Transcript CS-7010, course introduction

CS 7010: Computational
Methods in Bioinformatics
(course introduction)
Dong Xu
Computer Science Department
109 Engineering Building West
E-mail: [email protected]
573-882-7064
http://digbio.missouri.edu
Challenges of Our Civilization -1

top 125 unsolved problems in science over the next
quarter-century (http://www.sciencemag.org/sciext/125th/)

The Top 25
 What Is the Universe Made Of?
 What is the Biological Basis of Consciousness?
 Why Do Humans Have So Few Genes?
 To What Extent Are Genetic Variation and Personal Health Linked?
 Can the Laws of Physics Be Unified?
 How Much Can Human Life Span Be Extended?
 What Controls Organ Regeneration?
 How Can a Skin Cell Become a Nerve Cell?
Challenges of Our Civilization-2

How Does a Single Somatic Cell Become a Whole Plant?

How Does Earth's Interior Work?

Are We Alone in the Universe?

How and Where Did Life on Earth Arise?

What Determines Species Diversity?

What Genetic Changes Made Us Uniquely Human?

How Are Memories Stored and Retrieved?

How Did Cooperative Behavior Evolve?

How Will Big Pictures Emerge from a Sea of Biological Data?
Challenges of Our Civilization-3

How Far Can We Push Chemical Self-Assembly?

What Are the Limits of Conventional Computing?

Can We Selectively Shut Off Immune Responses?
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Do Deeper Principles Underlie Quantum Uncertainty
and Nonlocality?
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Is an Effective HIV Vaccine Feasible?

How Hot Will the Greenhouse World Be?

What Can Replace Cheap Oil -- and When?
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Will Malthus Continue to Be Wrong?
Lecture Outline
 What
does bioinformatics do?
 Course
topics
 Course
Organization
 Workload/grades
Technical Definitions
NIH (http://www.bisti.nih.gov/)
Bioinformatics: “research, development, or
application of computational tools and
approaches for expanding the use of biological,
medical, behavioral or health data, including
those to acquire, represent, describe, store,
analyze, or visualize such data”.
Computational Biology: “the development and
application of data-analytical and theoretical
methods, mathematical modeling and
computational simulation techniques to the
study of biological, behavioral, and social
systems”.
Scope of Bioinformatics:
Studying Biology on Computer
data management; data mining; modeling; prediction; theory formulation
bioinformatics
genes, proteins, protein complexes, pathways, cells, organisms, ecosystem
an indispensable part of biological science
with its own methodology
engineering
aspect
scientific
aspect
computer science, biology, statistics
physics, mathematics, chemistry, engineering,…
Why Bioinformatics is So Hot? (I)

More than 80 universities offer graduate
degrees in bioinformatics

At cross-section of two most active fields:
computer science and molecular biology

Exponential growths in computer
technologies (hardware, Internet) pave the
way for bioinformatics development
Why Bioinformatics is So Hot? (II)
Analytical technology
High-throughput data
Biological knowledge
Medicine & bioengineering
What Can Computing
Do for Biology?

Data interpretation in analytical technologies

Data management and computational
infrastructure

Discovery from data mining

Modeling, prediction and design

Theoretical / in silico biology
Almost cover every area of computer science
Data Interpretation
in Analytical Technologies (I)

Analytical technologies are the driving force of
new (large-scale) biology:

DNA sequencing (genomics)

X-ray / NMR structure determination (structural
genomics)

Protein identification using mass spectrometry
(proteomics)

Microarray chips (functional genomics)
Data Interpretation
in Analytical Technologies (II)
H
peak assignment
NMR spectra
NMR protein structure determination
C
N C
H H
R
H
O
C N C
H H
H
C
O
C N C
H H
R
structural
restraint
extraction
i+4
i+3
i+2
i+1
structure calculation
i
i-1
protein structure
C
R
O
C N
H
Data Interpretation
in Analytical Technologies (III)
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From image to data (imaging processing)
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Large-scale data cannot be handled without computer
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Noisy data (optimization with under-constraint / overconstraint)
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Computer algorithms/programs can mimic human
interpretation process and do it much faster
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Automation of experimental data interpretation
Data Management and
Computational Infrastructure
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Track instruments, experiment conditions and
results at each step of a complicated biological
experiment (LIMS at modern wet labs)
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Data storage and retrieval (database)

