CS5263 Bioinformatics

Lecture 1: Introduction

Outline

• Administravia

• What is bioinformatics

• Why bioinformatics

• Topics in bioinformatics

• What you will & will not learn

• Introduction to molecular biology

Student info

• Your name

• Email

• Enrollment status

• Academic background

• Interests

Course Info

• Instructor: Jianhua Ruan

Office: S.B. 4.01.48

Phone: 458-6819

Email: jruan@cs.utsa.edu

Office hours: Tues 6:30-7:30, Wed 3-4pm

• Web: http://www.cs.utsa.edu/~jruan/teaching/cs5263_fall_2007/

Course description

• A survey of algorithms and methods in bioinformatics, approached from a computational viewpoint.

• Discussions balanced between algorithmic analyses and biological applications

• Prerequisite:– Knowledge in algorithms and data structure – Programming experience– Basic understanding of statistics and probability– Appetite to learn some biology

Textbooks

• Required:– An Introduction to Bioinformatics Algorithms

by Jones and Pevzner

• Recommended:– Biological Sequence Analysis: Probabilistic

Models of Proteins and Nucleic Acidsby Durbin, Eddy, Krogh and Mitchison

• Additional resources – See course website

Grading

• Attendance: 10%– At most 2 classes missed without affecting

grade

• Homeworks: 50%– No late submission accepted– Read the collaboration policy!

• Final project and presentation: 40%

What is bioinformatics

• National Institutes of Health (NIH):– Research, development, or application of

computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.

What is bioinformatics

• National Center for Biotechnology Information (NCBI):– the field of science in which biology, computer

science, and information technology merge to form a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned.

What is bioinformatics

• Wikipedia – Bioinformatics refers to the creation and

advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems posed by or inspired from the management and analysis of biological data.

Why bioinformatics

• Modern biology generates huge amount of data– Human genome sequence has 3 billion bases

• Complex relationships among different types of data– Challenges to integrate and analyze data

• Algorithmic challenges– Biologists trained to programming are probably not sufficient

• Tremendous needs in both academic and industry– Job opportunities

• You get the chance to learn something different

Some examples of central role of CS in bioinformatics

1. Genome sequencing

AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT

3x109 nucleotides

~500 nucleotides

AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT

3x109 nucleotides

Computational Fragment AssemblyIntroduced ~19801995: assemble up to 1,000,000 long DNA pieces2000: assemble whole human genome

A big puzzle~60 million pieces

1. Genome sequencing

Where are the genes?Where are the genes?

2. Gene Finding

In humans:

~22,000 genes~1.5% of human DNA

Start codonATG

5’ 3’Exon 1 Exon 2 Exon 3Intron 1 Intron 2

Stop codonTAG/TGA/TAA

Splice sites

2. Gene Finding

Hidden Markov Models

(Well studied for many years in speech recognition)

3. Protein Folding• The amino-acid sequence of a protein determines the 3D fold• The 3D fold of a protein determines its function• Can we predict 3D fold of a protein given its amino-acid

sequence?– Holy grail of compbio—40 years old problem– Molecular dynamics, computational geometry, machine learning, robotics

4. Sequence Comparison—Alignment

AGGCTATCACCTGACCTCCAGGCCGATGCCC

TAGCTATCACGACCGCGGTCGATTTGCCCGAC

-AGGCTATCACCTGACCTCCAGGCCGA--TGCCC--- | | | | | | | | | | | | | x | | | | | | | | | | |

TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC

Sequence AlignmentIntroduced ~1970BLAST: 1990, most cited paper in historyStill very active area of research

query

DB

BLAST

Efficient string matching algorithms

Fast database index techniques

Sequence comparison is key to• Finding genes• Determining function• Uncovering the evolutionary processes

Sequence conservation implies function

5. Evolution

More than 200 complete genomes have been

sequenced

5. Evolution

6. Microarray analysisClinical prediction of Leukemia type

• 2 types– Acute lymphoid (ALL)

– Acute myeloid (AML)

• Different treatment & outcomes• Predict type before treatment?

