1

“It took millions of times more computing power to map the human genome than it did to land a man on the moon, and that’s only a fraction of what’s needed right now. The biggest challenge in biology is going to be computing.”

Dr. J. Craig VenterChairman of the BoardThe Institute for Genomic ResearchDec. 2001

The Need For Computing

1

Clusters : What and Why?What?• Collection of computers networked

together to perform a particular application in parallel

Why?• Scalability• Cost-effective

1

Role of Your Cluster Vendor

Expertise with cluster management• Allow you to concentrate on the complexities

of your models – not your computer system

Expertise with turnkey solutions• Open Source distribution• Proven compatibility with

life sciences applications

One-stop support

1

Trend to Clustered Supercomputers

Numbers Overwhelm Size

1

• High throughput screening runs involving multiple, repetitive sequence comparisons on vast amounts of data.

• Data or query can be partitioned and dispatched to nnodes of a cluster to gain direct n-fold speedup and gain in throughput.

• Even the most compute-intensive fine-grain applications (e.g., molecular chemistry) can be deployed on a cluster equipped with a high-performance interconnect fabric.

Clusters in the Life Sciences

1

Situation• Existing infrastructure would have taken

38 years to perform the 22 million genomic sequence comparisons

Solution• Linux Networx cluster of 150 Pentium-3

nodes with ICEBox and Clusterworx management

Results• Study completed in 34 days – 450x

acceleration

Tularik, IncorporatedSan Francisco, California

Case Study—Business Case

1

“Cluster management tools from Linux Networx are setting the standard …. Without cluster management tools, we would be spending five times as long managing the cluster…. ICE tools allow us to concentrate on finding new genes that cause disease and not worry about cluster management”

- Gene CutlerTularik, Inc.

Tularik, IncorporatedSan Francisco, California

Case Study— Business Success

Terascale Linux Clusters: Supercomputing Solutions for the

Life Sciences

Dr. Padmanabhan Iyer, Linux Networx

Dr. Bruce Ling, Tularik

The Need For ComputingThe Need For Computing“It took millions of times more computing power to map the human genome than it did to land a man on the moon, and that’s only a fraction of what’s needed right now. The biggest challenge in biology is going to be computing.”

Dr. J. Craig VenterChairman of the BoardThe Institute for Genomic Research Dec. 2001

Clusters : What and Why?What?•Collection of computers networked together to perform a particular application in parallel

Why?•Scalability•Cost-effective

Expertise with cluster management• Allow you to concentrate on the complexities

of your models – not your computer system

Expertise with turnkey solutions• Open Source distribution• Proven compatibility with

life sciences applications

One-stop support

Role of Your Cluster Vendor

Trend to Clustered Supercomputers

Numbers Overwhelm Size

Clusters in the Life Sciences

• High throughput screening runs involving multiple, repetitive sequence comparisons on vast amounts of data.

• Data or query can be partitioned and dispatched to nnodes of a cluster to gain direct n-fold speedup and gain in throughput.

• Even the most compute-intensive fine-grain applications (e.g., molecular chemistry) can be deployed on a cluster equipped with a high-performance interconnect fabric.

Case Study—Business CaseTularik, IncorporatedSan Francisco, California

Situation• Existing infrastructure would have taken 38

years to perform the 22 million genomic sequence comparisons

Solution• Linux Networx cluster of 150 Pentium-3

nodes with ICEBox and Clusterworxmanagement

Results• Study completed in 34 days – 450x

acceleration

Case Study— Business SuccessTularik, IncorporatedSan Francisco, California

“Cluster management tools from Linux Networx are setting the standard …. Without cluster management tools, we would be spending five times as long managing the cluster…. ICE tools allow us to concentrate on finding new genes that cause disease and not worry about cluster management”

- Gene CutlerTularik, Inc.

