Biocomputation: Comparative Genomics Tanya Talkar Lolly Kruse Colleen O’Rourke.

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Presentation transcript:

Biocomputation: Comparative Genomics Tanya Talkar Lolly Kruse Colleen O’Rourke

DNA

What is Biocomputation? Statistics Computer Science Molecular Biology

Four Main Parts  Biomolecular computation  Biological Computation  Computational Biology  Bioinformatics

Bioinformatics: Biology Computer Science Information Technology

Sequence Analysis  Very Functional!  Compare DNA between Species  Small Fragments  Return full sequence

Computational Genomics  Needleman – Wunsch  Not used much  More Mapped Genomes = Computational Genomics!

Alignment

Global Alignment: Needleman - Wunsch  O(N 3 )  Fewest edit operations  Similar strings

Local Alignment Smith - Waterman  O(N 2 )  Dissimilar strings  Find high similarity regions

Comparison

S1PQRAXABCSTVQ S2XYAXBACSLT

S1AXAB_CS S2AX_BACS Score

Advantages: Global Alignment

Advantages: Local Alignment

BLAST Basic Local Alignment Search Tool FASTA

Improvements  Increased Speed  Locate initial alignment hot spots  Statistical significance

Terminology  Segment Pairs  Locally maximal segment pairs  Maximal segment pairs

How it works  Query sentence, P  Database  Must have score over C!  Multiple segment pairs combined ABCDEFG AGCBFDE BEDGAFB GFBEDCA

How it works  Extends each hit  Done efficiently  Truncates  Doesn’t find all pairs

Proteins  Fixed length, W  Words above threshold  Each hit extended

DNA  Word List  Exact matches  NOT dynamic programming

Scoring  Blosum62 Matrix  Match (+2), Mismatch (-3), Gaps penalized

Substitution Matrix  Represents Scoring Functions

Multiple Sequence Alignment

Methods of MSA  Progressive Alignment Construction  Iterative Methods  Hidden Markov Models  Genetic Algorithms and Simulated Annealing

Comparative Genomics  Compare Species  Find Evolutionary Significances!  Low Level  High Level  Importance of Non Coding DNA