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Introduction to Bioinformatics Burkhard Morgenstern Institute of Microbiology and Genetics Department of Bioinformatics Goldschmidtstr. 1 Göttingen, March 2004

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Introduction to Bioinformatics Bioinformatics in Göttingen: Dep. of Bioinformatics (UKG), Edgar Wingender Dep. of Bioinformatics (IMG), BM Inst. Num. and Applied Mathematics, Stephan Waack Dep. of Genetics (Hans Fritz, IMG), Rainer Merkl

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Introduction to Bioinformatics Definition: Bioinformatics = development and application of software tools for Molecular Biology

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Bioinformatics: Topics: (a) Sequence Analysis (Gene finding …) (b) Structure Analysis (RNA, Protein) (c) Gene Expression Analysis (d) Metabolic Pathways, Virtual Cell

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Bioinformatics: Areas of work: (a) Application of software tools for data analysis in (Molecular) Biology (b) Computing infrastructure, database development, support (c) Development of algorithms and software tools

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Information flow in the cell

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Idea: Sequence -> Structure -> Function

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Information flow in the cell Lots of data available at the sequence level Fewer data at the structure and function level

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Topics of lecture: Data bases SwissProt, GenBank Pair-wise sequence comparison Data base searching Multiple sequence alignment Gene prediction

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Protein data bases Sanger and Tuppy: protein-sequencing methods (1951) Margaret Dayhoff: Atlas of Protein Sequence and Structure (1972); later: Protein Identification Resource (PIR) as international collaboration (a) Organize proteins into families; (b) Amino acid substitution frequencies Amos Bairoch: SwissProt (1986)

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Exponential growth of data bases

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DNA data bases Maxam and Gilbert; Sanger: DNA sequencing methods (1977) GenBank DNA data base (1979), now run by NCBI. Collaboration with EMBL (1982), DDBJ (1984) Translated DNA sequences stored in protein data bases (PIR, trEMBL)

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Most important tool for sequence analysis: Sequence comparison

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The dot plot Y Q E W T Y I V A R E A Q Y E C I V M R E Q Y

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The dot plot Y Q E W T Y I V A R E A Q Y E C I V M R E Q Y

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The dot plot Y Q E W T Y I V A R E A Q Y E C I X V X M R X E X X X Q X X Y X X

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The dot plot Y Q E W T Y I V A R E A Q Y E C I X V X M R X E X X X Q X X Y X X

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The dot plot Y Q E W T Y I V A R E A Q Y E C I X V X M R X E X X X Q X X Y X X

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The dot plot Y Q E W T Y I V A R E A Q Y E C I X V X M R X E X X X Q X X Y X X

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The dot plot Y Q E W T Y Q E V R E Y Q E I C I X V X M R Y X X X Q X X X E X X X X

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The dot plot Y Q E W T Y Q E V R E Y Q E I C I X V X M R Y X X X Q X X X E X X X X

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The dot plot Advantages: 1. Various types of similarity detectable (repeats, inversions) 2. Useful for large-scale analysis

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The dot plot

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Pair-wise sequence alignment Evolutionary or structurally related sequences: alignment possible Sequence homologies represented by inserting gaps

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I X V X M R X E X X Q X Y X X

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I X V X M R X E X X Q X Y X X

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I X V X M R X E X X Q X Y X X

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I X V X M R X E X X Q X Y X X

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I V M R E Q Y

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y –

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Global alignment: sequences aligned over the entire length

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Basic task: Find best alignment of two sequences

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Basic task: Find best alignment of two sequences = alignment that reflects structural and evolutionary relations

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Questions: 1. What is a good alignment? 2. How to find the best alignment?

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Problem: Astronomical number of possible alignments

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I - V M R E - Q Y – Problem: Astronomical number of possible alignments

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Problem: Astronomical number of possible alignments Stupid computer has to find out: which alignment is best ??

