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Transcript
Review of Course Topics
(Lecture for CS498-CXZ Algorithms in Bioinformatics)
Dec. 8, 2005
ChengXiang Zhai
Department of Computer Science
University of Illinois, Urbana-Champaign
Key Algorithms
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DNA Sequencing:
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Shortest superstring problem & Eulerian graph approach
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Lander-Waterman model
Overlap-Layout-Consensus
Gene identification
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Exon chaining (similarity)
Likelihood ratio (statistical)
Pairwise alignment
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Dynamic programming
Scoring (scoring matrix, affine gap)
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Variations (Local, Smith-Waterman algorithm)
Multiple sequence alignment
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Exact: Multidimensional dynamic programming
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Inexact: Feng-Doolittle progressive alignment
Hidden Markov models
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Finding most likely path: Viterbi
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Computing sequence probabilities: Forward/Backward
Supervised learning
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ProfileHMM
Microarray data analysis
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Agglomerative Hierarchical Clustering: Single-link, complete-link, avg/group link
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K-means clustering
Phylogenetic tree construction
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Neighbor-joining
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Maximum parsimony
Regulatory motifs
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Deterministic: Consensus
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Sampling: Gibbs Sampler
Genome rearrangements
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Sort by reversal (breakpoint elimination)
Typical Steps to Solve a
Bioinformatics Problem
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Problem formulation
– Understand the original biology problem
– Formalize the problem as a computational problem
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– Must make assumptions (many are unrealistic)
Find algorithms to solve the problem
– Brute force is often too slow or consumes too much memory
– Developing efficient algorithms is the main challenges
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– When it’s impossible to find an extract solution quickly, think about
finding an approximate solution
Evaluate the algorithms and
– Further improve the algorithms
– Further improve the problem formulation
What To Do Next?
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Research Track:
– For undergraduate students: consider graduate schools (many now
have Ph.D./MS in Bioinformatics)
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– For graduate students: find a research advisor in this direction (UIUC is
hiring more faculty in bioinformatics)
Industry Track:
– Pharmaceutical industry is the main job market
Further Training:
– Molecular biology
– Advanced/specialized bioinformatics courses
– Machine learning
– Data mining (relational and textual)
– Statistics
– Databases/Web search
Course Evaluation
Thank You!
Good Luck for the Final!