Affine gap penalties, which impose an 'opening' penalty for a gap and an 'extension' penalty that decreases the relative penalty for each additional position in an already opened gap, address both of these issues. NCBI's BLAST page [ 2 ] allows one to choose from several different sets of parameters for scoring gaps existence penalties of 7, 8, and 9 with an extension penalty of 2, and existence penalties of 10,11 and 12 with an extension penalty of 1.
The need for an automated way of finding the optimal alignment out of the numerous alternatives is clear, but the method must be consistent and biologically meaningful. Choosing a good alignment by eye is possible, but life is too short to do it more than once or twice. For two long sequences, doing this directly would take a considerable amount of time, even on the fastest computers.
Examining the calculations in detail, however, one might notice that the vast majority of the time would be spent evaluating the same portions of the candidate alignments many times over. This redundant aspect of sequence comparison makes it amenable to a time-saving shortcut called dynamic programming. Dynamic programming methods were first described in the s, outside the context of bioinformatics, and first applied in this context by Needleman and Wunsch in [ 22 ].
These methods find an optimal solution to a given problem by breaking the original problem into smaller and smaller subproblems until the subproblems have a trivial solution, and then using those solutions to construct solutions for larger and larger portions of the original problem.
In sequence comparison, the overall problem is determining the optimal alignment of two sequences. This is broken down into smaller and smaller alignments of parts of one sequence with parts of another sequence to the smallest case, which is the alignment of a single residue from one sequence with a single residue from the other sequence.
This solution to this smallest subproblem is known, and is taken from the scoring matrix. A generalization of the recursive dynamic programming approach, the Smith-Waterman algorithm [ 23 ] is an exhaustive, mathematically optimal method, which handles sequence comparisons in a single computation and is guaranteed to find the highest scoring alignment.
The algorithm incorporates the concepts of mismatches and gaps, and identifies optimal local alignments. Local alignments, where parts of one sequence are aligned to parts of another are more biologically relevant than global alignments where entire sequences are aligned to each other, because long regions of high similarity are the exception, rather than the rule, for most biological applications.
As fast as computers are, and as efficient as the dynamic programming algorithms are, they are still far too slow to enable exhaustive searches of huge sequence repositories such as GenBank [ 24 , 25 ] or SWISS-PROT [ 26 , 27 ]. An exhaustive search of GenBank is still beyond the reach of most researchers' computer power - and with the growth of sequence databases outstripping increases in computation speed, this situation is not going to get better any time soon.
Neither is guaranteed to find the best local alignment, but they almost always do. These high-scoring 'hits' are used as 'seeds' for the slower, more sophisticated dynamic programming algorithm. BLAST also performs some pre-processing of the query sequence - to filter out low-complexity regions such as CA repeats and to discard words not likely to form high-scoring pairs. From a practical standpoint, BLAST is generally the way to go, not only because of its better accuracy, but also because of its availability and its wide acceptance as the standard.
If we define a segment as a contiguous subsequence of a nucleotide or amino-acid sequence, and a segment pair as a pair of segments of the same length, one from each of the two sequences being compared, then the task that BLAST performs is the identification of all pairs of similar segments whose score exceeds a given threshold.
The resulting pairs of similar segments are called high-scoring segment pairs HSPs. The segment pair with the highest score is the maximal-scoring segment pair MSP ; its alignment cannot be improved by extending it or shortening it.
Detail for each of the steps is as follows. This word list is then expanded to include all high-scoring matching words, keeping only those that score more than the neighborhood word score threshold T when scored using a scoring matrix such as PAM or BLOSUM For typical parameter values, this results in about 50 words per residue of the query sequence.
Low compositional complexity or short-periodicity repeats can yield extremely large numbers of statistically significant but biologically uninteresting results. The filtering and removal of these can be controlled with the -F flag of the stand-alone version of BLAST and with check boxes in the web version. The default word lengths are 3 and 11, for amino-acid sequences and nucleotide sequences, respectively, and are adjustable using the -W flag in the stand-alone version.
No gaps are allowed. The list of matches is reduced by taking only those that will score above a given threshold, called the neighborhood word-score threshold. There is a trade-off at this stage between speed and sensitivity: a higher threshold gives greater speed but increases the chance of missing relevant pairs. Approximately 50 of these matches are usually kept for each of the words generated from the original query.
In the second step, BLAST searches through the target sequence database for exact matches to the word list generated Figure 3b. Because BLAST has already pre-processed and indexed the databases for the occurrence of all words in each sequence in the database, this search is extremely fast.