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – First (simplified) rules: 1. Minimize number of mismatches 2. Maximize number of matches

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I - V M R E - Q Y – First (simplified) rules: 1. Minimize number of mismatches 2. Maximize number of matches

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – First (simplified) rules: 1. Minimize number of mismatches 2. Maximize number of matches

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – First (simplified) rules: 1. Minimize number of mismatches 2. Maximize number of matches

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I - V M R E - Q Y – Second (simplified) rule: Minimize number of gaps

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Pair-wise sequence alignment T Y I V - A R E A Q Y E C I - V M - R E - Q Y – Second (simplified) rule: Minimize number of gaps

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Pair-wise sequence alignment For protein sequences: Different degrees of similarity among amino acids. Counting matches/mismatches oversimplistic

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Pair-wise sequence alignment T Y I V T L V

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Pair-wise sequence alignment T Y I V T L - V

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Pair-wise sequence alignment T Y I V T - L V

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Pair-wise sequence alignment T Y I V T - L V Use similarity scores for amino acids

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Pair-wise sequence alignment T Y I V T - L V Use similarity scores for amino acids: Define score s(a,b) for amino acids a and b

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Pair-wise sequence alignment T Y I V T - L V Given a similarity score for pairs of amino acids Define score of alignment as sum of similarity values s(a,b) of aligned residues minus gap penalty g for each residue aligned with a gap

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Pair-wise sequence alignment T Y I V T - L V Example: Score = s(T,T) + s(I,L) + s (V,V) - g

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Pair-wise sequence alignment T Y I V T - L V Dynamic-programming algorithm finds alignment with best score. (Needleman and Wunsch, 1970)

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Alignment corresponds to path through comparison matrix

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Pair-wise sequence alignment T Y I V A R E A Q Y E C I X V X M R X E X X Q X Y X X

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Pair-wise sequence alignment T Y I V A R E A Q Y E X X C X I X V X M X R X E X X Q X Y X X

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Pair-wise sequence alignment T Y I V A R E A Q Y E - C I V M R E - Q Y – Alignment corresponds to path through comparison matrix

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Pair-wise sequence alignment T W L V - R E A Q I - C I V M R E - H Y

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Pair-wise sequence alignment Score of alignment: Sum of similarity values of aligned residues minus gap penatly T W L V - R E A Q I - C I V M R E - H Y

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Pair-wise sequence alignment Example: S = - g + s(W,C) + s(L,L) + s(V,V) - g + s(R,R) … T W L V - R E A Q I - C I V M R E - H Y

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Pair-wise sequence alignment T W L V R E A Q Y I X X C X Alignment corresponds I X to path through V X comparison matrix M X R X E X X H X Y X X T W L V - R E A Q I - C I V M R E - H Y

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Pair-wise sequence alignment i T W L V R E A Q Y I X X Dynamic programming: C X Calculate scores S(i,j) I X of optimal alignment of V X prefixes up to positions M X i and j. j R X E H Y T W L V - R - C I V M R

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Pair-wise sequence alignment i T W L V R E A Q Y I X X C X S(i,j) can be calculated from I X possible predecessors V X S(i-1,j-1), S(i,j-1), S(i-1,j). M X j R X E H Y T W L V - R - C I V M R

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Pair-wise sequence alignment i T W L V R E A Q Y I X X C X Score of optimal path that I X comes from top left = V X M X S(i-1,j-1) + s(R,R) j R X E H Y T W L V - R - C I V M R

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Pair-wise sequence alignment i T W L V R E A Q Y I X X C X Score of optimal path that I X comes from above = V X j-1 M X S(i,j-1) – g j R X E H Y T W L V R - - C I V M R

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Pair-wise sequence alignment i-1 i T W L V R E A Q Y I X X C X Score of optimal path that I X comes from left = V X M X S(i-1,j) – g j R X X E H Y T W L - - V R - C I V M R -

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Pair-wise sequence alignment i-1 i T W L V R E A Q Y I X X C X Score of optimal path = I X V X Maximum of these three M X values j R X X E H Y T W L - - V R - C I V M R -

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Pair-wise sequence alignment Recursion formula: S(i,j) = max { S(i-1,j-i)+s(a i,b j ), S(i-1,j) – g, S(i,j-i) – g }

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Pair-wise sequence alignment T W L V R C I V M R E H Y

