Computational challenges in Bioinformatics€¦ · Bioinformatics applications for eHealth have...

GRID PON-2000-2006 Catania, IT 10-12 February 2009Milanesi Luciano

Computational challenges in

Bioinformatics

Milanesi Luciano

National Research Council

Institute of Biomedical Technologies, Milan, Italy

[email protected]


The human organism:

~ 3 billion nucleotides

~ 30,000 genes coding for

~ 100,000-300,000 transcripts

~ 1-2 million proteins

~ 60 trillion cells of

~ 300 cell types in

~14,000 distinguishable

morphological structures


Networking resources

Data analysis specific for bioinformatics allow the user to

store and search genetics data, with direct access to the

data files and application on GRID servers.

Researchers

perform their

activities

regardless

geographical

location, interact

with colleagues,

share and

access data

Scientific instruments and

experiments provide huge

amount of data from

microarray


Networks of resources

The potential of new biological and biomedical technological platforms in connection with HPC and GRIDtechnology will be particularly useful to deal with the increasing amount, complexity, and heterogeneity of biological and biomedical data.

Bioinformatics applications for eHealth have become an ideal research area where computer scientists can apply and further develop new intelligent computation methods, in both experimental and theoretical cases.


ICT and Genomics

A key development in the computational world has been the

arrival of de novo design algorithms that use all available

spatial information to be found within the target to design

novel drugs.

Coupling these algorithms to the rapidly growing body of

information from structural genomics together with the new

ICT technology (eg. HPC, GRID, Web Services,

Bioinspired networks ecc.)

provides a powerful new possibility for exploring design to a

broad spectrum of genomics targets, including more

challenging techniques such as:

protein–protein interactions, docking, molecular

dynamics, system biology, gene network ecc.


System Biology for Health

http://www.ncbi.nlm.nih.gov/cgi-bin/BLAST/nph-newblast?SEQUENCE=LSGEDR&PROGRAM=blastp&DATALIB=nr

http://www.ncbi.nlm.nih.gov/cgi-bin/BLAST/nph-newblast?SEQUENCE=SEIKR&PROGRAM=blastp&DATALIB=nr

http://www.ncbi.nlm.nih.gov/cgi-bin/BLAST/nph-newblast?SEQUENCE=YYNSDDK&PROGRAM=blastp&DATALIB=nr


Data mining

db

PROTEOME

TRANSCRIPTOME

GENOME

Identify

USEFUL and SIGNIFICANT

information


Allen Institute for Brain Science

http://www.biomedcentral.com/1471-2105/9/153/figure/F2?highres=y


What level

At what level can a systems biology strategy be implemented?

Faugeras O. et al., 2007, Journal of Physiology


The use of Statistical Parametric Mapping

Analysis (SPM) for the quantification of

hypometabolic patterns in brain is

currently the standard within the

neurological research community as

regards an analysis of PET/SPECT

studies for the early diagnosis of AD.

The use of Grid technologies allows easy

access to distributed data as well as to

distributed computational resources in a

secure way.

Remote access to SPM and to distributed

databases of normal subjects has been

made available through a Grid portal.

The original SPM scripts for data analysis

have not been modified. Only the SPM

routines concerning access and

extraction of information from normal

images have been rewritten in order to

allow parallelization and Grid

implementation.

SPM for the early diagnosis of AD



Study and implementation of innovative techniques for multiple sclerosis

lesions classification based on an integrated approach which uses ontologies

for a formal description of the domain and a fuzzy-based reasoning engine to

perform lesion classification.

Ontology based approach for the discovery of anomalies in the segmentation

process of brain tissues.

