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![Brute Force Finding Motif Solution
Compute the scores for each possible combination of
starting positions s
The best score will determine the best profile and the
consensus pattern in DNA
The goal is to maximize Score(s,DNA) by varying the
starting positions si, where:
si = [1, …, n-l+1]
i = [1, …, t]
25](https://image.slidesharecdn.com/findingmotif-171225104847/85/Finding-motif-25-320.jpg)
![Brute Force Finding Motif Solution
Compute the scores for each possible combination of
starting positions s
The best score will determine the best profile and the
consensus pattern in DNA
The goal is to maximize Score(s,DNA) by varying the
starting positions si, where:
si = [1, …, n-l+1]
i = [1, …, t]
25](https://image.slidesharecdn.com/findingmotif-171225104847/75/Finding-motif-25-2048.jpg)
More Related Content
Finding motif
- 1. Finding motif GENE RELATEDMOTIFS ALIREZA ALIKHANI 1
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- 5. What is gene? Classically, a unit of inheritance. In practice, a gene is a segment of DNA on a chromosome that encodes a protein and all the regulatory sequences (promoter) required to control expression of that protein. 5
- 6. What is motif? A conserved element of a protein sequence alignment that usually correlates with a particular function. Motifs are generated from a local multiple protein sequence alignment corresponding to a region whose function or structure is known. It is sufficient that it is conserved, and is hence likely to be predictive of any subsequent occurrence of such a structural/functional region in any other novel protein sequence. It is said to be patterns that can be used to identify the gene 6
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- 9. Defining Motifs Todefine a motif, lets say we know where the motif starts in the sequence The motif start positions in their sequences can be represented as s = (s1,s2,s3,…,st) 9
- 10. Defining Motifs Lineup the patterns by their start indexes s = (s1, s2, …, st) 10
- 11. How to finda motif? Fddfdd gggatactcttgtgaatggatttttaactga cacattagata fddfdd gggatactgataccgtatttggcctaggc cacattagata Fddfdd gggatactgataccgtatttggcctaggc cacattagata fddfdd gactgataccgtaccgtatttggcctaggc cacattagata ………………………………………………………………………………..... ………………………………………………………………………………….. fddfdd gggatactgataccgtatttggcctaggc cacattagata Fddfdd gggatactgataccgtatttggcctaggc cacattagata fddfdd gggatactgatacccttgtgaatggattgc cacattagata fddfdd gggatactgataccgtgatacccctaggc cacattagata fddfdd gggatactgataccgatactgcctaggc cacattagata fddfdd gggatactgataccgtatttggcctaggc cacattagata 11
- 12. Finding Motif Algorithms ExactMatch Non Exact Match 12
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- 17. Non Exact Matchmotif:acgtacgt 17
- 18. Distance with realmotifs The distance between a real motif and the consensus sequence is generally less than the distance between two real motifs 18
- 19. Type of Algorithms EPatternBranching PatternBranching PMS: Exhaustive Motif Search Pms1,pms2,… Pmsp Search Trees Projection The Gold Bug Problem EM Brute Force Motif Finding The Median String Problem Branch-and-Bound Motif Search Branch-and-Bound Median String Search Consensus and Pattern Branching: Greedy Motif Search Boyer-Moore Knuth-Morris-Pratt Suffix Array Construction 19
- 20. Defining Some Terms T : Number of sample DNA sequences N : length of each DNA sequence DNA : sample of DNA sequences (T*N array) L : length of the motif (l-mer) 𝑆𝑖 : starting position of an l-mer in sequence i S =(𝑆1, 𝑆2,… 𝑆𝑡) : array of motif’s starting positions 20
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- 23. The Motif FindingProblem the starting positions S are usually not given. How can we find the “best” profile matrix? 23
- 24. The Motif FindingProblem: Formulation Goal: Given a set of DNA sequences, find a set of l-mers, one from each sequence, that maximizes the consensus score Input: A t x n matrix of DNA, and l the length of the pattern to find Output: An array of t starting positions s = (s1, s2, … st) maximizing Score(s,DNA) 24
- 25. Brute Force FindingMotif Solution Compute the scores for each possible combination of starting positions s The best score will determine the best profile and the consensus pattern in DNA The goal is to maximize Score(s,DNA) by varying the starting positions si, where: si = [1, …, n-l+1] i = [1, …, t] 25
- 26. Brute Force AlgorithmSearch 1. BruteForceMotifSearch(DNA, t, n, l) 2. bestScore 0 3. for each s=(s1,s2 , . . ., st) from (1,1 . . . 1) to (n-l+1, . . ., n-l+1) 4. if (Score(s,DNA) > bestScore) 5. bestScore score(s, DNA) 6. bestMotif (s1,s2 , . . . , st) 7. return bestMotif 26
- 27. Brute Force features no preprocessing phase. constant extra space needed. always shifts the window by exactly 1 position to the right. comparisons can be done in any order. For each set of starting positions, the scoring function makes l operations, so complexity is l (n – l + 1) 𝒕 = O(l𝒏 𝒕 ). That means that for t = 8; n = 1000; l = 10 we must perform approximately 𝟏𝟎 𝟐𝟎 computations it will take billions years. 27
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- 40. PMS (Planted MotifSearch) Generate all possible l-mers from out of the input sequence Si. Let Ci be the collection of these l-mers. Example: AAGTCAGGAGT Ci = 3-mers: AAG-AGT-GTC-TCA-CAG-AGG-GGA-GAG-AGT 40
- 41. All patterns atHamming distance d = 1 41
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- 44. Find motif commonto all lists Follow this procedure for all sequences Find the motif common all Li (once duplicates have been eliminated) This is the planted motif 44
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- 46. PMS Running Time It takes time to Generate variants Sort lists Find and eliminate duplicates Running time of this algorithm: w is the word length of the computer M is Total number of patterns, In the previous example m=90 46
- 47. References AN INTRODUCTIONTO BIOINFORMATICS ALGORITHMS(NEIL C. JONES AND PAVEL A. PEVZNER) essential bioinformatics by jin xiong http://www-igm.univ-mlv.fr/~lecroq/string/node3.html http://docs.seqan.de/seqan/1.3/index.html https://www.youtube.com/ https://www.wikipedia.org/ https://genome.ucsc.edu/ 47