Bruce Ling, Ph.DDirector, Bioinformatics

Discovery Platform – working around the clock

Tularik, Inc. Tularik, Inc. in S. San Francisco, CA- headquarter

Tularik Genomics DivisionTularik Genomics Division in Cold Spring Harbor, NY- Cancer gene discovery

Tularik Pharmaceutical CompanyTularik Pharmaceutical Company in S. San Francisco, CA- Metabolic disease drug discovery

Tularik GmbHTularik GmbH in Regensburg, Germany- Screening technology and HTS

Cumbre Inc.Cumbre Inc. in Dallas, TX- Anti-microbial drug discovery

Tularik Ltd.Tularik Ltd. in Macclesfield, United Kingdom- Computer-Aided Molecular Design (CAMD)

• Virtual screening

Tularik’s Drug Discovery Pipeline

Drug Discovery and Development Pipeline

ImmunologyImmunology

Metabolic diseaseMetabolic disease

Various tumor typesVarious tumor types

CancerCancer

INDCandidates

55

Clinical Testing

44

Drug Leads

2626

Drug Targets

8888ObesityObesity

DiabetesDiabetes

CMV CMV -- transplanttransplant

Immune disordersImmune disorders

InflammationInflammation

Lipid disordersLipid disorders

Public Domain Genome Projects

Bioinformatics Data Mining

Pattern Match

IssueComputing power deficit

Data Mining Bottleneck

Linux: THE way to go

• Approach: using batch queuing systems

• Key: effective management and exploitation of all available computing resources

High Throughput Computing

Benefits of Batch Queuing System

• Scalability• Start with a small system, add more later• Revenue growth alignment

• Enable resource sharing across multiple platforms (No dedicated resource for particular project or personnel)

• Improve utilization of overall computing resources (CPU) from less than 20% to over 90%

• Increase visibility and availability of existing computing resources

single node

Linux Cluster Supporting Data Mining

Old vs. New• Time taken to BLAST raw mouse genomic sequence read against

human genome database:

34 days38 yearsAll mouse genomic

sequence reads at NCBI

(22 million reads)

3 minutes15 hours1000 mouse sequences

10 seconds1 minute1 mouse sequence

Linux Cluster

(RedHat 7.1)

Darwin

(SGI Irix 6.5)

Note:Darwin is a heavily used machine, So it is not a machine to machine comparison-

However, it does accurately reflect the environment in which these computers are used.

Linux Cluster Dedicated to J2EE Discovery Architecture

Discovery Platform J2EE Application Cluster

Tularik in the news

Celera HGSC (Ensembl) Celera

06-2000

07-2000

09-2000

10-2000

E-0.8.0

12-2000 E-1.0.0

04-2001 E-1.1.0

08-2001 E-1.2.0

12-2001

04-2002

R18, R19

R20, R21

R22, R23

R24

R25e

R25h

R26b

R26f

E-5.28

R25e

R25h

R26b

R26d

R26e

R26f, R26h

R26j

R26k

E-3.26

Assembly Curated genesRelease date

05-2000

08-2000

11-2000

01-2001

07-2001

01-2002

05-2002

10-2001

11-2001

06-2002

HGSC (UCSC)

06-2000

07-2000

09-2000

10-2000

12-2000

04-2001

08-2001

12-2001

04-2002

R26i

E-4.2803-2002

ENZYME Level 1

HYDROLASESLevel 2

GENE FUNCTIONROOT

TRANSFERASESLevel 2

SWISS-PROTsequences

mouse

TRANSFERASEsignature domains mouse

chicken

human

Human member A

Human member B

STRUCTURALPROTEINS RECEPTORS

ACCEPTED SEQUENCES

REJECTED SEQUENCES

Ontology EngineeringOntology Engineering

D IA N D atabase

Com

pute

r-A

ided

Hum

an C

urat

ion

M ap unto o ntolo gy

R epeat

C o rrect ass ig n m e nt

Inco rrect ass ig nm e nt

E volv ingC o ntro lled vo cab ulary

G B

S P

P DB

G P

U N IG EN E

V ocabular y-based m apping

D om ain-based m apping

K now ledge B ase

C ontro lled V ocabulary

D o m a in s

S W IS S -PR O T

A ss ig n m e nt D B

Ontological concept (Biological Process)