Brain NMR Brain tissue segmentation Automatic Reasoning on formalized knowledge Lesion

discovery


• Distributed Architectures for

DICOM images DBs

• Integration of textual data and

image features and descriptors

• Image retrieval by template

based on analysis of

– Histogram

– Texture

– Shape

User Interface for

Queries by

example

Reference Image

Visual query

Parameters

Template

Correlation threeshold

Histogram max diff

Entropy max diff

Biomedical Images Databases

BRAIN


BRAIN

Simulation of Associative/Intuitive Capabilities

Data-Driven “Conceptual Spaces”

– Based on psychological

foundations of Latent Semantic

Analysis

Sub-symbolic and symbolic

approaches combined together

– a conceptual similarity

relationship layer added to an

ontology

– tools for introduction of new

concepts in existing ontologies



Description, matching and retrieval of 3D anatomical data

(Extended Reeb graphs and size functions)


Example of data integration

•Entrez Gene

•Entrez SNP

•Entrez HomoloGene

•Entrez GEO

•ArrayExpress

•SMD

•HPRD

•BioGRID

•String

GENOMETRANSCRIPTOME

PROTEOMEAnnotated Pathways

•KEGG Pathway

•Reactome

•GO Biological Process

db


The GeneNerveCellDB


Genome-wide analysis

Current interest in the genome-wide analysis of cells at the

level of transcription ('transcriptome') and translation

('proteome'), the third level of analysis is the 'metabolome'.

The term 'metabolome' refers to the entire complement of all

the small molecular weight metabolites inside a cell

suspension of interest.

A new level of experiments are required to obtain an overall

picture of when, where, and how gene are expressed.

The functional genomics includes:

The analysis of gene expression profiles at the mRNA and

protein levels

The analysis of polymorphism or mutation patterns in the

genome


Disease resistant population Disease susceptible population

Genotype all individuals for thousands of SNPs

ATGATTATAG ATGTTTATAG

Resistant people all have an ‘A’ at position 4 in geneX,

while susceptible people have a ‘T’

geneX

Disease Network


Illumina Chips

0

50

100

150

200

250

300

350

0 100 200 300 400 500 600Jobs

Ho

urs

Grid - measured

Cluster

Single CPU

Log. (Grid - interpolated)


• This approach is mostly useful in high-end challenges, where

Grid overheads are less affecting overall execution times

compared to single CPU performances. Only very small

challenges may show higher efficiency when run in a single

CPU workstation.

Performaces

Single

CPU

Cluster(70 nodes)

(280 CPUs)

Grid*interp.

10 k 200 6 33 h 8 h 8 h

66 k 1320 35 220 h 9.5 h *30 h

100 k 2000 60 333 h 10 h 35 h

317 k 6340 172 1056 h 13 h *72 h

370 k 7400 206 1233 h 15 h *75 h

500 k 10000 278 1665 h 16 h 80 h

670 k 13400 373 2233 h 18 h *87 h

1 M 20000 556 3332 h 20 h 100 h

Illumina

Chip # Runs [50 SNP]

# Jobs [6 h]

Comput. Cost (time)

VNASJobsWorkflow

Results

Grid

U.I.

submission


Parametric Linkage Analysis

LOD score function for whole Chromosomes 2 and 17 (1M SNPs)


Chro

m. 2

Chro

m. 17

LOD Score > 3

→ high probability of linkage

between these markers/loci and

the disease

(the likelihood of observing the

given pedigree if the two loci are

not linked is less than 1 in 1000)


System Biology pipeline

Significant

information

Databases

Models of molecular systems:

gene regulation

signal transduction

Metabolism

Simulations, structural

and dynamycal analyses

Hypotheses

formulation

Wet experiments

Data integration /

data mining

Reverse

engeenering

Model predictions

Biological knowledge

and open problems

Hypotheses

Acceptance/

rejection

Data collection


NF-kappaB family


Inactivation of NF-kB pathway by steroids.


eHealth interoperability

Connecting eHealth services

The full benefits of eHealth services and tools will not reach patients unless a high level of interoperability is integrated at the heart of their design and deployment. Healthcare providers need to co-operate extensively with each other, and with their suppliers, to ensure that their services are well connected.

