MIT6_047f08_lec03_slide03

MIT6_047f08_lec03_slide03 - MIT OpenCourseWare...

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Unformatted text preview: MIT OpenCourseWare http://ocw.mit.edu 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms . Rapid sequence alignment and Database search Local alignment, varying gap penalties Karp-Rabin: Semi-numerical methods BLAST: dB search, neighborhood search Statistics of alignment scores (recitation) Lecture 3 Thursday Sept 11, 2008 .047/6.878 - Computational Biology: Genomes, Networks, Evolution DNA Genome Assembly Gene expression analysis Cluster discovery Gibbs sampling Protein network analysis Emerging network properties Regulatory network inference Challenges in Computational Biology 1 Gene Finding 5 Regulatory motif discovery Database search 3 Sequence alignment Evolutionary Theory 7 T C ATG C TAT T CG TGATA A TGA G GATAT T T AT C ATAT T T ATGAT T T Comparative Genomics 6 2 4 8 RNA transcript 9 10 11 13 12 ues: Sequence alignment + dynamic programming A C G T C A T C A A C G T G A T C A mutation A G T G T C A A G T G T C A deletion A G T G T C A T begin end A G T G T C A T insertion • The sequence alignment problem – Genomes change: mutation, insertions, deletions – Alignment: infer evolutionary events – Scoring metric reflects evolutionary properties AGTGCCCTGGAACCCTGACGGTGGGTCACAAAACTTCTGGA A G T G A C C T G G G A A G A C C C T G A C C C T G G G T C A C A A A A C T C • Needleman-Wunsch algorithm – Local update rule: F(i,j) = max{up, left, diagonal} – Save choice pointers for traceback – Bottom-right corner gives optimal alignment score – Trace-back of pointers gives optimal path/alignment A C G T C A T C A T A G T G T C A A G T C/G T C A • Dynamic programming and sequence alignment – Alignment scores are additive: decomposable – Represent sub-problem scores in M(i,j) matrix – Duality between alignment and path through matrix • Dynamic programming – Problems that can be decomposed into subparts – Identical sub-problems: reuse computation – Bottom-up approach: systematically fill table Today’s Goal: Diving deeper into alignments 1. Global alignment vs. Local alignment – Needleman-Wunsch and Smith-Waterman – Varying gap penalties and algorithmic speedups 2. Linear-time exact string matching – Karp-Rabin algorithm and semi-numerical methods – Hash functions and randomized algorithms 3. The BLAST algorithm and inexact matching – Hashing with neighborhood search – Two-hit blast and hashing with combs 4. Probabilistic foundations of sequence alignment – Mismatch penalties, BLOSUM and PAM matrices – Statistical significance of an alignment score Today’s Goal: Diving deeper into alignments 1. Global alignment vs. Local alignment – Needleman-Wunsch and Smith-Waterman – Varying gap penalties and algorithmic speedups 2. Linear-time exact string matching – Karp-Rabin algorithm and semi-numerical methods – Hash functions and randomized algorithms 3. The BLAST algorithm and inexact matching...
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This note was uploaded on 09/24/2010 for the course EECS 6.047 / 6. taught by Professor Manoliskellis during the Fall '08 term at MIT.

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MIT6_047f08_lec03_slide03 - MIT OpenCourseWare...

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