داده های عظیم در دوران پساژنوم

Big Data in Post Genome Era

مهدی صادقیپژوهشگاه ملی مهندسی ژنتیک و زیست فناوری

پژوهشکده علوم زیستی، پژوهشگاه دانش های بنیادی

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The Problem of Big Data

Volume

Velocity of process

Variability

Motivation

• Recent developments in biotechnology have allowed the high-throughput data generation from biological samples

• We have lots and lots of data about all aspects of biology (although still mostly about humans)

• How can we make sense of all this data?– Analyse the data to extract new knowledge about

the biology Data Mining

1973Sharp, Sambrook, Sugden

Gel Electrophoresis Chamber, $250

1958 Matt Meselson &

Ultracentrifuge, $500,000

The Problem of Big Data in Biology hopefully comfortable enough to minimize the technology

and focus on the biology.

Human Genome:$2.7 Billion, 11 Years

Human Genome: $900, 6 Hours

2012:Oxford Nanopore

MiniION

2003: ABI 3730 Sequencer

The Problem of Big Data in Biology A decade’s progress

9

2010: 5K$, a few days

2009: Illumina, Helicos40-50K$

Sequencing the Human Genome

Year

Log

10(p

rice)

201020052000

2012<1000$, <24 hrs

2008: ABI SOLiD60K$, 2 weeks

2007: 4541M$, 3 months

2001: Celera100M$, 3 years

2001: Human Genome Project2.7G$, 11 years

The Problem of Big Data in Biology

A Super-Moore’s Law

So what data can we generate?

• Biological data can be generated at many different levels– Genomics (DNA)– Transcriptomics (RNA)– Proteomics (proteins)–Metabolomics (small compounds)– Lipidomics (lipids)

• Hundreds of –omics have been catalogued

The Problem of Big Data in Biology

High Throughput Phenotyping

The large amount of sequencebased data need balancingwith equally powerful phenotypicdata.

Phytomorph Project (Univ. Wisconsin)

•$70K for 30 cameras•200 movies of root growth•4GB/day of images for processing

Data to Networks to Biology

Protein Interaction Network

Aims

• First Data organization researchers access to existing information submit new entries

• Second develop tools and resources that aid in the analysis of data

• Third interpret the results in a biologically meaningful manner.

Theoretical CS

interdisciplinary

MolecularBiology

Machine LearningData Mining

Information Management

Biophysics

Bioinformatics

Biochemistry

Applied Mathematics & Statistics

Biology Computer Science

General Types of “….Informatics techniques…..”

• Databases– Building, Querying– Object DB

• Text String Comparison– Text Search– 1D Alignment– Significance Statistics

• Finding Patterns– AI / Machine Learning– Clustering– Datamining

• Geometry– Robotics– Graphics (Surfaces, Volumes)– Comparison and 3D Matching

(Vision, recognition)• Physical Simulation

– Newtonian Mechanics– Electrostatics– Numerical Algorithms– Simulation

Algorithmic vs. Statistical Perspectives

Computer Scientists • Data: are a record of everything that happened. • Goal: process the data by positing a model to find interesting patterns and associations.• Methodology: Develop approximation algorithms under different models of data access since the goal is typically computationally hard.

Statisticians (and Natural Scientists)• Data: are a particular random instantiation of an underlying process describing unobserved patterns in the world.• Goal: is to extract information about the world from noisy data.• Methodology: Make inferences (perhaps about unseen events) by positing a model that describes the random variability of the data around the deterministic or stochastic model.

Major Application : Finding Homologs

Major Application :Designing Drugs

• Understanding How Structures Bind Other Molecules (Function)• Designing Inhibitors• Docking, Structure Modeling

(From left to right, figures adapted from Olsen Group Docking Page at Scripps, Dyson NMR Group Web page at Scripps, and from Computational Chemistry Page at Cornell Theory Center).

Pharmacogenomics

Everybody is different

The Right Drug

To The Right Patient

For The Right Disease

At The Right Time

Big changes in the past ... and future

Consider the creation of:

• Modern Physics Management Science

• Computer Science Transistors and Microelectronics

• Molecular Biology Biotechnology

•These were driven by new measurement techniques and technological advances, but they led to:

big new (academic and applied) questions

• new perspectives on the world

• lots of downstream applications

We are in the middle of a similarly big shift!