The European Union's eHealth action plan seeks to harness Information and Communication Technologies to provide better healthcare for the entire EU population.

Central to that project is the development of interoperable healthcare systems in and across Member States.

The plan calls for urgent action to set up health systems and services which are connected at local, regional, national and pan-European levels.

Early and wide collaboration is critical to share costs, thereby reducing the need for future reinvestment to update systems to ensure interoperability.

The development of eHealth systems should underpin better organisation and delivery of health services, and improve citizens' awareness of how to prevent disease and preserve good health.

For example, when eHealth systems are able to communicate with each other effectively, doctors in different hospitals, or even different countries, can manage a patient's care more efficiently.


www.bioinfogrid.eu

GRID PON-2000-2006 Catania, IT 10-12 February 2009Milanesi Luciano 26

BioinfoGrid publications

Participation in external events constituted a very

effective way of raising awareness about the

BioinfoGRID project and its activities. The project

has participated to 58 international conferences

over 24 months

The project has published 23 articles on scientific

journals

The project has published 19 papers on

conference proceedings


Gene expression data Analysis

Gene expression


Docking: predict how

small molecules bind

to a receptor of

known 3D structure

Starting compound

databaseStarting target

structure model

DOCKING

Predicted

binding models

Post-analysis

Compounds

for assay

WISDOM Virtual screening process

There are successful examples

rapid,

cost effective…

But there are limitations

CPU and storage needed


Virtual screening process

A few target structures

Millions of chemical

compounds

1 to 30 mn by docking

A few MB by output

100 CPU years, 1 TB

Large scale deployment on grid infrastructure

Challenges: - Speed-up the process - Manage the data

Docking

software


http://www.eu-egee.org/


http://www.edges-grid.eu:8080/web/edges


http://www.symbiomatics.org/


http://www.bbmri.eu/


http://www.cilab.upf.edu/aneurist1/


http://www.eu-acgt.org/


http://assist.iti.gr/


http://www.euresist.org/


http://www.health-e-child.org/


http://www.cfin.au.dk/


http://www.immunogrid.org/


http://www.livinghuman.org/


http://www.multiknowledge.eu/


http://www.biotec.tu-dresden.de/sealife


http://www.eu-share.org/


http://www.europhysiome.org/


http://www.virolab.org/


http://www.action-grid.eu/


http://www.vph-arch.eu/


http://www.euheart.eu/


http://www.eibir.org/


http://imppact.icg.tugraz.at/index.html


http://www.neomark.eu/portal/


http://www.passport-liver.eu/Homepage.html


http://www.predictad.eu/


http://www.vph-noe.eu/


http://www.vphop.eu/


Conclusion

SysBioHealth

Population

“ Someday we will understand how

more than 3 billion nucleotides will

be able to produce more than

300,000 transcripts used to build 2

million proteins interacting

between them in more then 60

trillion cells capable to build 14,000

distinguishable morphological

structures just for one man”

~ 3 billion nucleotides

~ 30,000 genes coding for

~ 100,000-300,000 transcripts

~ 1-2 million proteins

~ 60 trillion cells of

~ 300 cell types in

~14,000 distinguishable morphological

structures


BioMedGrid Summer school 2009


BioinfoGRID http://www.bioinfogrid.eu

EGEE Enabling Grid for E-science project http://www.eu.egee.org

FIRB-MIUR LITBIO: Laboratory for Interdisciplinary Technologies in Bioinformatics http://www.litbio.org,

FIRB-MIUR ITALBIONET: Italian Bioinformatics Network

Acknowledgments

http://www.bioinfogrid.eu/

http://www.eu.egee.org/

http://www.litbio.org/

Computational challenges in Bioinformatics€¦ · Bioinformatics applications for eHealth have...

Documents

Transcript of Computational challenges in Bioinformatics€¦ · Bioinformatics applications for eHealth have...