IBU

'A bioinformatic Problem Solving Environment in the e-BioLab'

VL-e Sub Program 1.5: Bioinformatics

Timo Breit

Micro-Array Department &

Integrative Bioinformatics Unit Faculty of Science,

University of Amsterdam

IBU

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Where in the Virtual Laboratory for e-Science?

Generic Virtual Laboratory e-science layer

Application Layer

Bio

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Food

Info

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Grid Layer

‘BI-PSE’

BioInformaticsProblemSolvingEnvironment

IBUWhy in the VL-e?Data explosion in life sciences research.

RNA analysis by Northern blot: 1-15 genes

Analyzedgenes

A B C D E F G H I J K L M N O P Q R S T

Samples of cellular experiments

RNA analysis by micro-array: 1.000-40.000 genes

A B C D E F G H I J K L M N O P Q R S T

IBULife sciences research today:whole system –omics data.

Biology

Genomics

Transcriptomics

Proteomics

Metabolomics

Integrative biologyor Systems biology

ExperimentRNA

protein

metabolite

DNA

Biotechnology

Results

Bioinformatics

Data storage

Data handling

Data preprocessing

Data analysis

Data integration

Data interpretation

Biologist

Informatics

ICT infrastructure

IBUHow in VL-e?A bioinformatics problem solving environment (BI-PSE)

a.o.: security (AAA)ICT infrastructure

Life sciencesdomain

e-bioscience

Genericvirtuallaboratory

Grid-layer

a.o.: analysis methodsinformation management

semantic modelingadaptive inf. disclosure

a.o.: domain knowledgedomain information

domain data

a.o.: semantic modeling

Hypothesisgeneration In-silico

experiment

Decisionprocess

Experimentdesign

HypothesesWet-lab

experiment Enhancingknowledge

model

Results

X

ICE

ICE: Interactive & Creative Environment

RESULT: Rauwerda et al: The Promise of a virtual lab. Drug Discov Today. 2006 Mar;11(5-6):228-36.

ESE

ESE: Experiment Support Environment

DSE

DSE: Decision Support Environment

Problem solving environment

IBUParts of the BI-PSE we work on

VL-e

Biological use caseHuntington Disease

Biological use caseToxicogenomics

Gridcomputing

Resource ICE Resources

identification modelStaffPD Christian HenkelPD Ramin Monajemi

e-BioLabStaffSS Han RauwerdaSP vacancy

StaffPD Scott MarshallPD Tessa PronkSP Frans Verster

StaffPD Marco RoosAIO Lennart Post

IB-ICE IB-ESEIntegrative bioinformatics

knowledge model experiment designStaffMAD Martijs JonkerMAD Oskar Bruning

StaffPD Marcia ad IndaSP vacancy

M-A ESEMicroarray analyses

methods workflows

VL-e Use case SigWin VL-e Use case Histone

IBUBasic configuration of e-BioLabVL-e use case SigWin finder

Goal: A workflow to find significant windows in data related to a given sequence (of any type).

Motivation: Find sets of genes (windows) with increased overall gene expression (significance) in expression data ordered by gene location on the chromosomes (sequence).

gene

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IBUBasic configuration of e-BioLabSigWin: Significant Windows*

Márcia Alves de Inda, Dimitri, Frans Verster, Marco Roos

Given a data set we compute Sliding Window (SW) Medians for a given window size.

Using the SW Medians data we compute a False Discovery Rate (FDR) threshold.

Windows with values above the FDR threshold are called significant windows (or Windows Beyond the Threshold)

*R. Versteeg et al. Genome Res 2003 13: 1998-2004.