Use Ontology to Annotate MicroArray data

The Drug Discovery Pipeline

Biology Chemistry DevelopmentTarget ID Target Validation Screening Optimization Pre-clinical Clinical

Genomic Location

Expression

SNP

Function

Pathway

Structure

Cross-species homology

knockout

mutation

Compound binding Toxicity

Literature

Disease information

Target Identification

RequestPatent & competitors’ information arebadly needed for Tularik Genes of Interest

Tularik Legal

Introducing Commercial Patent Database In House

•Automatic Pipeline Architecture enables •Regular & automatic data analysis initiation •Proper Update

•Dynamically link to BLAST Application

•Dynamically link to other intellect property sites• Derwent.com• Nerac.com• Dialogweb.com

•Synthesize information from various resources into the genomic relational database and notify relevant decision makers

Tularik Genes of InterestAutomatic Intellectual Properties Retrieval

Micro-Array Bioinformatics

Chip Design: Tularik Genes of Interest

• 60 – 80 mer

• Uniqueness in the genome

• Optimal melting temperature

• No secondary structure

• Allow high throughput design

•Software package: Oligo-Jungle

Oligo Chip Design Algorithm Development

• Minimized distance from 3' end (<350 bases)

• Consideration of splice variants

one probe designed to recognize all splice variants for a gene

• Minimized cross-homology

Algorithm Design Concerns

Oligo Chip Design Algorithm Development

TMAX – Tularik Micro Array Explorer

Tularik Micro-array Data Storageand Analysis Software Suite

TMAX Welcome Page

TMAX Overview

• TMAX is Tularik’s in-house micro-array data storage and analysis software solution.

– Designed for the needs of biologists studying gene expression or genomic amplification/deletion.

– Flexible technology-independent design that handles data in in a variety of formats including Incyte, Affymetrix, Scanalyze, Genepix, Rosetta, Motorola, and simple spreadsheets.

– TMAX is comprised of 12 front-end applications plus a database server and several administrator support tools.

– Currently, TMAX contains about more than 14 million data points.

TMAX Overview

• Data Management -- Administration

• Add Experiment• Delete Experiment• Edit Experiment Annotation• Combine Experiments• Export Experiments

• Data Analysis / Visualization

• Experiment Summarizer• Basic Query• Complex Query• Chip-Viewer• Chip-Plotter• Chip-Cluster• Genome-Scan

TMAX Overview

• Administration

– Add Experiment• Upload data, sequence, and image files into the TMAX

database.

– Delete Experiment• Remove unwanted experiments from the database.

– Edit Experiment Annotation• Modify annotation for loaded experiments..

– Combine Experiments• Generate synthetic data sets by combining replicate

data sets.

– Export Experiments• Export loaded experiments for use with other software.

TMAX Overview

• Analysis– Experiment Summarizer

• Generate a quick summary of an experiment, including research notes; numbers of genes up-regulated, down-regulated and unchanged; top 5 up-regulated genes; top 5 down-regulated genes.

– Basic Query• Retrieve data across multiple experiments based on

signal intensity, fold change, quality score, gene name, and/or gene accession values.

– Complex Query (functional group analysis)• Retrieve data across multiple experiments by specifying

fold change criteria in individual conditions. Can do “fuzzy” matching.

TMAX Overview

• Visualization analysis

– Chip-Viewer• Display fluorescence image of micro-array and pick

spots to display corresponding data. Mainly for quality control.

– Chip-Plotter• Display scatter plots of fold change values for a

selected pair of experiments to quickly identify genes that are similarly or differentially modulated in different conditions.

– Chip-Cluster• Perform clustering of data across multiple experiments

so that patterns of change can be observed. Can choose between a variety of hierarchical and non-hierarchical algorithms.

– Genome-Scan• Plot fold change data from multiple experiments along

chromosomes using radiation-hybrid mapping data. Allows for quick identification of genomic regions that are amplified or deleted.

