Friday, 20 November 2020

Bioinformatics: Analysis of Biological Sequences (#bioinformatics)(#ipumusings)(#biochemistry)(#biotechnology)


Bioinformatics: Analysis of Biological Sequences (#bioinformatics)(#ipumusings)(#biochemistry)(#biotechnology)

Author: Shailesh Kumar Shukla 


Sequence comparison lies at the guts of bioinformatics analysis. it's a crucial initiative toward structural and purposeful analysis of fresh determined sequences. As new biological sequences are being generated at exponential rates, sequence comparison is changing into progressively necessary to draw purposeful and biological process reasoning of a replacement macromolecule with proteins already existing within the information.The foremost basic method during this variety of comparison is sequence alignment.


DNA and proteins are product of evolution. The building blocks of those biological macromolecules, ester bases, and amino acids form linear sequences that confirm the first structure of the molecules. These molecules is thought-about molecular fossils that write in code the history of lots of years of evolution.During this time period,the molecular sequences bear random changes, a number of that are hand-picked throughout the method of evolution because the hand-picked sequences step by step accumulate mutations and diverge over time, traces of evolution should still stay in bound parts of the sequences to permit identification of the common ancestry.

For example, active site residues of an enzyme family tend to be conserved because they are responsible for catalytic functions. Therefore, by comparing sequences through alignment, patterns of conservation and variation is known. The degree of sequence conservation within the alignment reveals biological process connection of various sequences, whereas the variation between sequences reflects the changes that have occurred throughout evolution within the type of substitutions, insertions, and deletions.

Identifying the biological process relationships between sequences helps to characterize the operate of unknown sequences. once a sequence alignment reveals vital similarity among a bunch of sequences, they will be thought-about as happiness to a similar family.If one member at intervals the family encompasses a familiar structure and performance, then that info is transferred to those who haven't nonetheless been through an experiment characterized. Therefore,sequence alignment is used as basis for prediction of structure and performance of uncharacterized sequences.


Sequence alignment provides reasoning for the connection of 2 sequences beneath study. If the 2 sequences share vital similarity, it's very unlikely that the intensive similarity between the 2 sequences has been non inheritable at random,meaning that the 2 sequences should have derived from a common evolutionary origin.

Alignment techniques are necessary to whole-genome analysis, during which the comparison between completely different genomes or from a similar species permits us to spot variations within the sequences and associate them with specific phenotypes.

The overall goal of pairwise sequence alignment is to seek out the simplest pairing of 2 sequences, such there's most correspondence among residues. to attain this goal, one sequence must be shifted relative to the opposite to seek out the position wherever most matches are found. There are two different alignment strategies that are often used: 

✸ Global alignment and 

✸ Local alignment.

Global Alignment and local Alignment-

In global alignment, 2 sequences to be aligned are assumed to be typically similar over their entire length. Alignment is carried out from beginning to end of both sequences to find the best possible alignment across the entire length between the two sequences. This technique is additional applicable for positioning 2 closely connected sequences of roughly a similar length.

Local alignment, on the opposite hand, doesn't assume that the 2 sequences in question have similarity over the complete length. It solely finds native regions with the very best level of similarity between the 2 sequences and aligns these regions while not regard for the alignment of the remainder of the sequence regions.The two sequences to be aligned is of various lengths. 

Bioinformatics: Analysis of Biological Sequences (#bioinformatics)(#ipumusings)(#biochemistry)(#biotechnology)
Figure 1

Bioinformatics: Analysis of Biological Sequences (#bioinformatics)(#ipumusings)(#biochemistry)(#biotechnology)

Figure 2-Global and local alignment of amino acids

(The sequence alignment is organized in rows and also the characters in columns).

Alignment can be categorized by type according to the number of sequences that are compared, which can be: 

I: simple and 

II: multiple.

The simple alignment specifically depicts the similarity relation between two sequences, while the multiple considers a value greater than three sequences.

About the algorithm used, it may be classified as optimal or heuristic .The optimum result is the best alignment possible,while the heuristic, although not presenting an optimal result, presents the best alignment for a given period of analysis.


Alignment algorithms, each global and local,are basically similar and solely take issue within the optimisation strategy employed in positioning similar residues.Each kinds of algorithms is supported one in all the 3 techniques: the matrix method, the dynamic programming technique, and also the word technique.

An overview of the alignment ways and also the main algorithms used are given in figure below.

Bioinformatics: Analysis of Biological Sequences (#bioinformatics)(#ipumusings)(#biochemistry)(#biotechnology)

Table below presents the main alignment programs and their characteristics.

Bioinformatics: Analysis of Biological Sequences (#bioinformatics)(#ipumusings)(#biochemistry)(#biotechnology)


1. Luscombe NM, Greenbaum D and Gerstein M (2001). what's bioinformatics? A planned definition and summary of the sphere. strategies Inf. Med. 40: 346-358 ten.1053/

2.Algorithms in bioinformatics. A sensible introduction by Wing-Kin Sung Updated twelve July 2016,

3. Nisbet, Robert (2009). "Bioinformatics". reference book of applied mathematics Analysis and dataprocessing Applications. John Elder IV, Gary Miner. tutorial Press. p. 328. ISBN 978-0080912035. Retrieved nine might 2014.

4.REVIEW-ARTICLE Bioinformatics: an outline and its applications by Wellison Charles district attorney timberland Diniz Updated sixteen March 2017, 5.An outlook into ultra-scale visualisation of large-scale biological knowledge by Nagiza F. Samatova updated twelve might 2008,

6.Figure References-

Figure 2- REVIEW-ARTICLE Bioinformatics: an outline and its applications by Wellison Charles district attorney timberland Diniz Updated sixteen March 2017,

About Author:

Shailesh Kumar Shukla is pursuing his BTech in Biochemical Engineering at University School of Chemical Technology, GGSIP University, Dwarka, Delhi. He wants to do his specialization in bioinformatics.

👉See Also:

     A table of Genetic Codes 

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