IBUBasic configuration of e-BioLabVLAM SigWin-finder workflow

1) Read sequence

2) Rank sequence

3) SW Medians

4) Sample to Frequency

5) SW Medians Prob

6) FDR Threshold

7) WinBeTs

8) GnuPlot

Modules

IBUBasic configuration of e-BioLabSigWins and periodic data

IBUBasic configuration of e-BioLabExample periodic data: Temperature in Amsterdam

IBUBasic configuration of e-BioLabIntegration genomic & transcriptomics data

IBUBasic configuration of e-BioLabIntegration genomic & transcriptomics data (zoom)

IBUBasic configuration of e-BioLabVL-e use case Histone code and semantic modeling

Lennart Post, Scott Marshall, Marco Roos

HypothesisA relationship exists between histone modification and

transcription factor binding sites

Histones

Histone modification

Transcriptionfactor binding site

Transcription factor

Transcription

IBUBasic configuration of e-BioLabDesign ‘myModel’: Protégé - OWL plug-in

http://protege.stanford.edu

IBUBasic configuration of e-BioLabData integration through semantic modeling

IBUBasic configuration of e-BioLabResult data integration via semantic modeling

L

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UCSC genome browser snapshot

Result:Correlation between histone modification and

transcription factor binding sites

etc…

Overlap

IBU

BioinformaticsProblem Solving Environment

Domain interaction: Basic concept of an e-BioScience Laboratory (e-BioLab)

non formalized knowledge + ideas + intuition + discussion

BiologistsBiologists

e-BioScientist

Tools GridMethods Workflows

Basic model of problem area

e-BioOperator

Readily accessible data + models data mining

Easy visualization

Small integration experiments+ integration methods

Vague results

IBUBasic configuration of e-BioLab

a glass switch g video cameras m tables on wheels s printer tableb tiled display h desktop PCs n tablet PCs t file cupboardc barebones i plasma touch screen o lab chairs u headsetsd plasterboard wall j electronic whiteboard p desk chairse mobile tiled display k speakers q desk blocksf mobile barebones l office desk r A3 inkjet printer

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VL e-bio laboratory k

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Basic set-up of the e-BioLab

IBUBasic configuration of e-BioLabAnticipated tiled display in e-BioLab

SO

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P1 cluster 1

P2 cluster 1

P3 cluster 1

1

2

P2 cluster 2

P3 cluster 2

P1 cluster 2

3

P2 cluster 3

P3 cluster 3

P1 cluster 3

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Gene lists

Chrom.map 1

Chrom.map 2 Chrom.map 3

Remote whiteboard

Pathways displayed

IBUBasic configuration of e-BioLabAcknowledgements

Within SP1.5:Marco Roos Molecular biologist Han Rauwerda BioinformaticiaRoel van Driel Biochemist Christiaan Henkel Molecular BiologistLennart Post AIO (vDriel) Martijs Jonker BioinformaticianMarcia Alves de Inda Computational scientists Oskar Brunning BioinformaticianScott Marshall Informatician Tessa Pronk Molecular biologistFrans Verster Scientific programmer Ramin Monajemi InformaticianTimo Breit Molecular biologist

Within VL-eSP1.2; use ontologies in semantic modelingSP1.4; use case R on Grid, e-bioscienceSP2.2; AID; ontologies and semantic modelingSP2.4; information managementSP2.5; workflow methods and toolsSp3.3; e-BioLabSP4.1: VLEIT team

More information:www.micro-array.nl

Outside VL-eBioRange, NBIC; Dutch bioinformatics- Content driven data modeling (Kok-LUMC, Adriaans,-UvA etc…)- Test case systems biology (RUG, CMBI, TNO, UvA, etc…)- SigWin (vKampen-AMC etc…)- E-BioLab (vdVeer-VU, vd Vet-UT, Nikhef, SARA,etc…)BioAssist- Microarray workflow (many….)- Reannotatie (Leunissen-WU, Neerincx-WU etc…)

Vacancies @ IBU:Bioinformatician: micro-array data analysis (HBO/WO, 2 years)Scientific Programmers: building the e-BioLab

IBUWhere in the Virtual Laboratory for e-Science?

