2. Bioinformatics Lecture 2 By: Dr. Mehdi Mansouri M.mansouri@uk.ac.ir
Mehr 1395

3. Sequence Alignment

4. Sequence comparison lies at the heart of bioinformatics analysis.
It is an important first step toward structural and functional analysis of newly determined sequences.

5. Driving force of evolution
Mutation
Natural Selection

6. EVOLUTIONARY BASIS
Biologists define evolution as genetic change in a population across generations.
Over time, this process of genetic change can give rise to new genes, new traits and new species, all brought about through changes in the genetic code or DNA.

7. Structural variations
Deletions
Insertions
Inversions
Tandem duplications
Translocations
And more complex rearrangements

8. Sequence Homology VS Sequence Similarity
When two sequences are descended from a common evolutionary origin, they are said to have a homologous relationship
Sequence similarity is percentage of aligned residues that are similar in physiochemical properties such as size, charge, and hydrophobicity.

9. Paralogue
A homologue which arose through gene duplication in the same species/chromosome
Orthologue
A homologue which arose through speciation (found in different species)

10. Sequence Similarity VS Sequence Identity
Sequence similarity and sequence identity are synonymous for nucleotide sequences.

11. Figure 3. 1: The three zones of protein sequence alignments
Figure 3.1: The three zones of protein sequence alignments. Two protein sequences can be regarded
as homologous if the percentage sequence identity falls in the safe zone. Sequence identity values below
the zone boundary, but above 20%, are considered to be in the twilight zone, where homologous relationships
are less certain. The region below 20% is the midnight zone, where homologous relationships
cannot be reliably determined.

12. Sequence alignment

13. Global Alignment and Local Alignment
In global alignment, two sequences to be aligned are assumed to be generally similar over their entire length
Local alignment, does not assume that the two sequences in question have similarity over the entire length.

15. Alignment Algorithms Dot matrix method Dynamic programming method
Word method.

16. Dot matrix method

17. Dynamic programming method

18. Gap Penalties
Performing optimal alignment between sequences often involves applying gaps that represent insertions and deletions.

19. Database Similarity Searching
A main application of pairwise alignment is retrieving biological sequences in databases based on similarity
Sensivity
Specificity
speed

20. BLAST
FASTA
Both BLAST and FASTA use a heuristic word method

21. BASIC LOCAL ALIGNMENT SEARCH TOOL (BLAST)
The BLAST program was developed by Stephen Altschul of NCBI in 1990

23. BLAST variants https://blast.ncbi.nlm.nih.gov
BLASTN
BLASTP
BLASTX
TBLASTN
TBLASTX