Introduction to bioinformatics(I617)Haixu Tang

School of InformaticsEmail: hatang@indiana.edu

Office: EIG 1008Tel: 812-856-1859

Textbook

• A Primer of Genome Science (2nd Edition) by Greg Gibson, Spencer V. Muse, Sinauer Associates, 2004

• Suggested reading materials will be posted on the class wiki page: http://cheminfo.informatics.indiana.edu/djwild/I617_2006_wiki/index.php/Main_Page

• Office Hour: MW 11:00-12:00, EIG 1008 or appointment

Grading

• Class project: selected from one of four covered areas (bioinformatics, Chemical informatics, Laboratory informatics and Health informatics) 25%– Suggested Bioinformatics topics will be

posted on the class wiki page

• Homework: 25% in Bioinformatics– 4, each 6.25%

Bioinformatics = BIOlogy + informatics?

• Not really: it is a term (somehow arbitrarily chosen) to define a multi-disciplinary area that combines life sciences, physical sciences and computer science / informatics;

• It addresses biological problems using theoretical informatics approaches, not vice versa;

• It is transforming classical Biology into a Information Science.

The birth of bioinformatics

• A revolution in biology research: the emergence of Genome Science

• Technology advancement in both biology and information science

Genome science: a revolution of biology

• Classical Biology • Genome Science

Hypothesis

Data

Knowledge

Hypothesis driven approach

Hypothesis

Knowledge

Data

Data driven approach

Bioinformatics: from data analysis to data mining

Hypothesis

Data

• Classical Biology

Low throughput data

• Genome Science

Hypothesis

Data

High throughput data

Hypothesis confirmation / rejection

Hypothesis generation

1 2 3 …

Bioinformatics: in the driver’s seat

• Classical Biology

Hypothesis

Data

Knowledge

• Genome Science

Hypothesis

Knowledge

Data

Data analysis

Data mining

Key technology advancements• High throughput biotechnologies

– Genome sequencing techniques– DNA microarray– Mass spectrometry

• Large-scale experiments– HGP, HapMap– Omics / Systems Biology

• Massive data generation, storage, exchange and analysis– CPU, storage, etc.– High speed network (Internet)– Bioinformatics

Bioinformatics: mutually beneficial

• For biologists– Fragment assembly in

genome sequencing– Genome comparison– Gene clustering in

DNA microarray analysis

– Protein identification in proteomics

• For computer scientists– String algorithms / Tree

algorithms– Alternative Eulerian path

(BEST theorem)– Reversal distances– Probabilistic graphic

models (HMMs, BNs, etc.)

Two origins of bioinformatics

• Combinatorial pattern matching in theoretical computer science– DNA and protein sequence analysis

• Physical and analytical chemistry of Biomolecules– Protein structure analysis Structural

bioinformatics– Bio-analytical chemistry Proteomics

Bioinformatics addresses computational challenges in life and medical sciences

• New computational problems for automatic data analysis

• Reformulation of old problems using new high throughput data

• Formulating new problems using high throughput data

Bioinformatics addresses computational challenges in life and medical sciences

• New computational problems for automatic data analysis• Genome sequencing• Proteomics• Transcriptomics

• Data representation and visualization• Genome Browser

• Solving biological problems by in silico approaches– Reformulation of old problems using new high throughput data

• Gene finding• Protein structure and function

– Formulating new problems using high throughput data• Comparative genomics• Polymorphisms / Population genetics• Systems Biology

Bioinformatics resources

• Databases– Nucleic Acid Research (NAR) annual database issue

• Organization– ISCB (International Society in Computational Biology)

• Conferences– ISMB– RECOMB– Many other smaller or regional conferences, e.g.

ECCB, CSB, PSB, etc, including local Indiana Bioinformatics conference

A case study

• How bioinformatics help and transform classical biological topics?