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Data

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Generic Virtual Laboratory e-science layer

Application LayerB

ioin

form

atic

sA

SP

Bio

dive

rsity

Food

Info

rmat

ics

Grid Layer

‘IB-PSE’

IntegrativeBioinformaticsProblemSolvingEnvironment

BioR

ange

&Bi

oAss

ist

IBU

SP1. Bioinformatics for Microarray Technology

1. Experimental design

2. Understanding biological processes

3. Genotype-phenotype analysis

4. Dissemination of bioinformatics tools and expertise, and education

SP2. Bioinformatics for Proteomics and Metabolomics

5. Preprocessing and identification tools

6. Analysis and modeling tools

7. Molecular interactions tools

SP3. Integrative Bioinformatics.

8. Structural genomics

9. Comparative genomics

10. Phenotype-genotype modelling

11. Pathway modelling and visualisation

12. Content driven data modelling

13. Content driven text mining

SP4. VL-E Informatics for Bioinformatics Applications.

14. Adaptive information disclosure

15. User interface and visualization

16. Collaborative information management

SP5. Test bed with “Real-Life Applications”.

17. Selection of bioinformatics applications, scaling approach, & real-life test applications

18. Dedicated scaling and validation approach

19. Integrated scaling and validation approach

Dissemination

Subprograms & research themes in national bioinformatics initiative BioRange.

Bio

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ICT

in

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ture

IBUUse cases (user scenarios)

1. R on grid (IUC1.5.1) (finished)

• Creation of a web service that executes an R-script that invokes a LAM-MPI distributed calculation on the grid on a number of nodes that can be chosen by the user.

2. R in workflows (IUC1.5.4) (started)

• Proof of principle of a micro-array analysis workflow by invocation of web services. Requirements are visualization of intermediate results and enabling human interaction.

3. Re-annotation of micro-array libraries (IUC1.5.5) (started, with J. Leunissen WU)

• Re-annotation from sequence by invocation of remotely hosted web services in a workflow environment.

4. ‘SigWin’ (IUC1.5.3) => Significant Window Finder (proof of principle given)

• Generalization of method that finds ‘Regions of IncreaseD Gene Expression’ (RIDGEs) into workflow in VLAM environment that finds significant windows in sequences of values.

5. Histone Code case 1 (IUC1.5.2) (proof of principle given)

• Proof-of-concept data integration via semantic models

6. Scaling problems semantic data integration (RUC1.5.1) (Finished, lead to 2 new IUCs)

• Provide guidelines for the infrastructure to use for semantic data integration

IBUA view on bioinformatics research and IBU

Informaticsresearch

Appliedbioinformatics

Bioinformaticsresearch

Biologyresearch

Bio - - informaticsIBU

IBUOutline of presentation.

- Where are we in positioned in the VL-e project?

- Why do we need a Integrative Bioinformatics Problem Solving Environment?

- What do we want to do with a IB-PSE?

- How do we think to create a functional IB-PSE?

- Who are we?

- Where do we start?

- When do we think we will have a functional IB-PSE?

- Who are our collaborators?

IBUWhat do we want to do with a IB-PSE?Concept of integrative bioinformatics

Analysismethods

ICT infra-structure

Experiment design

VL-e

Integrative & computationalbioinformatics

experiment

Model

Visualization

Biologicalsolutions

Biologicalphenomena

Biologicalknowledge

Omicsdata

Data-driven

hypothesis

Problem-driven

hypothesis

biologicalproblem

Biological research domain e-bioscience core domain Enabling science domain

IBUComputational experimentation through advanced data integration.

