Blast Algorithm

on of the best blast algorithm presentation

1. WELCOME TO OUR PRESENTATION OF BIO-INFORMATICS

2. GROUP MEMBERS NAME A.K.M.ASADUZZAMAN KOUSHIK ROY MD.ZAHID HASAN MD.ASIF-AL-FAHAD

3. BLAST

4. Suppose you have acquired a DNA/Protein sequence derived from a sample of some environments such as lake, pond or plant. Introduction KLMNTRARLIVHISGLTRK……………………………………………… Sequencing process Cell Samples Your sequence

5. Introduction Or you might get a DNA/Protein sequence from a database such as NCBI/EMBL/Swiss-Prot. You might also find an interesting gene/sequence from a journal. KLMNTRARLIVHISGLTRK……………………………………………… Your sequence

6. In that case, you might want to know if the sequence that you have, already exists or is similar to some sequences in a database, may be down to a particular organism database. Why do you want to know that? Because you can infer structural, functional and evolutionary relationship to your query sequence. Introduction Already in here? Similar? Your sequence

7. Sequence Alignment In bioinformatics, 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.

8. Type Of Alignment Local Alignment Global Alignment

9. ???????????????????????????? Your Sequence Unknown Sequence What is this Sequence? Where does it come from? KLMNTRARLIVHISGLTRK

10. Introducing BLAST (Basic Local Alignment Search Tool) BLAST tool is used to compare a query sequence with a library or database of sequences. It uses a heuristic search algorithm based on statistical methods. The algorithm was invented by Stephen Altschul and his co-workers in 1990. BLAST programs were designed for fast database searching.

11. BLAST Algorithm

12. BLAST Algorithm

13. BLAST Algorithm

14. BLAST Algorithm (Protein) L E H K M G S Query Sequence Length 11 L E H E H K H K M This generates 11 – 3 + 1 = 9 words H K M H K M Y A N C Y A N W = 3

15. BLAST Algorithm Example L E H For each word from a window = 3, generate neighborhood words using BLOSUM62 matrix with score threshold = 11 L M H D E H L E H C E H L K H Q E H L F H L E R . . . All aligned with LEH using BLOSUM62 (then sorted by scores) 17 13 12 10 9 11 9 9 Score threshold (cut off here) 20 3 20 x 20 x 20 alignments Sorted by scores 3 Amino Acids

16. BLAST Algorithm Example L E H C E H L K H Q E H Word List DAPC QEH KRGWPNDC L E H Database sequences L E H L E H L E H L E H L K H L K H C E H C E H QEH Exact matches of words from the word list to the database sequences

17. Q E H D A P C Q E H K R G W P N D C For each exact word match, alignment is extended in both directions to find high score segments. Extended in the right direction Max drop off score X= 2 5 5 8 Score drop = 3 > X Score drop = 1 <= X Trim to max Query = Y A N C L E H K M G S K 5 23 5 10 18 M -1 22 G 6 28 S -3 25

18. Q E H D A P C Q E H K R G W P N D C For each exact word match, alignment is extended in both directions to find high score segments. Extended in the left direction K M G Max drop off score X= 2 5 5 8 Score drop = 3 > X Score drop = 2 <= X Query = Y A N C L E H K M G S 18 13 8 C 9 27 N -2 25 A 4 29 Y -3 26

19. BLAST Algorithm Example A P C Q E H K R G 5 -1 6 5 5 8 9 4 -2 Maximal Segment Pair (MSP) Pair Score = 4-2+9+5+5+8+5-1+6 = 39 A N C Q E H K M G BLOSUM62 Scoring Matrix

20. A P C Q E H K R G A N C Q E H K M G 39 Maximal Segment Pairs (MSPs) from other seeds Sorted by alignment scores 42 45 35 37 51 55 33 BLAST Algorithm Example Each match has its own E-Value

21. E-Value: The number of MSPs with similar score or higher that one can EXPECT to see by chance alone when searching a database of a particular size. BLAST Algorithm Expect Value (E-Value)

22. For example: if the E-Value is equal to 10 for a particular MSP with score S, one can say that actually…about 10 MSPs with score >= S can just happen by chance alone (for any query sequence). So most likely that our MSP is not a significant match at all. BLAST Algorithm Expect Value (E-Value)

23. If E-Value if very small e.x. 10 -4 (very high score S), one can say that it is almost impossible that there would be any MSP with score >= S. Thus, our MSP is a pretty significant match (homologous). BLAST Algorithm Expect Value (E-Value)

24. First: Calculate bit score S = Score of the alignment (Raw Score) , values depend on the scoring scheme and sequence composition of a database. [log value is natural logarithm (log base e)] BLAST Algorithm E-Value Calculation

25. The lower the E-Value, the better. E-Value can be used to limit the number of hits in the result page. BLAST Algorithm Expect Value (E-Value)

26. Second: Calculate E-Value = Bit Score m = query length n = length of database BLAST Algorithm E-Value Calculation

27. E-values of 10 -4 and lower indicate a significant homology. E-values between 10 -4 and 10 -2 should be checked (similar domains, maybe non-homologous). E-values between 10 -2 and 1 do not indicate a good homology BLAST Algorithm E-Value Interpretation

28. Gapped BLAST The Gapped BLAST algorithm allows gaps to be introduced into the alignments. That means similar regions are not broken into several segments. This method reflects biological relationships much better. This results in different parameter values when calculating E-Value ( , ).

29. BLAST programs Name Description Blastp Amino acid query sequence against a protein database Blastn Nucleotide query sequence against a nucleotide sequence database Blastx Nucleotide query sequence translated in all reading frames against a protein database Tblastn Protein query sequence against a nucleotide sequence database dynamically translated in all reading frames Tblastx Six frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

30. BLAST programs Name Common Word Size Blastp 3 Blastn 11 Blastx 3 Tblastn 3 Tblastx 3

31. BLAST Suggestion Where possible use translated sequence (Protein). Split large query sequence (if > 1000 for DNA, >200 for protein) into small ones. If the query has low complexity regions or repeated segments, remove them and repeat the search. IVLKVA LRPV LRPV LRPV WQARNGS Repeated segments might confuse the program to find the ‘real’ significant matches in a database.