Data visualization
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Data query and analysis pipeline
Discovery from Data Mining (I)
Discovery from Data Mining (II)

Pattern/knowledge discovery from data
 many biological data are generated by biological processes
which are not well understood
 interpretation of such data requires discovery of convoluted
relationships hidden in the data

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which segment of a DNA sequence represents a gene, a regulatory
region
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which genes are possibly responsible for a particular disease
Complicated data
 Large-scale, high-dimension
 Noisy (false positives and false negatives)
Modeling, Prediction
and Design (I)
 Modeling
and prediction of biological
objects/processes
modeling of biochemistry

enzyme reaction rates
modeling of biophysics

dynamics of biomolecules
modeling of evolution

prediction of phylogeny
Modeling, Prediction
and Design (II)

Prediction of outcomes of biological processes
 computing will become an integral part of modern biology through an
iterative process of
model
formulation
computational
prediction

experimental
validation
From prediction to engineering design
 Protein structure prediction to protein engineering
 Design genetically modified species
Theoretical / In Silico Biology

Generate new hypothesis, formulate and
test fundamental theories of biology

new hypothesis about detailed evolutionary
history, through mining genomic sequence data?

new hypothesis about a particular signaling
network, through data mining?

new hypothesis about protein folding pathways,
through simulations?
Bioinformatics Application to
Biological Systems
bacteria
(Synechococcus)
plants
(Arabidopsis)
viruses
(SARS)
yeast
(Saccharomyces cerevisia)
neural systems
(neurons)
Can Biology Help Computing?

Computational techniques inspired by biology:
 Neural network (artificial intelligence)
 Genetic algorithm, automata

A new driver of computer science:
 Better hardware (supercomputers)
 New data representation
 Develop new theoretical framework:

DNA computing

Network communication
(communication between ants, see http://newsservice.stanford.edu/news/2003/may7/antchat-57.html)
Computing versus Biology

what computer science is to molecular biology is
like what mathematics has been to physics ......
-- Larry Hunter, ISMB’94

molecular biology is (becoming) an information
science .......
-- Leroy Hood, RECOMB’00

Bioinformatics is still in its infancy!
Lecture Outline
 What
does bioinformatics do?
 Course
topics
 Course
Organization
 Workload/grades
Course Topics





Data interpretation in analytical technologies
Data management and computational
infrastructure
Discovery from data mining
Modeling, prediction and design
Theoretical / in silico biology
Cover classical/mainstream bioinformatics
problems from computer science prospective
Course Schedule
o See http://digbio.missouri.edu/cs7010/
First take home exam:
--given on 9/29; due on 10/6
Second take home exam:
--given on 11/17; due on 11/29
Three phases of project:
--9/22, 10/20, 11/17, final report due 12/8
What I Will Teach

A general introduction to a few major problems in the
field of bioinformatics
 problems definitions: from biological problem to computable problem
 some key computational techniques

A way of thinking: tackling “biological problem”
computationally


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how to look at a biological problem from a computational point of view
how to formulate a computational problem to address a biological issue
how to collect statistics from biological data
how to build a computational model
how to design algorithms for the model
how to test and evaluate a computational algorithm
how to access confidence of a prediction result
New Ways of Thinking
 Critical
thinking
 Analytical
thinking
 Quantitative
 Algorithmic
thinking
thinking
Lecture Outline
 What
does bioinformatics do?
 Course
topics
 Course
Organization
 Workload/grades
A Brief Survey

Register for the course?

Academic department?