17 1 195.9 96.75 142.49 71.95 245.36 150.33 309.75 219.68 2.024806 1.980403 1.632143 1.410005 1.31657317 1 297.89 140.18 135.29 72.31 299.44 208.34 316.12 163.49 2.125054 1.870972 1.437266 1.933574 1.18546918 1 258.88 133.89 198.39 99.32 269.61 152.15 600.04 501.95 1.933528 1.997483 1.772001 1.195418 1.32472418 1 343.7 182.82 185.06 93.88 223.53 131.69 381.29 256.01 1.879991 1.97124 1.697395 1.489356 1.20851319 1 420.56 246.45 242.37 117.64 313.9 198.39 362.91 209.43 1.706472 2.060269 1.582237 1.732846 1.13624319 1 356.92 203.84 239.09 121.24 230.15 134.61 379.83 219.32 1.750981 1.972039 1.709754 1.731853 1.08176820 1 917.96 550.93 744.69 312.29 715.53 381.94 1012.41 692.51 1.666201 2.38461 1.873409 1.461943 1.21450820 1 929.84 495.35 722.07 270.12 534.66 288.89 723.47 381.34 1.877137 2.673145 1.850739 1.897178 1.21408321 1 633.48 443.86 316.97 166.45 295.89 231 431.29 281.97 1.427207 1.904296 1.280909 1.52956 1.1853921 1 491.55 296.56 305.4 147.29 275.9 191.24 355.25 192.53 1.657506 2.073461 1.44269 1.845167 1.13477222 1 1695.87 800.25 2772.45 458.42 516.05 435.22 450.85 337.16 2.119175 6.047838 1.185722 1.337199 3.23712622 1 1670.69 501.76 3217.71 395.69 410.16 335.64 422.77 288.3 3.32966 8.131896 1.222024 1.466424 4.2632621 2 1394.88 757.09 908.91 549.26 940.94 542.6 681.48 651.54 1.842423 1.65479 1.734132 1.045953 1.2579521 2 2002.18 1155 863.68 509.28 926.69 507.4 801.16 817.44 1.733489 1.695884 1.82635 0.980084 1.2219682 2 316.65 157.76 182.51 90.46 316.21 195.7 351.78 218.51 2.007163 2.017577 1.615789 1.609903 1.2477132 2 694.9 442.9 210.75 106.14 282.96 166.7 364.46 286.64 1.568977 1.985585 1.697421 1.27149 1.1972613 2 197.62 95.71 163.28 67.84 241.07 126.59 326.33 142.05 2.064779 2.40684 1.904337 2.29729 1.0642593 2 508.98 303.32 176.03 75.66 240.2 122.34 292.99 137.14 1.67803 2.326593 1.963381 2.13643 0.9767824 2 223.91 124.2 169.5 83.46 341.53 207.71 653.56 594.13 1.802818 2.030913 1.644264 1.100029 1.3969844 2 600.34 311.24 191.92 92.05 239.36 139.3 327.7 173.77 1.928865 2.084954 1.718306 1.885826 1.1136725 2 204.66 91.38 153.4 69.72 364.43 222.92 310.61 141.53 2.239659 2.200229 1.634802 2.194658 1.1594035 2 306.11 156.82 172.75 73.17 217.55 102.43 328.43 183.87 1.951983 2.36094 2.123889 1.786208 1.1030226 2 1721.28 759.11 1359.44 580.02 869.26 577.31 911.75 516.62 2.267497 2.343781 1.505708 1.764837 1.4099426 2 1911.88 791.52 1263.34 526.91 831.29 548.07 897.03 556.1 2.415454 2.397639 1.516759 1.613073 1.5378127 2 330.21 177.54 217.86 97.29 299.83 168.65 403.61 236.38 1.859919 2.239285 1.777824 1.707463 1.1761457 2 428.87 208.77 232.24 103.04 368.87 194.28 310.05 145.07 2.05427 2.253882 1.898651 2.137244 1.0674598 2 488.6 366.88 432.12 314.93 455.17 330.7 520.66 412.96 1.331771 1.372114 1.376383 1.2608 1.0252938 2 702.34 487.23 455.52 313.5 625.33 527.92 468.02 388.88 1.441496 1.453014 1.184517 1.203508 1.2120949 2 263.17 174.35 186.88 126.12 247.88 174.32 281.02 194.52 1.509435 1.481763 1.421983 1.444684 1.0434419 2 511.42 316.47 204.19 129.8 446.28 377.22 292.81 187.43 1.616014 1.573112 1.183076 1.562237 1.161662