Bone marrow samples: ALL vs AML

Measure amount of each gene

Some goals of biology for the next 50 years

• List all molecular parts that build an organism– Genes, proteins, other functional parts

• Understand the function of each part• Understand how parts interact• Study how function has evolved across all species• Find genetic defects that cause diseases• Design drugs rationally• Sequence the genome of every human, use it for

personalized medicine

• Bioinformatics is an essential component for all the goals above

Major conferences

• ISMB (Summer every year)• RECOMB (and its satellites) (Spring every year)• PSB (Jan every year, Hawaii)• ECCB (Europe)• CSB (July every year, Stanford)• Conferences in computer science

– ICDM (conference on data mining)– ICML (conference on machine learning)– AAAI (conference on AI)

Major journals

• Bioinformatics• Journal of Computational Biology• PLoS Computational Biology• BMC Bioinformatics• Genome Biology• Genome Research• Nucleic Acids Research• IEEE Trans on Computational Biology• Science, Nature, PNAS, Cell, Nature Genetics,

Nature Biotech, …

Major Bioinfo research topics

Covered topics

• Sequence analysis– Alignment– Motif finding– Pattern matching– Phylogenetic tree

• Sequence-based predictions– Gene components– RNA structure

• Functional Genomics– Microarray analysis– Biological networks

What you will learn?

• Basic concepts in molecular biology and genetics

• Selected topics in bioinformatics and challenges

• Algorithms:– DP, graph, string algorithms– Statistical learning algorithms: HMM, EM,

Gibbs sampling– Data mining: clustering / classification

What you will not learn?

• Existing tools / databases

• Design / perform biological experiments

• Protein structure prediction (commonly avoided by most bioinfo researchers…)

• Building bioinformatics software tools (GUI, database, Perl / Python, …)

Goals

• Basis of sequence analysis and other computational biology algorithms

• Overall picture about the field

• Read / criticize research articles

• Think about the sub-field that best suits your background to explore

• Communicate and exchange ideas with (computational) biologists

Computer Scientists vs Biologists

(courtesy Serafim Batzoglou, Stanford)

Biologists vs computer scientists

• (almost) Everything is true or false in computer science

• (almost) Nothing is ever true or false in Biology

Biologists vs computer scientists

• Biologists seek to understand the complicated, messy natural world

• Computer scientists strive to build their own clean and organized virtual world

Biologists vs computer scientists

• Computer scientists are obsessed with being the first to invent or prove something

• Biologists are obsessed with being the first to discover something

Biologists vs computer scientists

• Biologists are comfortable with the idea that all data have errors, and every rule has exceptions

• Computer scientists are not

Biologists vs computer scientists

• Computer scientists get high-paid jobs after graduation

• Biologists typically have to complete one or more 5-year post-docs...

Molecular biology 101

• Cell

• DNA, RNA, Protein

• Genome, chromosome, gene

• Central dogma

Life

• Categories– Prokaryotes (e.g. bacteria)

• Unicellular• No nucleus

– Eukaryotes (e.g. fungi, plant, animal)• Unicellular or multicellular• Has nucleus

• The most important distinction among groups of organism

Prokaryote vs Eukaryote

• Eukaryote has many membrane-bounded compartment inside the cell– Different biological processes occur at different

cellular location

Chemical contents of cell

• Small molecules–Sugar–Ions (Na+, Ka+, Ca2+, Cl- ,…)–…

• Macromolecules (polymers): –DNA–RNA–Protein–…

• Polymers: “strings” made by linking monomers from a specified set (alphabet)

Polymer Monomer

DNA Deoxyribonucleotides

RNA Ribonucleotides

Protein Amino Acid

DNA

• DNA: forms the genetic material of all living organisms– Can be replicated and passed to descendents– Contains information to produce proteins