• Molecular evolutionary studies: from anatomical features to molecular evidences

• Genome evolution: comparison of gene orders

Early Evolutionary Studies

• Anatomical features were the dominant criteria used to derive evolutionary relationships between species since Darwin till early 1960s

Early Evolutionary Studies

• Anatomical features were the dominant criteria used to derive evolutionary relationships between species since Darwin till early 1960s

• The evolutionary relationships derived from these relatively subjective observations were often inconclusive. Some of them were later proved incorrect

Evolution and DNA Analysis: the Giant Panda Riddle

• For roughly 100 years scientists were unable to figure out which family the giant panda belongs to

• Giant pandas look like bears but have features that are unusual for bears and typical for raccoons, e.g., they do not hibernate

Evolution and DNA Analysis: the Giant Panda Riddle

• In 1985, Steven O’Brien and colleagues solved the giant panda classification problem using DNA sequences and bioinformatics algorithms

Evolutionary Tree of Bears and Raccoons

Evolutionary Trees: DNA-based Approach

• 40 years ago: Emile Zuckerkandl and Linus Pauling brought reconstructing evolutionary relationships with DNA into the spotlight

• In the first few years after Zuckerkandl and Pauling proposed using DNA for evolutionary studies, the possibility of reconstructing evolutionary trees by DNA analysis was hotly debated

• Now it is a dominant approach to study evolution.

Evolutionary Trees

How are these trees built from DNA sequences?

Evolutionary Trees

How are these trees built from DNA sequences?

– leaves represent existing species

– internal vertices represent ancestors

– root represents the common evolutionary ancestor

Rooted and Unrooted Trees

In the unrooted tree the position of the root (“common ancestor”) is unknown. Otherwise, they are like rooted trees

Distances in Trees

• Edges may have weights reflecting:– Number of mutations on evolutionary path from

one species to another– Time estimate for evolution of one species into

another• In a tree T, we often compute

dij(T) - the length of a path between leaves i and j

dij(T) – tree distance between i and j

Distance in Trees: an Exampe

d1,4 = 12 + 13 + 14 + 17 + 12 = 68

i

j

Distance Matrix

• Given n species, we can compute the n x n distance matrix Dij

• Dij may be defined as the edit distance between a gene in species i and species j, where the gene of interest is sequenced for all n species.

Dij – edit distance between i and j

Fitting Distance Matrix

• Given n species, we can compute the n x n distance matrix Dij

• Evolution of these genes is described by a tree that we don’t know.

• We need an algorithm to construct a tree that best fits the distance matrix Dij

Reconstructing a 3 Leaved Tree

• Tree reconstruction for any 3x3 matrix is straightforward

• We have 3 leaves i, j, k and a center vertex c

Observe:

dic + djc = Dij

dic + dkc = Dik

djc + dkc = Djk

Turnip vs Cabbage: Look and Taste Different

• Although cabbages and turnips share a recent common ancestor, they look and taste different

Turnip vs Cabbage: Comparing Gene Sequences Yields No Evolutionary Information

Turnip vs Cabbage: Almost Identical mtDNA gene sequences

• In 1980s Jeffrey Palmer studied evolution of plant organelles by comparing mitochondrial genomes of the cabbage and turnip

• 99% similarity between genes• These surprisingly identical gene

sequences differed in gene order• This study helped pave the way to

analyzing genome rearrangements in molecular evolution

Turnip vs Cabbage: Different mtDNA Gene Order

• Gene order comparison:

Before

After

Evolution is manifested as the divergence in gene order

Turnip vs Cabbage: Different mtDNA Gene Order

• Gene order comparison:

Turnip vs Cabbage: Different mtDNA Gene Order

• Gene order comparison:

Turnip vs Cabbage: Different mtDNA Gene Order

• Gene order comparison:

Turnip vs Cabbage: Different mtDNA Gene Order

• Gene order comparison:

Transforming Cabbage into Turnip

Reversal distance

History of Chromosome X

Rat Consortium, Nature, 2004