CSCI 6900/4900 Special Topics in Computer Science

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CSCI 6900/4900 Special Topics in Computer Science

• Automata and Formal Grammars for Bioinformatics

• Bioinformatics problems
• sequence comparison
• pattern/structure search
• pattern/structure recognition
• relationship of sequences

• Algorithm design
• optimal algorithms
• heuristic algorithms
• parallel algorithms

• Probabilistic models
• stochastic finite state automata (HMMs)
• stochastic regular grammars
• stochastic context-free grammars
• more complex grammar models

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Probabilistic modeling and algorithms

• M modeling a family of sequences (e.g. RNA) to
  capture certain properties
• Q1, Q2, .
• Each sequence x possesses a property Qk(x) with
  probability Pk(x)
• (2) A probability distribution for each sequence
  x over the properties,
• i.e., ?k Pk(x) 1 for each given x
• (3) The most likely property Q(x) is one with
  the highest probability,
• i.e., Q(x) arg maxk Pk(x)
• (4) Algorithms are designed to find the most
  likely property for given sequences. But how?

D (sample, training data)
assigning probs
Computational linguistic systems can describe
desired properties of bio sequences
Modeling mechanism
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Outline for the course

• Part 0 molecular biology basics and review of
  probability theory
• Part 1 pairwise alignment, HMMs, profile-HMMs,
  gene finding, and multiple alignment (chapters
  1-6)
• potential research projects efficient HMM
  algorithms, gene finding
• Part 2 RNA stem-loops, SCFG, secondary structure
  prediction, structural homology search (chapters
  9-10)
• potential research projects efficient SCFG
  algorithms, pseudoknot prediction, protein
  secondary structure prediction
• Part 3 phylogeny reconstruction, probabilistic
  approaches (chapters 7-8)
• potential research projects grammar
  modeling of evolution

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The ways this course is to be conducted

• To learn new concepts and techniques
• Lectures (by the instructor and students)
• To apply learned knowledge to research
• Research discussions (lead by students and
  the instructor)
• To demonstrate learning effectiveness
• Presentations of research results (by
  students)

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The central dogma of molecular biology
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Building blocks of DNA

• Nucleotides
• Purines
• Adenine,
• Guanine
• Pyrimidines
• Cytosine,
• Thymine

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Double helix of DNA
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DNA replication
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• Genetic code

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Mutations
(1) synonymous
(2) Missense
(3) nonsense
(4) frame-shift
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RNA synthesis
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RNA synthesis (cont)
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RNA can fold to itself
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Protein synthesis
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Biological information flow
Introns Exons
Gene sequence
Protein sequence
Protein structure
Genome AGACGCTGGTATCGCATTAACTAACGGGTTACTCGGATATTA
CCTTACTATAGGGCGCTATCGCGCGTTAATCTGGTATC
Regulatory DNA sequence
Sequence family
Structure family
Protein-DNA interactions
Protein-protein interactions
Gene regulation
Gene expression
Protein function
Protein abundance
Cellular role
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What bioinformatics is NOT

• Not just using a computer to speed up biology
• Not just applying computer algorithms to biology
• Not just the accountant of genomic data

What bioinformatics is then

• The creative use of computers to define and solve
  central biological puzzles
• The computer becomes an hypothesis machine,
  making predictions to be tested at the bench.