Introduction to bioinformatics (I617)

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Introduction to bioinformatics(I617)

• Haixu Tang
• School of Informatics
• Email hatang_at_indiana.edu
• Office EIG 1008
• Tel [phone]

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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.india
  na.edu/djwild/I617_2006_wiki/index.php/Main_Page
• Office Hour MW 1100-1200, EIG 1008 or
  appointment

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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

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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.

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The birth of bioinformatics

• A revolution in biology research the emergence
  of Genome Science
• Technology advancement in both biology and
  information science

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Genome science a revolution of biology

• Classical Biology

• Genome Science

Hypothesis
Data
Hypothesis driven approach
Data driven approach
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Bioinformatics from data analysis to data mining

• Classical Biology

• Genome Science

Hypothesis
Data
High throughput data
Low throughput data
Hypothesis generation
Hypothesis confirmation / rejection
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Bioinformatics in the drivers seat

• Classical Biology

• Genome Science

Hypothesis
Data
Data mining
Data analysis
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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

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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.)

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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

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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

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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

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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

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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

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Early Evolutionary Studies

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

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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

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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

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Evolution and DNA Analysis the Giant Panda
Riddle

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

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Evolutionary Tree of Bears and Raccoons
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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.
  

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Evolutionary Trees

• How are these trees built from DNA sequences?

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Evolutionary Trees

• How are these trees built from DNA sequences?
• leaves represent existing species
• internal vertices represent ancestors
• root represents the common evolutionary ancestor

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Rooted and Unrooted Trees
In the unrooted tree the position of the root
(common ancestor) is unknown. Otherwise, they
are like rooted trees
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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

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Distance in Trees an Exampe
d1,4 12 13 14 17 12 68
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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

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

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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
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Turnip vs Cabbage Look and Taste Different

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

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Turnip vs Cabbage Comparing Gene Sequences
Yields No Evolutionary Information
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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

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Turnip vs Cabbage Different mtDNA Gene Order

• Gene order comparison

Before
After
Evolution is manifested as the divergence in gene
order
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Turnip vs Cabbage Different mtDNA Gene Order

• Gene order comparison

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Turnip vs Cabbage Different mtDNA Gene Order

• Gene order comparison

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Turnip vs Cabbage Different mtDNA Gene Order

• Gene order comparison

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Turnip vs Cabbage Different mtDNA Gene Order

• Gene order comparison

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Transforming Cabbage into Turnip
Reversal distance
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History of Chromosome X
Rat Consortium, Nature, 2004