Incorporating Bioinformatics in an Algorithms Course

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Incorporating Bioinformatics in an Algorithms
Course

• Lawrence DAntonio
• Ramapo College of New Jersey

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What is Bioinformatics?

• Algorithms to analyze DNA, RNA, or protein
  sequences
• Database searches to find homologous sequences
• Construction of evolutionary trees
• Structure prediction
• Human Genome Project

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Why use Bioinformatics in an Algorithms Course?

• Real-life applications of algorithms
• Variety of string processing algorithms
• Use of similarity instead of exact matching
• Dynamic programming examples
• Theory vs. Practice Issues

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Models for Incorporating Bioinformatics

• Infusion include material from bioinformatics
  in computer science courses
• Paired Courses have joint lectures and projects
  from, e.g., Algorithms and Genetics courses
• Tracked Courses have a separate Algorithms for
  Bioinformatics course

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Biology Basics

• Primary DNA structure Oriented
  character string
• Double strand constructed through base pairing
• Central Dogma Information passes in one
  direction, from DNA to RNA to protein
• Amino acids formed from triples of bases, called
  codons

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Bonding along a strand
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Bonding between strands
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Complexity of DNA Problems

• 3 billion base pairs in human genome
• Many NP complete problems
• 10600 possible alignments for two 1000 character
  sequences

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Sequence Alignment

• Determine the alignment of two sequences that
  maximizes similarity (global alignment)
• Determine substrings of two sequences with
  maximum similarity (local alignment)
• Determine the alignment for several sequences
  that maximizes the sum of pairs similarity
  (multiple alignment)

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Edit Operations

Substitution
Insertion
Deletion
AATAAGC
AAT-AAGC
AATAAGC
ATTAAGC
AATTAAGC
AA-AAGC
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Dynamic Programming Alignment Algorithm
(Needleman-Wunsch)
If a1,a2,,ai and b1,b2,,bj have been
aligned, there are three possible next moves

• Match ai1 with bj1
• Match ai1 with a space
• Match bj1 with a space

Choose the move that maximizes the similarity of
the two sequences
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Alignment Scoring System

• 1 for a character match
• -1 for a mismatch (substitution)
• -2 for using a space (indel)
• or
• a bk for a gap of k spaces (affine gap penalty)

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Global Alignment Matrix
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Optimal Alignment
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Other Bioinformatics Algorithms

• Palindromes
• Tandem Repeats
• Longest Common Subsequence
• Double Digest (NP complete)
• Shortest Common Superstring (NP complete)

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References

• Clote and Backofen, Computational Molecular
  Biology, Wiley
• Gusfield, Algorithms on Strings, Trees, and
  Sequences, Cambridge University Press
• Mount, Bioinformatics, Cold Spring Harbor Press
• Setubal and Meidanis, Introduction to
  Computational Molecular Biology, PWS
• Waterman, Introduction to Computational Biology,
  CRC Press