TMAX Add Experiment

TMAX Summarizer

TMAX Basic Query -- Data Selection

TMAX Basic Query

Basic Query: Query Results• Results can also be downloaded as a spreadsheet for further processing

TMAX Complex Query

Complex Query: Data Selection• Select genes that have specific fold changes with different treatments• Can do “fuzzy matching” (e.g. up-regulated in 3 out of 4 conditions)

TMAX Complex Query

Complex Query: Query Results• Data can also be downloaded as a spreadsheet for further analysis

TMAX ChipViewer

• Viewer Window: Picking a spot gives information for that spot in the table view

• Table View: Experimental data for selected spots can be sorted, columns rearranged.

• Query Window: Find spots based on Gene Name or Accession

• Detail Window: Additional spot details including gene chromosome position; links to Genbank, Unigene, BLAST search

TMAX Chip-Plotter

Plotter: Results Display

TMAX ChipCluster

Cluster: Results Window• Each row represents a single gene across several experimental conditions.• The color of each box represents the fold change of one gene in one condition.

TMAX GenomeScan

• View Window: Each gene from each condition is plotted along the selected chromosome based on mapping data. Fold change is plotted on the Y-axis.

• Control Panel: Selects the chromosome to plot, the mapping panel to display, the quality cutoff score for data display and the scale of the Y-axis

• Gene Table: Displays additional data on the selected genes including experimental measurements, chromosome mapping information, and UniGene cluster information

Tularik’s lead discovery informatics

~80 to 90current targets

50+ different HTS assaysperformed annually

>950,000 compoundscreening library

• Proprietary & comprehensive screening database

• Information intensive lead discovery demands powerful informatics support

MicroArray

GenomeLegal

StructuralBiology

Virtual Screening

Chemistry

SAR

In vivo

HTS

Personnel

Clinical

• T-number database• Chemical characterization• Inventory

• Biological assays • CYP, solubility/permeability, cytotoxicity• PK

• Biological results from HTS• Equipment scheduling

• Pharmacology• Special PK experiments

Types of Data, Links

IT&S for Research

Acquisition

Storage

Reports

DataManagement

HTS

ADMET

SAR

MicroArray

Acquisition InventoryProcesses

Compounds

Assays

Identification Validation

Targets

HTS MicroarraysSAR ADMET

Automated Data Analyses

Assay Registration

On-line Data Management

Reporting (ISIS &Browsers)

Assays---ActivitiesLead Discovery and Follow-up

Experiments

Security Entities

Resource Entities Reports Entities

Operation Entities

Collaboration Entities

Chemistry Entities

Biology Entities

Administration Entities

Data Entities

Business Entities Modeling

Raw Data

Other Data

“Discovery Platform for Drug Discovery Data management”

Data Interpretation

e.g. “Screen Hits”

Data Automatic Upload

Presentation Logic Data Logic

Biology Process Logic

Data Analysis

Configuration

Chemistry Process Logic

Compound Priority

Data QA

Curve Fitting

single node

Dedicated Linux cluster Infrastructure

34 days38 yearsAll mouse genomic

sequence reads at NCBI

(22 million reads)

3 minutes15 hours1000 mouse sequences

10 seconds1 minute1 mouse sequence

Linux Cluster

(Red Hat 7.1)

Darwin

(SGI Irix 6.5)

Corporate Database

http://discovery.tularik.com

Automated data flow-http://discovery.tularik.com

Live

Data Capturing CORPORATE DATABASEData Source

ISISAnalysis

Discovery Platform– http://discovery.tularik.com

Web Server

Client

Reader

OracleServer

Fishnet

Security Server

Application ServerCluster

Data Central

Enterprise JAVA ArchitectureEnterprise J2EE Architecture

Web Server

OracleServer

Security Server

XML

Cluster

Rule Server

LDAP

Data Central

XML

Reader

XML Objects

XML Objects

Central Controller

XSLTTransformer

Client

JMS

Application Controller

Automated Data Flow

Live

Data Capturing CORPORATE DATABASEData Source

Visualization

Registration

Configuration

Data Analysis

ISIS

Discovery J2EE Architecture

J2EE•Extensible•Flexible•Reusable

Discovery Platform– http://discovery.tularik.com

Bruce Ling, Ph.DDirector, Bioinformatics