If a match is found, it is used to seed a possible alignment between the query and the database sequences. In the third step, the original BLAST method tried to extend the alignment from the matching words in both directions as long as the score continued to increase Figure 3c.
The resulting alignment was called a high-scoring pair, or HSP. Gapped BLAST [ 28 ] uses a lower threshold for generating the list of high-scoring matching words; the algorithm uses short matched regions with no insertions or deletions between them and within a certain distance of each other as the starting points for longer ungapped alignments. Next, BLAST determines whether each score found by one of the above methods is greater in value than a given cutoff score S, determined empirically by examining the range of scores given by comparing random sequences and then choosing a value that is significantly greater.
The maximal scoring pairs, or MSPs, from the entire database are identified and listed. Finally, BLAST determines the statistical significance of each score, initially by calculating the probability that two random sequences, one the length of the query sequence and the other the length of the database the sum of the lengths of all of the database sequences with the same composition nucleotide or amino acid could produce the calculated score.
Sometimes, two or more segment pairs can be made into a longer alignment; in such cases, a combined assessment of the significance is made by one of two methods [ 29 ]: the Poisson method is based on the assumption that the probability of the multiple scores is higher when the lower score of each set is higher; the sum-of-scores method calculates the probability of the sum of the scores.
When the expectation value for a given database sequence satisfies the user-selectable threshold parameter set by the - e flag with the stand-alone version; see Table 3 , the match is reported. The first part of the output is the header and gives the BLAST program and version used, the reference, and the names and lengths of the query sequence and the target database. The second part is a summary of the sequences producing significant alignments along with normalized bit scores and E values.
The third part displays the alignments and includes more detailed information about the scores, including raw score, bit score, E value and identity. If you frame your question carefully, meaning a careful choice of parameters and databases against which to search, BLAST and other sequence comparison tools can provide a vast resource of useful information. But in using sequence similarity to infer homology, one should take care to follow a few simple rules.
The first is the header a , which includes the BLAST program and version used, and the name and length of both the query sequence and of the target database. In this case, the program used was BLASTX, so the query sequence was a nucleotide sequence and was translated in all six frames and compared to a protein database, nr, which is the non-redundant protein database maintained by NCBI. The second part of the output b is a summary of sequences producing significant alignments, along with both normalized scores and E values see text for further details; only the four highest-scoring hits are shown.
Given that nucleotide and protein databases are not uniformly populated, nucleotide and amino-acid sequence comparisons should be used to complement each other.
Despite the fact that protein databases tend to be more sparsely populated than nucleotide databases, the constraints of protein evolution - the fact that a protein folds into a functional structure - along with the redundancy of the genetic code, make protein sequence comparison a more powerful tool for inferring structure and function from sequence.
Although most sequences that share significant similarity are homologous, many homologous sequences do not share significant similarity. In addition, repetitive sequences violate certain assumptions made in the statistical theory that underlies BLAST. Ensure that matches are not simply due to biased amino-acid composition. Certain sequences, such as low-complexity regions, can display significant similarity when there is no significant homology. And keep in mind that similarity spread out over a whole domain is likely to be more biologically significant than short, nearly exact matches.
The significance and meaning of raw BLAST scores depends on many things, so they are, at best, meaningless and may be deceptive. It is much better to show an alignment. Although normalized scores allow comparison of the results of searches using different scoring systems, they are an extreme reduction of the rich information available in an alignment.
Measuring structural homology involves computing the geometric—topological features of a space. One approach used togenerate and analyze three-dimensional 3D protein structures is homology modeling also called comparative modeling or knowledge-based modeling.
Homology modeling works by finding similar sequences on the basis of the obvious fact that 3D similarity reflects 2D similarity. Nonetheless, it is important to note that homologous structures do not imply sequence similarity as a necessary condition. Sequence identity is the amount of characters which match exactly between two different sequences.
This is deduced in terms of the identity distance measure. Similarity in alignment tells the resemblance between two sequences when compared while identity in sequence alignment tells the amount of characters that match exactly between two different sequences. Therefore, this is the key difference between similarity and identity in sequence alignment. Sequence alignment helps to identify regions of resemblance in DNA, RNA or protein resulted due to functional, structural or evolutionary relationship between the sequences.
Hence, similarity and identity are two key terms in the context of sequence alignment. The key difference between these two terms is that similarity is the resemblance between two sequences in comparison whilst identity is the number of characters that match exactly between two different sequences.
Thus, this is the summary of the difference between similarity and identity in sequence alignment. Org Wiki, Available here. Samanthi Udayangani holds a B.
Degree in Plant Science, M. Your email address will not be published. Figure Similarity in Sequence Alignment.
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