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Bioinformatics Algorithms Review & Statistical Significance

Review of pairwise sequence alignment, scoring matrices (PAM, BLOSUM), dynamic programming (Needleman-Wunsch, Smith-Waterman), and statistical significance in aligning algorithms using p-value, E-value, z-score, and random samples computing. Also covers suffix trees and arrays for pattern search and applications.

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Bioinformatics Algorithms Review & Statistical Significance

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  1. Midterm Review

  2. Review of previous weeks • Pairwise sequence alignment • Scoring matrices • PAM, BLOSUM, • Dynamic programming • Needleman-Wunsch (Global) • Semi-global (no end-gap penalties) • Smith-Waterman (Local)

  3. Review of previous weeks • Statistical significance of alignments • p-value, E-value, z-score? • Computing significance using random samples. • Example: • Suppose we have a face matching algorithm and we assign scores based on match of gender, complexion, eye-color, hair-color, use of glasses, use of face jewellery, existence of mustache, beard.

  4. Statistical significance • We match 100K pairs of faces randomly and here’s the (hypothetical) score distribution:

  5. Statistical significance • What is the p-value of a match with score 7? score 1?

  6. Statistical significance • p-value(x>=7) = 0.125

  7. Statistical significance • p-value(x>=1) = 0.875

  8. Statistical significance • p-value(x>=4) = 0.5

  9. Suffix Trees and Suffix Arrays • Construction of suffix trees and suffix arrays • Pattern search in suffix trees/arrays • Other applications • E.g., • Finding the most occurring pattern of length 2 in a string (Solution: count the # of leaves below the nodes that have string depth>=2)

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