10 2 429.35 289.02 262.78 181.07 409.12 312.51 307.54 230.44 1.485537 1.451262 1.309142 1.334577 1.11085910 2 548.31 353.24 271.8 185.09 401.98 312.77 321.65 207.52 1.552231 1.468475 1.285226 1.549971 1.06543111 2 244.36 140.01 131.97 72.29 247.63 154.7 254.77 192.64 1.745304 1.825564 1.600711 1.322519 1.22154911 2 411.04 226.5 133.78 75.34 188.31 112.98 295.32 194.54 1.814746 1.775684 1.666755 1.518043 1.12736512 2 240.88 134.74 150.62 85.64 287.61 197.51 229.57 135.52 1.787739 1.758758 1.456179 1.693994 1.1258112 2 553.57 307.88 156.5 86.32 187.27 121.98 266.52 139.05 1.798006 1.813021 1.535252 1.916721 1.04607613 2 619.69 382.88 501.52 318.36 547.3 478.67 521.16 473.07 1.618497 1.575324 1.143376 1.101655 1.422617

Data source A

Randomiser

Source:‘RNA sequences’

N-pletdistributions

N-pletdistributions

Code-lengthdistributor

Code-lengthdistributor

Binomialtester

Coding lengthlikelihood distribution

Optimumextractor

Most likely:triplet

Computationalexperiment

Semanticmodelling Interface model A

17 1 195.9 96.75 142.49 71.95 245.36 150.33 309.75 219.68 2.024806 1.980403 1.632143 1.410005 1.31657317 1 297.89 140.18 135.29 72.31 299.44 208.34 316.12 163.49 2.125054 1.870972 1.437266 1.933574 1.18546918 1 258.88 133.89 198.39 99.32 269.61 152.15 600.04 501.95 1.933528 1.997483 1.772001 1.195418 1.32472418 1 343.7 182.82 185.06 93.88 223.53 131.69 381.29 256.01 1.879991 1.97124 1.697395 1.489356 1.20851319 1 420.56 246.45 242.37 117.64 313.9 198.39 362.91 209.43 1.706472 2.060269 1.582237 1.732846 1.13624319 1 356.92 203.84 239.09 121.24 230.15 134.61 379.83 219.32 1.750981 1.972039 1.709754 1.731853 1.08176820 1 917.96 550.93 744.69 312.29 715.53 381.94 1012.41 692.51 1.666201 2.38461 1.873409 1.461943 1.21450820 1 929.84 495.35 722.07 270.12 534.66 288.89 723.47 381.34 1.877137 2.673145 1.850739 1.897178 1.21408321 1 633.48 443.86 316.97 166.45 295.89 231 431.29 281.97 1.427207 1.904296 1.280909 1.52956 1.1853921 1 491.55 296.56 305.4 147.29 275.9 191.24 355.25 192.53 1.657506 2.073461 1.44269 1.845167 1.13477222 1 1695.87 800.25 2772.45 458.42 516.05 435.22 450.85 337.16 2.119175 6.047838 1.185722 1.337199 3.23712622 1 1670.69 501.76 3217.71 395.69 410.16 335.64 422.77 288.3 3.32966 8.131896 1.222024 1.466424 4.2632621 2 1394.88 757.09 908.91 549.26 940.94 542.6 681.48 651.54 1.842423 1.65479 1.734132 1.045953 1.2579521 2 2002.18 1155 863.68 509.28 926.69 507.4 801.16 817.44 1.733489 1.695884 1.82635 0.980084 1.2219682 2 316.65 157.76 182.51 90.46 316.21 195.7 351.78 218.51 2.007163 2.017577 1.615789 1.609903 1.2477132 2 694.9 442.9 210.75 106.14 282.96 166.7 364.46 286.64 1.568977 1.985585 1.697421 1.27149 1.1972613 2 197.62 95.71 163.28 67.84 241.07 126.59 326.33 142.05 2.064779 2.40684 1.904337 2.29729 1.0642593 2 508.98 303.32 176.03 75.66 240.2 122.34 292.99 137.14 1.67803 2.326593 1.963381 2.13643 0.9767824 2 223.91 124.2 169.5 83.46 341.53 207.71 653.56 594.13 1.802818 2.030913 1.644264 1.100029 1.3969844 2 600.34 311.24 191.92 92.05 239.36 139.3 327.7 173.77 1.928865 2.084954 1.718306 1.885826 1.1136725 2 204.66 91.38 153.4 69.72 364.43 222.92 310.61 141.53 2.239659 2.200229 1.634802 2.194658 1.1594035 2 306.11 156.82 172.75 73.17 217.55 102.43 328.43 183.87 1.951983 2.36094 2.123889 1.786208 1.1030226 2 1721.28 759.11 1359.44 580.02 869.26 577.31 911.75 516.62 2.267497 2.343781 1.505708 1.764837 1.4099426 2 1911.88 791.52 1263.34 526.91 831.29 548.07 897.03 556.1 2.415454 2.397639 1.516759 1.613073 1.5378127 2 330.21 177.54 217.86 97.29 299.83 168.65 403.61 236.38 1.859919 2.239285 1.777824 1.707463 1.1761457 2 428.87 208.77 232.24 103.04 368.87 194.28 310.05 145.07 2.05427 2.253882 1.898651 2.137244 1.0674598 2 488.6 366.88 432.12 314.93 455.17 330.7 520.66 412.96 1.331771 1.372114 1.376383 1.2608 1.0252938 2 702.34 487.23 455.52 313.5 625.33 527.92 468.02 388.88 1.441496 1.453014 1.184517 1.203508 1.2120949 2 263.17 174.35 186.88 126.12 247.88 174.32 281.02 194.52 1.509435 1.481763 1.421983 1.444684 1.0434419 2 511.42 316.47 204.19 129.8 446.28 377.22 292.81 187.43 1.616014 1.573112 1.183076 1.562237 1.161662

