Introduction to bioinformatics I 617 Haixu Tang School

Introduction to bioinformatics (I 617) Haixu Tang School of Informatics Email: hatang@indiana. edu Office: EIG 1008 Tel: 812 -856 -1859

Textbook • A Primer of Genome Science (2 nd Edition) by Greg Gibson, Spencer V. Muse, Sinauer Associates, 2004 • Suggested reading materials will be posted on the class wiki page: http: //cheminformatics. indiana. edu/djwild/I 61 7_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 Hypothesis • Genome Science Data Hypothesis Knowledge Hypothesis driven approach Data driven approach

Bioinformatics: from data analysis to data mining • Classical Biology • Genome Science Hypothesis Data Hypothesis 1 2 3… Low throughput data High throughput data Hypothesis confirmation / rejection Hypothesis generation

Bioinformatics: in the driver’s seat • Classical Biology Hypothesis • Genome Science Data mining Data analysis Data Hypothesis Knowledge

Key technology advancements • High throughput biotechnologies – Genome sequencing techniques – DNA microarray – Mass spectrometry • Large-scale experiments – HGP, Hap. Map – 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 1960 s

Early Evolutionary Studies • Anatomical features were the dominant criteria used to derive evolutionary relationships between species since Darwin till early 1960 s • 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 j i d 1, 4 = 12 + 13 + 14 + 17 + 12 = 68

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 3 x 3 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 mt. DNA gene sequences • In 1980 s 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