequencing System HiScan SQ-
Szilárd Póliska

21. Automatic Chromatin IP for maximal
reproducibility
Diagenode IP-Star Magnetic ChIP

22. Affymetrix Systems

23. GeneChip® 3000 platform
•
•
Hybridisation Oven
Delivers precise
temperature control
during hybridization
• Fluidics stations
• Washing and staining
of hybridized target
probe complexes
• Scanner
• High resolution
scanner for fast
image acquisition
• Autoloader
including barcode
reader for sample
tracking

24. Bioinformatics
Endre Barta

25. NGS software installed on ngsdeb
Programs used for ChIP-seq,
GRO-seq and RNA-seq analysis
•
HOMER
•
SAMTOOLS
•
BOWTIE
•
ChIPSEEQER
•
BEDTOOLS
•
BAMTOOLS
•
TOPHAT
•
TRINITYRNASEQ
•
BWA
•
MEME
•
MACS
•
FASTX-TOOLKIT
•
PICARD
25
Head Node: 2x6 core, 144GB RAM, 20 Tbyte disk
6x computing nodes: 2x6 core, 48GB RAM 600GB
disk
2012.05.16.

26. ChIP-seq analyze script
•
Aim: To have a simple script that can be used either to analyze local
ChIP-seq sequencing data or to do meta-analysis of ChIP-seq
experiments stored on the NCBI SRA database
•
Command line tool
•
Input: SRA (NCBI reads), fastq (reads), bam (alignments)
•
•
•
•
•
•
•
Downloads SRA format files NCBI
Maps fastq format reads
Peak calling by HOMER and MACS
Peak annotation by HOMER
Known and denovo motif finding by HOMER
GO enrichment analysis by HOMER
Generates bedgraph and bed files for visualization
Barta E
Command line analysis of ChIP-seq results.
EMBNET JOURNAL 17:(1) pp. 13-17. (2011)

27. UPL based miRNS detection system
Czimmerer Zsolt
Astrid
Research
http://genomics.dote.hu:8080/mirnadesigntool/

28. High sensitivity e.g on hsa-mir-181 miRNA gen
family
Hsa-mir-181a:
AACAUUCAACGCUGUCGGUGAGU
Hsa-mir-181b:
AACAUUCAUUGCUGUCGGUGGGU
Hsa-mir-181c:
AACAUUCAAC*CUGUCGGUGAGU
http://genomics.dote.hu:8080/mirnadesigntool/

29. More info:
Balint L. Balint , Head of
Laboratory
Lbalint@med.unideb.hu
http://genomics.med.unideb.hu