Introduction to bioinformatics(I617)Haixu Tang
School of InformaticsEmail: [email protected]
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
Top Related