10 2 429.35 289.02 262.78 181.07 409.12 312.51 307.54 230.44 1.485537 1.451262 1.309142 1.334577 1.11085910 2 548.31 353.24 271.8 185.09 401.98 312.77 321.65 207.52 1.552231 1.468475 1.285226 1.549971 1.06543111 2 244.36 140.01 131.97 72.29 247.63 154.7 254.77 192.64 1.745304 1.825564 1.600711 1.322519 1.22154911 2 411.04 226.5 133.78 75.34 188.31 112.98 295.32 194.54 1.814746 1.775684 1.666755 1.518043 1.12736512 2 240.88 134.74 150.62 85.64 287.61 197.51 229.57 135.52 1.787739 1.758758 1.456179 1.693994 1.1258112 2 553.57 307.88 156.5 86.32 187.27 121.98 266.52 139.05 1.798006 1.813021 1.535252 1.916721 1.04607613 2 619.69 382.88 501.52 318.36 547.3 478.67 521.16 473.07 1.618497 1.575324 1.143376 1.101655 1.422617

Data source B

Semanticmodelling

Ontology B

Interface model B

Ontology A

IBUBioinformatics in the Netherlands

University of Amsterdam

NBIC- Bioinformatics

NBIC, national foundation for Dutch bioinformatics. Involves all academic and several industrial life sciences research organizations.

VL-e Consortium- Informatics

VL-e, informatics Bsik project by WTCW supporting BiOrange.

Local bioinformatics initiatives, mainly focused on directly supporting specific local life sciences research questions.

VL-E Experimental

(rapid prototyping) Environment

VL-E Proof-of- concept

Environment

VL-E Exploitation Environment

(SARA)

BiOrange Proof-of- concept

Environment

Life sciences researchers mainly focused on resolving

specific life sciences research questions.

BioRange Bioinformatics

Research

BiOrange, bioinformatics Bsik project by NBIC and “Nationaal Regieorgaan Genomics”.

BioASP, Bioinformatics Service Provider for life sciences researchers by NBIC and “Nationaal Regieorgaan Genomics”.

Bio-Application

SupportProgram

(Bio-ASP)

IBU

component interaction

stimuli

mechanism

program

history

response

presence state

Data integration: basic concept of any cell

Assumption: the complexity of life is organized via a limited number of general cellular mechanisms.

ED DA

DA

DI

LC