Sequence alignment 1
- 2. CONTENT 1-SEQUENCE ALIGNMENT 2-APPLICATIONS OF SEQUENCE ALIGNMENT 3-DEFINITIONS FOR ALIGNED SEQUENCES (a) ALGORITHM (b) DIVERGENT EVOLUTION (c) CONSERVATION (d) PROGRAM (e) IDENTITY (f) SIMILARITY (g) HOMOLOGS (h) HETEROLOGS (i) ANALOGS (j) ORTHOLOGS (k) PARALOGS (l) XENOLOGS 4-PHYSICOCHEMICAL RELATIONSHIPS BETWEEN AMINO 5-MEASURES OF SEQUENCE SIMILARITY (a) HAMMING AND LEVENSHTEIN DISTANCES (b) CONCEPT OF SIMILARITY AND DISTANCE TABLE (c) SIMPLE MATCHING COEFFICIENT
- 3. SEQUENCE ALIGNMENT • Sequence alignment describes the relationship between biological sequences by designating portions of sequences that correspond to each other. • It is the method used to analyze the similarities and differences at the level of individual bases or amino acids with the aim of inferring structural, functional and evolutionary relationships or random events among the sequences. • It is the identification of residue- residue correspondence OR • Any assignment of correspondence that preserves the order of the residues within the sequences is an alignment.
- 4. APPLICATIONS OF SEQUENCE ALIGNMENT INFORMATIONS that are gained by aligning DNA, RNA and protein sequences---------------- ● Searching for patterns and informative elements within a sequence. ● Obtaining statistical information on a sequence. ● Searching for similarities between two sequences, or many sequences ● Constructing phylogenetic trees based on sequences. ● Predicting and analyzing the secondary/tertiary structures and folding on the basis of the sequence.
- 5. APPLICATIONS OF SEQUENCE ALIGNMENT ● Identifying unknown sequences. ● Finding other members of multigene families. ● Gaining information for primer designing. ● Reconstructing long sequence of DNA from string fragments. ● Determining physical and genetic maps from probe data under various experimental protocols. ● Predicting function of actual gene products. ● Getting information for molecular modelling.
- 6. DEFINITIONS FOR ALIGNED SEQUENCES ALGORITHM Algorithm is defined by a logical sequence of steps by which a task can be performed. It is a set of rules for calculating or solving a problem which normally is carried out by a computer program. Important features of an algorithm are- (i) It should stop after a finite number of steps. (ii) All steps of an algorithm must be precisely defined. (iii) Input to the algorithm must be specified. (iv) Output to the algorithm must be specified. (v) Algorithm must be very effective. Thus algorithm is a complete and precise specification of a method for solving a problem
- 7. DEFINITIONS FOR ALIGNED SEQUENCES DIVERGENT EVOLUTION CONSERVATION Similarity among sequence could arise by chance or it could be a convergance towards a common sequence and structure and therefore function, through evolution, or, the similarity could arise from divergent evolution of the two sequences from a common ancestral sequence. The similarity that arises from this last mechanism alone (i.e. divergent evolution) is called homologs. Homologs, heterologs, anologs, orthologs, paralogs and xenologs are words that describe the different ways in which sequence similarity could arise. Changes at a specific position of an amino acid in proteins (less commonly nucleotides in DNA sequences) that preserves the physicochemical properties of the original residue is known as conservation.
- 8. DEFINITIONS FOR ALIGNED SEQUENCES PROGRAM IDENTITY SIMILARITY : A program is an implementation of an algorithm.. The extent to which two (nucleotide or amino acid) sequences are invariant expresses identity among the sequences. The extent to which nucleotide or protein sequences are related is known as similarity. The extent of similarity between two sequences can be based on percent sequence identity and/or conservation. In BLAST similarity refers to a positive matrix score.
- 9. DEFINITIONS FOR ALIGNED SEQUENCES Homologs ‘Homologs’ is a general term to indicate sequences with common origin. It is qualitative term and is somewhat arbitrary in the sense that it is up to the researcher to decide what level of similarity indicates homology. Numerical indicators of similarity determined by the sequence alignment programs, other parameters derived from biochemistry or other areas of biology may contribute to the decision on whether a pair of sequence is homologous.
- 10. DEFINITIONS FOR ALIGNED SEQUENCES Heterologs : The opposite of 'homologs' is 'heterologs'. Heterologous sequences may still be similar, but they do not have a common origin, and nor do they have a common function or activity.
- 11. DEFINITIONS FOR ALIGNED SEQUENCES ANALOGS : Sequences that have same function but lack sufficient similarity to imply common origin are said to be 'analogs'. What it means is that analogous sequences followed evolutionary pathways from different origins to converge upon same function and activity. They have homologous function but heterologous origins. They may be considered a product of convergent evolution. Examples are Chymotrypsin and subtilisin proteins.
