Difference between revisions of "Alignment"

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<p><span style="font-size:14px">A sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.</span></p>
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<p><span style="font-size:14px">A sequence alignment is a method of arranging the sequences of DNA, RNA, or protein to identify similar regions in order to study the&nbsp;relationships between the sequences in functional, structural, or evolutionary aspects.</span></p>
  
 
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<p><span style="font-size:14px">Human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that &quot;forces&quot; the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database searching, that do not guarantee to find best matches. Hybrid methods, known as semi-global or &quot;glocal&quot; (short for global-local) methods, attempt to find the best possible alignment that includes the start and end of one or the other sequence. This can be especially useful when the downstream part of one sequence overlaps with the upstream part of the other sequence. In this case, neither global nor local alignment is entirely appropriate: a global alignment would attempt to force the alignment to extend beyond the region of overlap, while a local alignment might not fully cover the region of overlap.</span></p>
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<p><span style="font-size:14px">Many researchers have been trying to&nbsp;construct&nbsp;algorithms to produce&nbsp;high-quality sequence alignments.&nbsp;Computational methods of&nbsp;alignment generally categorized into&nbsp;two method</span>&mdash;<span style="font-size:14px">global alignments and local alignments. A&nbsp;global alignment finds&nbsp;globally&nbsp;optimized alignment, forcing&nbsp;the alignment to span the entire length of all query sequences. On the other hand, local alignments find&nbsp;similar region&nbsp;within long sequences that are overally divergent. Local alignments are more&nbsp;prefered, but it is&nbsp;more difficult to calculate since&nbsp;similar region should be identified. In order to deal with sequence alignment, many computational algorithms have been applied. For example, dynamic programming is slow but is formal method to correct alignment. Also, there are&nbsp;efficient&nbsp;algorithms or method utilizing probability&nbsp;for large-scale database searching, but it doesn&#39;t guarantee best fitted alignment. Hybrid methods, known as semi-global or glocal</span>&mdash;<span style="font-size:14px">short for global-local</span>&mdash;<span style="font-size:14px">methods, tries to&nbsp;find the best&nbsp;alignment. This method is&nbsp;useful for the sequence whose&nbsp;downstream part&nbsp;is&nbsp;overlapped&nbsp;with the upstream part&nbsp;of the other sequence. In this case, both&nbsp;global and&nbsp;local alignment is&nbsp;entirely inappropriate, since a&nbsp;global alignment&nbsp;force the alignment to extend beyond the&nbsp;overlap, while&nbsp;a local alignment don&#39;t&nbsp;fully cover the&nbsp;overlap.</span></p>
  
 
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<p><span style="font-size:14px">Phylogenetics and sequence alignment are closely related fields due to the shared necessity of evaluating sequence relatedness. Sequence alignment can be used for construction and interpretation of phylogenetic trees, which are used to classify the evolutionary relationships between homologous genes represented in the genomes of divergent species. Roughly speaking, high sequence identity suggests that the sequences in question have a comparatively young most recent common ancestor, while low identity suggests that the divergence is more ancient.</span></p>
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<p><span style="font-size:14px">The relationship between&nbsp;phylogenetics and sequence alignment is close&nbsp;since both fields need&nbsp;to&nbsp;evaluate&nbsp;sequence relatedness. Sequence alignment is&nbsp;useful&nbsp;for construction or&nbsp;interpretation of phylogenetic trees. phylogenetic trees is used&nbsp;to classify the evolution&nbsp;between homologs&nbsp;in the genomes of diverged&nbsp;species. Roughly, high score&nbsp;identity in sequences means that the sequences&nbsp;have a comparatively close&nbsp;recent common ancestor, while low identity suggests&nbsp;the divergence happened&nbsp;more ancient era.</span></p>
  
 
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Revision as of 00:53, 11 December 2015

Sequence Alignment

 

 

What is sequence alignment?

 

A sequence alignment is a method of arranging the sequences of DNA, RNA, or protein to identify similar regions in order to study the relationships between the sequences in functional, structural, or evolutionary aspects.

 

How sequence is aligned?

 

Many researchers have been trying to construct algorithms to produce high-quality sequence alignments. Computational methods of alignment generally categorized into two methodglobal alignments and local alignments. A global alignment finds globally optimized alignment, forcing the alignment to span the entire length of all query sequences. On the other hand, local alignments find similar region within long sequences that are overally divergent. Local alignments are more prefered, but it is more difficult to calculate since similar region should be identified. In order to deal with sequence alignment, many computational algorithms have been applied. For example, dynamic programming is slow but is formal method to correct alignment. Also, there are efficient algorithms or method utilizing probability for large-scale database searching, but it doesn't guarantee best fitted alignment. Hybrid methods, known as semi-global or glocalshort for global-localmethods, tries to find the best alignment. This method is useful for the sequence whose downstream part is overlapped with the upstream part of the other sequence. In this case, both global and local alignment is entirely inappropriate, since a global alignment force the alignment to extend beyond the overlap, while a local alignment don't fully cover the overlap.

 

How sequence alignment be used?

 

The relationship between phylogenetics and sequence alignment is close since both fields need to evaluate sequence relatedness. Sequence alignment is useful for construction or interpretation of phylogenetic trees. phylogenetic trees is used to classify the evolution between homologs in the genomes of diverged species. Roughly, high score identity in sequences means that the sequences have a comparatively close recent common ancestor, while low identity suggests the divergence happened more ancient era.

 

References

  1. https://en.wikipedia.org/wiki/Sequence_alignment
  2. Valery, O. P., Mikhail A, R., & Vladimir G. T. (2011). Comparative analysis of the quality of a global algorithm and a local algorithm for alignment of two sequences. Algorithms Mol Biol. doi:  10.1186/1748-7188-6-25.
  3. Philippe O., & Olivier B. (2010). Where Does the Alignment Score Distribution Shape Come from? Evolutionary Bioinformatics. 6: 159–187.