Gap penalty

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a Penalty gap is a scoring method of scoring two or more sequences. When aligning the sequence, introducing loopholes in sequence can allow the alignment algorithm to match more terms than the slit-less juxtaposition. However, minimizing the gap in alignment is important for creating useful alignment. Too many loopholes can cause harmony to be meaningless. The gap fines are used to adjust the alignment scores based on the number and length of the gap. The five main types of gap penalties are constant, linear, affine, convex, and Profile based.

Video Gap penalty

Apps

• Genetic sequence alignment - In bioinformatics, the gap is used to account for genetic mutations occurring from insertions or deletions in sequence, sometimes referred to as indel. Insertion or deletion may occur due to single mutations, unbalanced crossovers in meiosis, missed groove fault, and chromosomal translocation. The notion of disparity in harmony is important in many biological applications, because insertion or deletion consists of all sub-sequences and often occurs from single mutation events. Furthermore, a single mutation event can create gaps of different sizes. Therefore, when scoring, the gap should be assessed as a whole while aligning two DNA sequences. Considering some gaps in the sequence as larger single gaps will reduce high cost assignments to mutations. For example, two protein sequences may be relatively similar but, may vary at certain intervals as one protein may have different subunits than others. Representing this different sub-sequence as a gap will allow us to treat these cases as "good matches" even though there is a long sequential operation with the operation of the indel in sequence. Therefore, using a good gap penalty model will avoid low scores in alignments and increase the chances of finding the correct alignment. In the alignment of the genetic sequence, the gap is represented as a line mark (-) in the alignment of the protein/DNA sequence.

• The function diff unix - calculates the minimum difference between two files with plagiarism detection.
• Spell check - The slit penalty can help find the spelled word correctly with the shortest edited distance to the misspelled word. Gaps can show missing letters in misspelled words.
• Plagiarism detection - The gap fines allow the algorithm to detect the traced part of the document by placing the gap in the original part and matching what is identical. The slit punishment for a particular document quantifies how much of the given document may be original or copied.
• Speech recognition

Maps Gap penalty

Applications Bioinformatics

Global alignment

Global alignment performs end-to-end alignment of query sequences with reference sequences. Ideally, this alignment technique is best suited for closely related sequences of the same length. The Needleman-Wunsch algorithm is a dynamic programming technique used to perform global alignment. Basically, the algorithm divides the problem into a set of sub-problems, then uses sub-problem results to reconstruct the solution to the original request.

Semi-global alignment

The use of semi-global alignment exists to find a particular match in a large order. Examples include finding a promoter in a DNA sequence. Unlike global alignment, it compromises no final gap in one or both sequences. If the final slot is punished in one order of 1 but not in 2nd order, it produces a sequence containing sequence 1 in order 2.

Local alignment

Local order alignment matches the adjacent sub-sections of a sequence with adjacent sub-sections of the other. The Smith-Waterman algorithm is motivated by scoring for matches and incompatibilities. Match increases the overall alignment score while the mismatch decreases the score. Good alignment then has a positive score and poor alignment has a negative value. The local algorithm finds alignment with the highest score taking into consideration only the positive alignments and choosing the best of them. Algorithm is a Dynamic programming algorithm. When comparing proteins, one uses a matrix of similarity that gives a score for every possible residue. Scores should be positive for similar and negative residues for different residual pairs. The gap is usually punishable using a linear gap function that provides initial penalty for opening the gap, and an additional penalty for the gap extension, increasing the length of the gap.

Scoring matrix

Substitution matrices such as BLOSUM are used for the sequence of protein alignment. A Substitution matrix gives a score to align any possible pair of residuals. In general, different substitution matrices are adjusted to detect similarities between sequences that deviate by different degrees. A single matrix may be efficient enough over a relatively broad range of evolutionary changes. The BLOSUM-62 matrix is ​​one of the best substitution matrices to detect weak protein equations. High numerical BLOSUM matrices are designed to compare closely related sequences, while those with low numbers are designed to compare distantly related sequences. For example, BLOSUM-80 is used for more similar alignments in sequence, and BLOSUM-45 is used for alignments that have diverged from each other. For very long and weak alignments, the BLOSUM-45 matrix can provide the best results. Short alignments are more easily detected using matrices with higher "relative entropy" than BLOSUM-62. The BLOSUM series does not include any matrix with relative entropy suitable for the shortest query.