Computer background?

Biology background?
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Statistical background?

Taken another bioinformatics course?
Prerequisites

CS 2050 (Algorithm Design and Programming
II) or equivalent training

Statistics 2500 (Introduction to Probability and
Statistics I) or equivalent training

Programming skills in any programming
language are required

No biology background is necessary
Course Info

Co Instructor: Trupti Joshi
([email protected])

Course Web Site:
http://digbio.missouri.edu/cs7010/
Reference Books - 1
• Neil C.Jones and Pavel A. Pevzner: An
Introduction to Bioinformatics Algorithms
(Computational Molecular Biology). MIT Press,
2004.
• Pavel Pevzner: Computational Molecular
Biology - An Algorithmic Approach. MIT Press,
2000.
• Current Topics in Computational Molecular
Biology, edited by Tao Jiang, Ying Xu, and
Michael Zhang. MIT Press. 2002.
Reference Books - 2
• Pierre Baldi and Soren Brunak: Bioinformatics
– The Machine Learning Approach (second
edition). MIT Press, 2001.
• Dan Gusfield: Algorithms on Strings, Trees,
and Sequences. Cambridge University Press.
1997.
• Warren J. Ewens and Gregory R. Grant:
Statistical Methods in Bioinformatics – An
Introduction. Springer. 2001.
• Terry Speed: Statistical analysis of gene
expression of gene expression microarray data.
Chapman&Hall/CRC. 2003.
Lectures
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3:30pm – 4:45pm, Tuesday and Thursday
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Powerpoint sides for each lecture (posted
before the lecture)
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Questions/answers in the beginning and
end of lecture
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Discussions are encouraged during the
lecture (A topic discussion may be at the
end of a lecture)
Office Hours
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4:45pm-5:35pm, Tuesdays and Thursdays
The instructor who deliver the lecture will give
the office hour
Dong Xu: Room 109, Engineering Building West
(882-7064)
Trupti Joshi: Room 317, Engineering Building
North (884-3528)
Special office hours will be arranged close to the
final
Appointments at other time
Lecture Outline
 What
does bioinformatics do?
 Course
topics
 Course
Organization
 Workload/grades
Minimum Requirement

Attend class regularly

Read suggested class handout after class

Deliver the two take-home exams

Deliver final project (for graduate
students)

Expected workload: 5-6 hours / week in
addition to class attendance
How to Get Maximum
out of the Course

Study suggested reading/slide before class

Study optional reading

Ask questions on class
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Frequent visits at office hours

Perform homework assignments (not graded)

Not required (not counted in the final grade) but
encouraged.
Grading

A final grade of A, B, C, etc. will be assigned,
 2 take-home exams (20% each)
 Project : 3 Phase Reports (5% each), Final Report
(15%), Software Demo (15%), Presentation (15%)

Final project
 A working bioinformatics program that can be used
by biologists or comprehensive computational
analysis on bioinformatics tool outputs
 One student one project (independent development)
with consultation from instructors
 Potential for publication
Three Phases of Project

Phase 1 (due 9/22): Define your project
subject. A brief literature survey and
illustration of its importance.

Phase 2 (due 10/20): Describe key
methods.

Phase 3 (due 11/17): Present key results.
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Final report: due 12/8
Discussion
What do you expect from this course?
- content?
- ways of teaching?
- how the instructors can help?
-…
Assignments
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Suggested reading:
 http://bioinfo.mbb.yale.edu/e-print/whatis-mim/text.pdf
 Bioboxes in “Neil C.Jones and Pavel A. Pevzner: An
Introduction to Bioinformatics Algorithms (Computational
Molecular Biology). MIT Press, 2004.”

Optional reading:
 Chapter 1 in “Current Topics in Computational Molecular
Biology, edited by Tao Jiang, Ying Xu, and Michael
Zhang. MIT Press. 2002.”
 http://www.ncbi.nih.gov/About/primer/bioinformatics.html