- 12. DEFINITIONS FOR ALIGNED SEQUENCES ORTHOLOGS Orthologs’ are homologs that arise by speciation. The implication is that the two sequences are currently taken from two different species, but they show similarity because they are both derived from the same ancestral sequence that was present in the ancestral organism. The amount of difference in two orthologous sequences may be taken as a rough indicator of the amount of time that has passed since the speciation event took place. For example hemoglobin sequences from horse and zebra.
- 13. DEFINITIONS FOR ALIGNED SEQUENCES PARALOGS ‘Paralogs’ are homologs that arise by gene duplication, without this duplication event being followed by a speciation event. It means that paralogs are homologous sequences that exist in the same organism, and that have different functions. Paralogs have homologous origin but heterologous function. for e g. myoglobin and haemoglobin from human beings. In DNA sequence, the functional differences between paralogs may simply be that one is a functional gene, while the other is a silent non-expressed gene or a pseudogene.
- 14. DEFINITIONS FOR ALIGNED SEQUENCES XENOLOGS ‘Xenologs’ are homologs resulting from horizontal gene transfer. They are an exception to the rule that the homologs are always descended from a common ancestor. horizontal or lateral exchange of genes may take place between different species. Function of such genes would be homologous.
- 15. PHYSICOCHEMICAL RELATIONSHIPS BETWEEN AMINO ACID Construction of biologically significant alignments should consider the fact that protein evolution is constrained by the chemical properties of amino acids, and by the degeneracy of the genetic code. Chemically conservative replacements tend to occur more frequently than replacements with amino acids that are chemically different. For example, it is more likely to see a substitution of the Leucine with Isoleucine, both of which are nonpolar, than a substitution of Aspartic acid, which is negatively charged, for Leucine. Such changes are less likely to affect the structure and function of the protein. The figure given below gives an idea of allowed substitutions among amino acids
- 16. PHYSICOCHEMICAL RELATIONSHIPS BETWEEN AMINO ACIDS
- 17. MEASURES OF SEQUENCE SIMILARITY Quantitative measures of sequence similarity and difference; two measures of the distance between two sequence are : (1) The Hamming Distance- defines the number of positions with mismatching characters between two strings of equal length. (2)The Levenshtein (Edit) Distance- between two strings of not necessarily equal length, is the minimal number of 'edit operations' required to change one string into other, where an edit operation is a deletion, insertion or alteration of a single character in either sequence. It is desirable to assign variable weights to different edit operations since certain changes are more likely to happen naturally than others.
- 18. MEASURES OF SEQUENCE SIMILARITY A given sequence of edit operations induces a unique alignment, but not vice versa. Example AGTC CGTA Hamming distance = 2 AG-TCC CGCTCA Levenshtein distance = 3 Hamming and Levenshtein distances measure the dissimilarity of two sequences: similar sequences give small distances and dissimilar sequences give large distances.
- 19. CONCEPT OF SIMILARITY AND DISTANCE TABLE a b c d e a 100 65 50 50 50 b 65 100 50 50 50 c 50 50 100 97 65 d 50 50 97 100 65 e 50 50 65 65 100 By taking hypothetical tables concept of similarity and distance may be explained. Here (a-e) are five organisms who are scored for resemblance. Table-1 table for similarity Similarity table shows percent of matches, thus the diagonal (in which each species is compared to itself) consists of 100% values. (Such data forms the basis of Adansonian analysis or numerical taxonomy. Definition of the term is “the classification of organisms based on giving equal weight to every character of the organism; this principle has its greatest application in numeric taxonomy.")
- 20. CONCEPT OF SIMILARITY AND DISTANCE TABLE a b c d e a 0 6 11 11 11 b 6 0 11 11 11 c 11 11 0 2 6 d 11 11 2 0 6 e 11 11 6 6 0 The numbers in the distance table show percent differences or distances. Thus, the diagonal consists of 0% values. Distance tables are in general use. A common measure of difference between macromolecular sequences is 100-S where S is the percentage of identical monomers when sequences have been optimally aligned. Table 2- Distance table
- 21. SIMPLE MATCHING COEFFICIENT The simple matching coefficient (SMC) is a statistic used for comparing the similarity and diversity of sample sets.
- 22. M01 is the total number of attributes where the attribute of A is 0 and the attribute of B is 1. M10 is the total number of attributes where the attribute of A is 1 and the attribute of B is 0. M00 is the total number of attributes where A and B both have a value of 0. The simple matching distance(SMD), which measures dissimilarity between sample sets, is given by 1-SMC. SIMPLE MATCHING COEFFICIENT