Problem 3 (10 points) Implement the Needleman-Wunsch algorithm with m = 1, s = -

Question

Problem 3 (10 points) Implement the Needleman-Wunsch algorithm with m = 1, s = -1, d = -1. The input and output of your programs should be as follows. Input: two sequence les. Each le contains one sequence, which can be recorded in multiple lines. Discard spaces if there is any. 1 Output: the optimal global alignment between the two sequences and the alignment score. The output alignment should have three lines as shown in the example below, where matching characters are shown by a — character, mismatches by a dot (.), and gaps by a dash (-). For longer sequences, break the alignment into lengths of 50.

ACGTACGTAG--GACGTAAGCAGAGAACGAGAACCCGGGAAC-ACGAGGC

||.||. ||| |||.|||||..||||.||.||||| ||||| |||||||

ACCTAG-TAGCGGACTTAAGCGTAGAAGGACAACCC-GGAACGACGAGGC

TGGTCGGCTT

|.||||.|||

TGGTCGTCTT

First try your algorithm on the sequences used in Problem 2 to make sure it works correctly. Then download hw1prob3.fa (google: fasta format) from the course website and copy the three sequences into separate files; then use your program to align each pair of sequences in the file. FYI, the sequences encode the hemagglutinin (HA) protein for different strains of the inuenza viruses. From the sequences, can you manually identify the start codon and end codon? From the alignment, is there any particular pattern to where or how the gaps occur? If you are allowed to manually adjust the alignment, what might you do to improve the biological relevance of the alignment and why? Alternatively, how can you improve your alignment algorithm to achieve this?

HERE IS THE HW1PROB3.FA FILE

Explanation / Answer

Sequence Alignment or sequence comparison lies in spite of appearance of the bioinformatics, that describes the approach of arrangement of DNA/RNA or macromolecule sequences, so as to spot the regions of similarity among them. it's wont to infer structural, useful and biological process relationship between the sequences. Alignment finds similarity level between question sequence and completely different info sequences. The algorithmic program works by dynamic programming approach that divides the matter into smaller freelance sub issues. It finds the alignment a lot of quantitatively by assignment scores.

When a replacement sequence is found, the structure and performance will be simply expected by doing sequence alignment. Since it's believed that, a sequence sharing common ascendent would exhibit similar structure or perform. bigger the sequence similarity, bigger is that the likelihood that they share similar structure or perform.


Methods of Sequence Alignment:

There square measure primarily 2 strategies of Sequence Alignment:

Global Alignment : Closely connected sequences that square measure of same length square measure much applicable for international alignment. Here, the alignment is dole out from starting until finish of the sequence to search out out the simplest potential alignment.

The Needleman-Wunsch algorithmic program (A formula or set of steps to unravel a problem) was developed by Saul B. Needleman and Christian D. Wunsch in 1970, that could be a dynamic programming algorithmic program for sequence alignment. The dynamic programming solves the initial drawback by dividing the matter into smaller freelance sub issues. These techniques square measure utilized in many various aspects of engineering science. The algorithmic program explains international sequence alignment for positioning ester or macromolecule sequences.

Local Alignment : Sequences that square measure suspected to possess similarity or perhaps dissimilar sequences will be compared with native alignment technique. It finds native regions with high level of similarity.

These 2 strategies of alignments square measure outlined by completely different algorithms, that use grading matrices to align the 2 completely different series of characters or patterns (sequences). {the 2|the 2} {different|totally completely different|completely different} alignment strategies square measure largely outlined by Dynamic programming approach for positioning two different sequences.

Dynamic Programming:

Dynamic programming is employed for optimum alignment of 2 sequences. It finds the alignment in an exceedingly a lot of quantitative approach by giving some scores for matches and mismatches (Scoring matrices), instead of solely applying dots. By looking out the best scores within the matrix, alignment will be accurately obtained. The Dynamic Programming solves the initial drawback by dividing the matter into smaller freelance sub issues. These techniques square measure utilized in many various aspects of engineering science. Needleman-Wunsch and Smith-Waterman algorithms for sequence alignment square measure outlined by dynamic programming approach.

Scoring matrices:

In optimum alignment procedures, largely Needleman-Wunsch and Smith-Waterman algorithms use classification system. For ester sequence alignment, the grading matrices used square measure comparatively less complicated since the frequency of mutation for all the bases square measure equal. Positive or higher price is appointed for a match and a negative or a lower price is appointed for pair. These assumption based mostly scores will be used for grading the matrices. There square measure different grading matrices that square measure predefined largely, utilized in the case of organic compound substitutions.

Mainly used predefined matrices square measure PAM and BLOSUM.

PAM Matrices: Margaret Dayhoff was the primary one to develop the PAM matrix, PAM stands for purpose Accepted Mutations. PAM matrices square measure calculated by perceptive the variations in closely connected proteins. One PAM unit (PAM1) specifies one accepted gene mutation per a hundred organic compound residues, i.e. one hundred and twenty fifth amendment and ninety nine remains in and of itself.

BLOSUM: BLOcks SUbstitution Matrix, developed by Henikoff and Henikoff in 1992, used preserved regions. These matrices square measure actual proportion identity values. merely to mention, they depend upon similarity. Blosum sixty two suggests that there's sixty two Gestalt law of organization.

Gap score or gap penalty: Dynamic programming algorithms use gap penalties to maximise the biological that means. Gap penalty is subtracted for every gap that has been introduced. There square measure completely different gap penalties like gap open and gap extension. The gap score defines a penalty given to alignment after we have insertion or deletion. throughout the evolution, there is also a case wherever we are able to see continuous gaps right along the sequence, that the linear gap penalty wouldn't be applicable for the alignment. therefore gap open and gap extension has been introduced once there square measure continuous gaps (five or more). The open penalty is often applied at the beginning of the gap, so the opposite gaps following it's given with a niche extension penalty which is able to be less compared to the open penalty. Typical values square measure –12 for gap gap, and –4 for gap extension.

Working of Needleman -Wunsch algorithmic program

To study the algorithmic program, take into account the 2 given sequences.

CGTGAATTCAT (sequence #1) , GACTTAC (sequence #2)

The length (count of the nucleotides or amino acids) of the sequence one and sequence a pair of square measure eleven and seven severally. The initial matrix is made with A+1 column’s and B+1 row’s (where A and B corresponds to length of the sequences). additional row and column is given, therefore on align with gap, at the beginning of the matrix as shown in Figure one.

Figure 1: Initial matrix

After making the initial matrix, grading schema should be introduced which might be user outlined with specific scores. the straightforward basic grading schema will be assumed as, if 2 residues (nucleotide or amino acid) at ith and jth position square measure same, matching score is one (S(i,j)= 1) or if the 2 residues at ith and jth position don't seem to be same, pair score is assumed as -1 (S(i,j)= -1 ). The gap score(w) or gap penalty is assumed as -1 .

*Note: The a lot of match, pair and gap will be user outlined, provided the gap penalty ought to be negative or zero.

Gap score is outlined as penalty given to alignment, after we have insertion or deletion.

The dynamic programming matrix is outlined with 3 completely different steps.
1.Initialization of the matrix with the scores potential.
2.Matrix filling with most scores.
3.Trace back the residues for applicable alignment.

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