Indels

During DNA Replication, the vulnerable replication machine makes two types of errors when duplicating DNA. These two replication errors are the insertion and removal of a single DNA base from the DNA strand (indels). Indels can have severe biological consequences by causing mutations in DNA strands that may result in inactivation or activation of target proteins. For example, if one or two nucleotide indices occur in sequence the encoding results will be a shift in the reading frame, or a frameshift mutation that can make the protein inactive. The biological consequences of indels are often damaging and are often associated with human pathologies such as cancer. However, not all of the indications are frameshift mutations. If the indel occurs in the trinucleotide, the result is an extension of the protein sequence that may also have implications for protein function.

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Type

Constant

This is the simplest type of punishment gap: negative scores are still given for every slot, regardless of length. This pushes the algorithm to make smaller, larger, gaps leaving a larger adjacent part.

 ATTGACCTGA  || |||||  AT --- CCTGA  

Syncing two short DNA sequences, with '-' which describes the gap of one base pair. If each game is worth 1 point and the whole difference is -1, the total score is: 7Ã,-1Ã, = Ã, 6.

Linear

Compared with the constant gap penalty, the linear gap penalty takes into account the length (L) of each insertion/deletion in the gap. Therefore, if the penalty for each element inserted/deleted is B and the length of slot L; total penalty gap will be the product of two BL. This method supports shorter distances, with the total score decreasing with each additional gap.

 ATTGACCTGA  || |||||  AT --- CCTGA  

Unlike a constant gap penalty, the size of the gap is considered. With a match with a score of 1 and every gap of -1, the score here is (7 Â ± 3 = 4).

Affine

The most widely used gap penalty function is afine gap penalties. The afine gap penalties combine components in both a constant and linear gap penalties, taking the form ${\ displaystyle A B \ cdot L}$ . This introduces a new term, A is known as the opening penalty gap, B penalty loophole extension and length L gap. The opening of the gap refers to the costs required to open the gap of any length, and the extension of the cost extension to extend the length of the existing gap by 1. Often it is unclear as to what values ​​A and B should be because different according to purpose. In general, if interest is to find a closely related match (eg vector sequence removal during genomic sequencing), higher gap penalties should be used to reduce openings. On the other hand, the gap penalties should be lowered when interested in finding more matches. The relationship between A and B also has an effect on the size of the gap. If the size of the slit is important, the small A and B big (more expensive to extend the slit) are used and vice versa.

Convex

Using an afin gap penalty requires fixing a fixed penalty to open and expand the gap. This can be too rigid to use in a biological context.

Celah logaritmik mengambil bentuk ${\ displaystyle G (L) = A C \ ln L}  ÂÂ$ dan diusulkan karena penelitian menunjukkan distribusi ukuran indel mematuhi hukum kekuatan. Masalah lain yang diusulkan dengan penggunaan jurang afinitas adalah favoritisme menyelaraskan urutan dengan celah yang lebih pendek. Kesenjangan celah logaritmik diciptakan untuk memodifikasi celah afin sehingga celah panjang yang diinginkan. Namun, berbeda dengan ini, telah ditemukan bahwa menggunakan model logaritma telah menghasilkan keberpihakan yang buruk bila dibandingkan dengan model affine.

Berbasis profil

Alignment algorithm profiles are powerful tools for detecting the relationship of protein homology with enhanced alignment accuracy. The profiles of alignments are based on the frequency profile of the indel statistics of some sequential alignments generated by PSI-BLAST search. Instead of using a substitution matrix to measure the similarity of amino acid pairs, the profile profile alignment method requires a profile-based rating function to measure the similarity of the profile vector pairs. Participation profiles use penalty function. The gap information is usually used in the form of an indelference frequency profile, which is more specific for the sequence to be aligned. ClustalW and MAFFT adopted this gap penalty for multiple successive alignments. The accuracy of alignment can be improved using this model, especially for proteins with low sequence identities. Some alignment profiles algorithms also execute secondary structure information as a term in their assessment function, which improves alignment accuracy.

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Compare the time complexity

The use of alignment in computational biology often involves varying length sequences. It is important to choose a model that will run efficiently at known input sizes. The time required to run the algorithm is known as the time complexity.

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Challenges

Source of the article : Wikipedia