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Pair-wise sequence alignment T W L V R x x x C x x x I x x V x x M x x R x x E x x H x x Y x x Fill matrix from top left to bottom right:

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Pair-wise sequence alignment T W L V R x x x C x x x I x x x V x x M x x R x x E x x H x x Y x x Fill matrix from top left to bottom right:

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Pair-wise sequence alignment T W L V R x x x x x x C x x x x x x I x x x x x x V x x x x x x M x x x x x x R x x x x x x E x x x x x x H x x x x x x Y x x x x x x Fill matrix from top left to bottom right:

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Pair-wise sequence alignment T W L V R x x x x x x C x x x x x x I x x x x x x V x x x x x x M x x x x x x R x x x x x x E x x x x x x H x x x x x x Y x x x x x x Find optimal alignment by trace-back procedure

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Pair-wise sequence alignment T W L V R x x x x x x C x I x V x M x R x E x H x Y x Initial matrix entries?

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Pair-wise sequence alignment i T W L V R X X C X Entries S(i,j) scores I X of optimal alignment of j V X prefixes up to positions M i and j. R E H Y T W L V - C I V

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Pair-wise sequence alignment i T W L V R j X X X X X C Entries S(i,0) scores I of optimal alignment of V prefix up to positions M i and empty prefix. R E Score = - i* g H Y T W L V - - - -

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Pair-wise sequence alignment T W L V R C I V M R E H Y Initial matrix entries: Example, g = 2

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Pair-wise sequence alignment T W L V R 0 -2 -4 -6 -8 -10 C -2 I -4 V -6 M -8 R -10 E -12 H -14 Y -16 Initial matrix entries: Example, g = 2

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Pair-wise global alignment T W L V R E A Q Y I X X C X I X V X M X R X E X X F X Y X X T W L V - R E A Q I - C I V M R E - F Y

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Pair-wise global alignment Complexity: l 1 and l 2 length of sequences: Computing time and memory proportional to l 1 * l 2 Time and space complexity = O(l 1 * l 2 )

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Pair-wise local alignment Sequences often share only local sequence similarity (conserved genes or domains) Important for database searching

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Pair-wise local alignment T W L V R E A Q Y I X X C X I X V X M X R X E X X H X Y X X T W L V - R E A Q I - C I V M R E - F Y

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Pair-wise local alignment T W L V R E A Q Y I X X C X I X V X M X R X E X X F X Y X X T W L V - R E A Q I - C I V M R E - F Y

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Pair-wise local alignment Problem: Find pair of segments with maximal Alignment score (not necessarily part of optimal global alignment!)

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Pair-wise local alignment T W L V R E A Q Y I X X C X I X V X M X R X E X X F X Y X X T W L V - R E A Q I - C I V M R E - F Y

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Pair-wise sequence alignment Recursion formula for global alignment: S(i,j) = max { S(i-1,j-i)+s(a i,b j ), S(i-1,j) – g, S(i,j-i) – g }

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Pair-wise sequence alignment Recursion formula for local alignment: S(i,j) = max { 0, S(i-1,j-i)+s(a i,b j ), S(i-1,j) – g, S(i,j-i) – g }

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Pair-wise sequence alignment T W L V R 0 0 0 0 0 0 C 0 I 0 V 0 M 0 R 0 E 0 H 0 Y 0 Initial matrix entries = 0

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Pair-wise sequence alignment T W L V R 0 0 0 0 0 0 C 0 0 I 0 V 0 M 0 R 0 E 0 H 0 Y 0 s(C,T) = -2

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Pair-wise sequence alignment Recursion formula for local alignment: S(i,j) = max { 0, S(i-1,j-i)+s(a i,b j ), S(i-1,j) – g, S(i,j-i) – g } Store position with maximal value S(i,j) in matrix

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Pair-wise local alignment T W L V R E A Q Y I X X C X I X V X M X R X E X X F X Y X X T W L V - R E A Q I - C I V M R E - F Y

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Pair-wise local alignment Algorithm by Smith and Waterman (1983) Implementation: e.g. BestFit in GCG package

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