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Your name Dr Karol Kozak ETH Zurich May 2009, Paris OME Large Scale Biological Data Handling and Analysis Using Open Source Alternatives.

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Presentation on theme: "Your name Dr Karol Kozak ETH Zurich May 2009, Paris OME Large Scale Biological Data Handling and Analysis Using Open Source Alternatives."— Presentation transcript:

1 your name Dr Karol Kozak ETH Zurich May 2009, Paris OME Large Scale Biological Data Handling and Analysis Using Open Source Alternatives

2 your name Bioinformatics STORAGE Data acquisition Instrument management Image processing Normalization, QC Archiving Data storage Data mining Large Scale Experiments and Informatics Data flow

3 your name Detection points Data Automation: - Library Handling - Robotics - Microscopy - Image processing - Cell reliability - Hit Definition

4 your name Database architecture (LIMS) MySQL/ PostgreSQL Oracle/DB/SQL Server WEBAPPLICATION DB SCHEME WEB USER INTERFACE Import / Export Read/Write Search Database architecture Command Line Client External Bioinformatics Databases : NCBI, PubMed, SCOPE, PDB, Harvester, …

5 your name Link to microscope STORAGE

6 your name Link to microscope STORAGE

7 your name Link to microscope STORAGE Olympus SCAN R

8 your name Data archiving Microscope –format  BD  ThermoFisher - tiff  Zeiss - lsm  Leica - lei  Olympus  MD JPEG ~2MB Results Database DB After Screen In Screening Process Tape Convert to jpeg 50 x smaller Image processing Data Management Tool ~100MB + numeric results plots

9 your name JPEG ~0.5MB Results Database DB After Screen In Screening Process ETH NAS Users Tape 2x25TB LMC (6 weeks) *.tiff ETH NAS User storage Buffer server LMC interface- user + numeric results plots Data Management Tool

10 your name Dataflow architecture siRNA Inhibit. Library IMAGE SERVER Library DB Screen DB HitBase Published screens Screening process Library checker Data mining Bioinformatics Plates with compounds Screen results Protocol Microscope images Booking system Equipment manager Image analysis Post-processing Pre-processing We need software (Library Handling, QC, Data Mining, Visualization, Export, Flexibility)

11 your name HC/DC

12 your name Read data from database

13 your name Link with image storage

14 your name Data mining STORAGE LIMS Microscope File system Statistics -One/two parameters -Normalization -Correlation -Compare distribution -Ex. Statistical tests, Z- score, Z’, etc Pattern recognition – Machine Learning -Multi-parameters -Clustering -Dimensionality reduction -Check parameter importance

15 your name Classification - Unsupervised learning (Cluster analysis, Clustering) seeks to discover the classes ?

16 your name Classification problem ? - Supervised learning (Classification) assumes classes are known

17 your name 2 class problem ? Positive controlNegative control

18 your name Train data for supervised learning 5 people

19 your name Train data for supervised learning WEKA/R-Project nodes (KNIME) 92% Accuracy Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.

20 your name HC/DC Type I 1 or 2 Input(s) and 1 or 2 Output(s) Type II 1 Input (mostly for visualization) Type III 1 Output (mostly data readers) Each node has submenu (right mouse button) Input data Output data Settings: Parameters Rules Algorithms rules Filters Settings

21 your name HC/DC nodes WEKA/R-Project nodes (KNIME) KNIME nodes HC/DC

22 your name Next-Generation sequencing nodes Type I 1 or 2 Input(s) and 1 or 2 Output(s) Input data Output data Roche 454

23 your name Next-Generation sequencing nodes Type I 1 or 2 Input(s) and 1 or 2 Output(s) Input data Output data SOLEXA Illumina

24 your name Proteomics Type I 1 or 2 Input(s) and 1 or 2 Output(s) Input data Output data

25 your name Drag-and-drop

26 your name Pioneering study

27 your name Plot image parameters/descriptors

28 your name Compound screening

29 your name Menu HC/DC

30 your name Library Handling Data Automation: - Volume - Concentration - Dilutio - Splitting - Take liquid - Barcode

31 your name Workflows for HCS

32 your name Workflows for HCS

33 your name Workflows for HCS

34 your name Workflows for HCS

35 your name Link to OME/OMERO

36 your name Acknowledgements & Partners: Contribution in development: MPI-CBG, Dresden Eberhard Krausz (former) & team Eugenio Fava & team Marc Bickle & team ETH Zurich LMC-RISC: Gabor Csucs & team SystemX Adrian Honegger & team MPI-IB, Berlin Nikolaus Machuy & team

37 your name HCDC: http://hcdc.ethz.ch Workshops - Webinar: 04.06.2009 2-4pmhttp://hcdc.ethz.ch WebPage Workshops HCDC+ KNIME: 16-17.10.2009 2 days ETH Zurich


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