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©CMBI 2005 Sequence Alignment In phylogeny one wants to line up residues that came from a common ancestor. For information transfer one wants to line up residues at similar positions in the structure. gap = insertion ór deletion
©CMBI 2005 Global versus Local Alignment Global Local
©CMBI 2005 Global Alignment Align two sequences from “head to toe”, i.e. from 5’ ends to 3’ ends from N-termini to C-termini Algorithm published by: Needleman, S.B. and Wunsch, C.D. (1970) “A general method applicable to the search for similarities in the amino acid sequence of two proteins”. J. Mol. Biol. 48:443-453.
©CMBI 2005 Global Alignment aacttgagc- c-6 t-5 g-4 a-3 g-2 t-1 --9-8-7-6-5-4-3-2-10 We fill-up this matrix backwards, using a very simple scorings scheme. Identity = 1. Other = 0. Gaps cost -1.
©CMBI 2005 Global Alignment aacttgagc- c-6 t-5 g-4 a-3 g-2 t-1 --9-8-7-6-5-4-3-2-10 Score = Where you came from + Gap penalty + Similarity score
©CMBI 2005 Global Alignment aacttgagc- c-6 t-5 g-4 a-3 g-2 t0-1 --9-8-7-6-5-4-3-2-10 0 + 0 = 0 -1 + 0 – 1 = -2
©CMBI 2005 Global Alignment aacttgagc- c-1-2-4-6 t0-1-4-5 g310-3-4 a120-2-3 g001-1-2 t-3-2-10-1 --9-8-7-6-5-4-3-2-10 2 + 1 = 3 1 + 1 – 1 = 1 1 + 1 – 1 = 1
©CMBI 2005 Global Alignment aacttgagc- c345431-1-2-4-6 t1234420-1-4-5 g-2-1012310-3-4 a-2-2-2-10 120-2-3 g-5-4-3-2-1001-1-2 t-6-5-4-3-3-3-2-10-1 --9-8-7-6-5-4-3-2-10
©CMBI 2005 Global Alignment aacttgagc- c345431-1-2-4-6 t1234420-1-4-5 g-2-1012310-3-4 a-2-2-2-10 120-2-3 g-5-4-3-2-1001-1-2 t-6-5-4-3-3-3-2-10-1 --9-8-7-6-5-4-3-2-10 aacttgagc--ct-gagtaacttgagc--ct-gagt
©CMBI 2005 Global Alignment aacttgagc- c345431-1-2-4-6 t1234420-1-4-5 g-2-1012310-3-4 a-2-2-2-10 120-2-3 g-5-4-3-2-1001-1-2 t-6-5-4-3-3-3-2-10-1 --9-8-7-6-5-4-3-2-10 aacttgagc--c-tgagtaacttgagc--c-tgagt
©CMBI 2005 Local Alignment Locate region(s) with high degree of similarity in two sequences Algorithm published by: Smith, T.F. and Waterman, M.S. (1981) “Identification of common molecular subsequences”. J. Mol. Biol. 147:195-197.
©CMBI 2005 Local Alignment aacttgagc-c3454310010t1234421000g2101231100a2210112000g0011010100t0001100000-0000000000aacttgagc-c3454310010t1234421000g2101231100a2210112000g0011010100t0001100000-0000000000 cttgagct-gagcttgagct-gag
©CMBI 2005 Gap Penalty Functions Linear Penalty rises monotonous with length of gap Affine Penalty has a gap-opening and a separate length component Probabilistic Penalties may depend upon the character of the residues involved Other functions Penalty first rises fast, but levels off at greater length values
©CMBI 2005 Significance of Alignment How significant is the alignment that we have found? Or put differently: how much different is the alignment score that we found from scores obtained by aligning random sequences to our sequence?
©CMBI 2005 Calculating Significance Repeat N times (N > 100): Randomise sequence A by shuffling the residues in a random fashion Align randomized sequence A with sequence B, and calculate alignment score S Calculate mean and standard deviation Calculate Z-score: Z = (S genuine – Ŝ random ) / s.d.
©CMBI 2005 Significance of Alignment Random matches Genuine match Alignment score
©CMBI 2005 Significance of Alignment Random matches Random match Alignment score
Sequence Alignments with Indels Evolution produces insertions and deletions (indels) – In addition to substitutions Good example: MHHNALQRRTVWVNAY MHHALQRRTVWVNAY-
Pairwise Sequence Alignment Sushmita Roy BMI/CS 576 Sushmita Roy Sep 10 th, 2013 BMI/CS 576.
Parallel BioInformatics Sathish Vadhiyar. Parallel Bioinformatics Many large scale applications in bioinformatics – sequence search, alignment, construction.
Bioinformatics Tutorial I BLAST and Sequence Alignment.
If Score(i, j) denotes best score to aligning A[1 : i] and B[1 : j] Score(i-1, j) + galign A[i] with GAP Score(i, j-1) + galign B[j] with GAP Score(i,
S. Maarschalkerweerd & A. Tjhang1 Probability Theory and Basic Alignment of String Sequences Chapter
Sequence Similarity Searching Class 4 March 2010.
Heuristic alignment algorithms and cost matrices
1-month Practical Course Genome Analysis (Integrative Bioinformatics & Genomics) Lecture 3: Pair-wise alignment Centre for Integrative Bioinformatics VU.
Developing Pairwise Sequence Alignment Algorithms Dr. Nancy Warter-Perez.
Developing Pairwise Sequence Alignment Algorithms Dr. Nancy Warter-Perez June 23, 2005.
Developing Pairwise Sequence Alignment Algorithms
Developing Pairwise Sequence Alignment Algorithms Dr. Nancy Warter-Perez June 23, 2004.
Sequence Analysis Tools
Sequence Comparison Intragenic - self to self. -find internal repeating units. Intergenic -compare two different sequences. Dotplot - visual alignment.
Alignment methods June 26, 2007 Learning objectives- Understand how Global alignment program works. Understand how Local alignment program works.
Pairwise Alignment Global & local alignment Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis.
Developing Pairwise Sequence Alignment Algorithms Dr. Nancy Warter-Perez May 20, 2003.
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