• To computer scientists, DNA is a string made from alphabet {A, C, G, T}– e.g. ACAGAACGTAGTGCCGTGAGCG

• Each letter is called a base– A deoxyribonucleotides

• Length varies. From hundreds to billions

RNA

• Historically thought to be information carrier only– DNA => RNA => Protein– New roles have been found for them

• To computer scientists, RNA is a string made from alphabet {A, C, G, U}– e.g. ACAGAACGUAGUGCCGUGAGCG

• Each letter is called a base– A ribonucleotides

• Length varies. From tens to thousands

Protein

• Protein: the actual “worker” for almost all processes in the cell– Enzymes: speed up reactions– Signaling: information transduction– Structural support– Production of other macromolecules– Transport

• To computer scientists, protein is a string built from 20 letters– E.g. MGDVEKGKKIFIMKCSQCHTVEKGGKHKTGP

• Each letter is called an amino acid• Lengths: from tens to thousands

Central dogma of molecular biology

DNA/RNA zoom-in

• Commonly referred to as Nucleic Acid

• DNA: Deoxyribonucleic acid

• RNA: Ribonucleic acid

• Found mainly in the nucleus of a cell (hence “nucleic”)

• Contain phosphoric acid as a component (hence “acid”)

• They are made up of nucleotides

Nucleotides• A nucleotide has 3 components

– Sugar (ribose in RNA, deoxyribose in DNA)– Phosphoric acid– Nitrogen base

• Adenine (A)• Guanine (G)• Cytosine (C)• Thymine (T) or Uracil (U)

Monomers of RNA• A ribonucleotide has 3 components

– Sugar - Ribose– Phosphate group– Nitrogen base

• Adenine (A)• Guanine (G)• Cytosine (C)• Uracil (U)

Monomers of DNA• A deoxyribonucleotide has 3 components

– Sugar - Deoxyribose– Phosphoric acid– Nitrogen base

• Adenine (A)• Guanine (G)• Cytosine (C)• Thymine (T)

Polymerization: Nucleotides => nucleic acids

Phosphate

Sugar

Nitrogen Base

Phosphate

Sugar

Nitrogen Base

Phosphate

Sugar

Nitrogen Base

G

A

G

T

C

A

G

C

5’-AGCGACTG-3’

AGCGACTG

Phosphate

Sugar

Base

1

23

4

5

Many biological processes go from 5’ to 3’e.g. DNA replication, transcription, etc.

5’

3’

DNA

G

A

G

U

C

A

G

U

5’-AGUGACUG-3’

AGUGACUG

Phosphate

Sugar

Base

1

23

4

5

Many biological processes go from 5’ to 3’e.g. transcription.

5’

3’

RNA

T

C

A

C

T

G

G

C

G

A

G

T

C

A

G

C

Base-pair:

A = T

G = C

5’

5’3’

3’

5’-AGCGACTG-3’3’-TCGCTGAC-5’

AGCGACTGTCGCTGAC

AGCGACTG

Forward (+) strand

Backward (-) strand

One strand is said to be reverse- complementary to the other

Reverse-complementary sequences

• 5’-ACGTTACAGTA-3’

• The reverse complement is:

3’-TGCAATGTCAT-5’

=>

5’-TACTGTAACGT-3’

• Or simply written as

TACTGTAACGT

DNA double helix

Orientation of the double helix

• Double helix is anti-parallel–5’ end of each strand at 3’ end of the other–5’ to 3’ motion in one strand is 3’ to 5’ in the other

• Double helix has no orientation–Biology has no “forward” and “reverse” strand–Relative to any single strand, there is a “reverse complement” or “reverse strand”–Information can be encoded by either strand or both strands

5’TTTTACAGGACCATG 3’3’AAAATGTCCTGGTAC 5’

RNA Secondary structures

• RNAs are normally single-stranded

• Can form complex structure by self-base-pairing

• A=U, C=G