1.
Lecture 3.1 BLAST

2. Sequence Similarity Searching: Understanding and Using Web Based BLAST
First & Last Name
February X, 2005
Sequence Similarity Searching: Understanding and Using Web Based BLAST
Dr. Joanne Fox
Lecture 3.1 BLAST
(c) 2005 CGDN

3. Concepts of Sequence Similarity Searching
The premise:
The sequence itself is not informative; it must be analyzed by comparative methods against existing databases to develop hypothesis concerning relatives and function.
Lecture 3.1 BLAST

4. Important Terms for Sequence Similarity Searching with very different meanings
The extent to which nucleotide or protein sequences are related. 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.
Identity
The extent to which two (nucleotide or amino acid) sequences are invariant.
Homology
Similarity attributed to descent from a common ancestor.
It is your responsibility as an informed bioinformatician to use these terms correctly: A sequence is either homologous or not. Don’t use % with this term!
Lecture 3.1 BLAST

5. Sequence Similarity Searching: The Approach
Sequence similarity searching involves the use of a set of algorithms (such as the BLAST programs) to compare a query sequence to all the sequences in a specified database.
Comparisons are made in a pairwise fashion. Each comparison is given a score reflecting the degree of similarity between the query and the sequence being compared.
The higher the score, the greater the degree of similarity. The similarity is measured and shown by aligning two sequences.
Lecture 3.1 BLAST

6. Sequence Similarity Searching – The Alignment
Alignments can be global or local (this is algorithm specific)
A global alignment is an optimal alignment that includes all characters from each sequence (clustal generates global alignments)
A local alignment is an optimal alignment that includes only the most similar local region or regions (BLAST generates local alignments).
Lecture 3.1 BLAST

7. QUERY sequence(s) BLAST results BLAST program BLAST database
Lecture 3.1 BLAST

8. BLAST program Topics: The different blast programs
Understanding the BLAST algorithm
Word size
HSPs
Understanding BLAST statistics
The alignment score (S)
Scoring Matrices
Dealing with gaps in an alignment
The expectation value (E)
Lecture 3.1 BLAST

9. The BLAST algorithm
The BLAST programs (Basic Local Alignment Search Tools) are a set of sequence comparison algorithms introduced in 1990 that are used to search sequence databases for optimal local alignments to a query.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) “Basic local alignment search tool.” J. Mol. Biol. 215:
Altschul SF, Madden TL, Schaeffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) “Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.” NAR 25:
Lecture 3.1 BLAST

10. http://www.ncbi.nlm.nih.gov/BLAST/ blastp blastn blastx tblastn
tblastx
Lecture 3.1 BLAST

11. Several different BLAST programs:
Several different BLAST programs:
Program 
Description
blastp
Compares an amino acid query sequence against a protein sequence database.
blastn
Compares a nucleotide query sequence against a nucleotide sequence database.
blastx
Compares a nucleotide query sequence translated in all reading frames against a protein sequence database. You could use this option to find potential translation products of an unknown nucleotide sequence.
tblastn
Compares a protein query sequence against a nucleotide sequence database dynamically translated in all reading frames.
tblastx
Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database. Please note that the tblastx program cannot be used with the nr database on the BLAST Web page because it is computationally intensive.
Lecture 3.1 BLAST

12. Other BLAST programs BLAST 2 Sequences (bl2seq) VecScreen
Aligns two sequences of your choice
Can do different types of comparison ex. Blastx
Gives dot-plot like output
VecScreen
Compares query with sequences of known cloning vectors
Both very handy for sequencing!
Lecture 3.1 BLAST

13. More BLAST programs BLAST against genomes
Many available
BLAST parameters pre-optimized
Handy for mapping query to genome
Search for short exact matches
Great for checking probes and primers
Lecture 3.1 BLAST

14. MegaBLAST megaBLAST More detailed info: see megaBLAST pages
For aligning sequences which differ slightly due to sequencing errors etc.
Very efficient for long query sequences
Uses big word (k-tuple) sizes to start search
Very fast
Accepts batch submissions of ESTs
Can upload files of sequences as queries
More detailed info: see megaBLAST pages
Lecture 3.1 BLAST

15. How Does BLAST Really Work?
The BLAST programs improved the overall speed of searches while retaining good sensitivity (important as databases continue to grow) by breaking the query and database sequences into fragments ("words"), and initially seeking matches between fragments.
Word hits are then extended in either direction in an attempt to generate an alignment with a score exceeding the threshold of "S".
Lecture 3.1 BLAST

16. Picture used with permission from Chapter 11 of “Bioinformatics:
A Practical Guide to the Analysis of Genes and Proteins”
Lecture 3.1 BLAST

17. How Does BLAST Really Work?
The BLAST programs improved the overall speed of searches while retaining good sensitivity (important as databases continue to grow) by breaking the query and database sequences into fragments ("words"), and initially seeking matches between fragments.
Word hits are then extended in either direction in an attempt to generate an alignment with a score exceeding the threshold of "S".
Lecture 3.1 BLAST

18. Picture used with permission from Chapter 11 of “Bioinformatics:
A Practical Guide to the Analysis of Genes and Proteins”
Lecture 3.1 BLAST

19. Each BLAST “hit” generates an alignment that can contain one or more these high scoring pairs (HSPs)
Lecture 3.1 BLAST

20. Where does the score (S) come from?
The quality of each pair-wise alignment is represented as a score and the scores are ranked.
Scoring matrices are used to calculate the score of the alignment base by base (DNA) or amino acid by amino acid (protein).
The alignment score will be the sum of the scores for each position.
Lecture 3.1 BLAST

21. What’s a scoring matrix?
Substitution matrices are used for amino acid alignments. These are matrices in which each possible residue substitution is given a score reflecting the probability that it is related to the corresponding residue in the query.
A unitary matrix is used for DNA pairs because each position can be given a score of +1 if it matches and a score of zero if it does not.
Lecture 3.1 BLAST

22. PAM vs. BLOSUM scoring matrices
BLOSUM 62 is the default matrix in BLAST 2.0. Though it is tailored for comparisons of moderately distant proteins, it performs well in detecting closer relationships. A search for distant relatives may be more sensitive with a different matrix.
Lecture 3.1 BLAST

23. PAM vs BLOSUM scoring matrices
The PAM Family
PAM matrices are based on global alignments of closely related proteins.
The PAM1 is the matrix calculated from comparisons of sequences with no more than 1% divergence.
Other PAM matrices are extrapolated from PAM1.
The BLOSUM family
BLOSUM matrices are based on local alignments.
BLOSUM 62 is a matrix calculated from comparisons of sequences with no less than 62% divergence.
All BLOSUM matrices are based on observed alignments; they are not extrapolated from comparisons of closely related proteins.
Lecture 3.1 BLAST

24. What happens if you have a gap in the alignment?
A gap is a position in the alignment at which a letter is paired with a null
Gap scores are negative. Since a single mutational event may cause the insertion or deletion of more than one residue, the presence of a gap is frequently ascribed more significance than the length of the gap.
Hence the gap is penalized heavily, whereas a lesser penalty is assigned to each subsequent residue in the gap.
Lecture 3.1 BLAST

25. What do the Score and the e-value really mean?
The quality of the alignment is represented by the Score.
Score (S)
The score of an alignment is calculated as the sum of substitution and gap scores. Substitution scores are given by a look-up table (PAM, BLOSUM) whereas gap scores are assigned empirically .
The significance of each alignment is computed as an E value.
E value (E)
Expectation value. The number of different alignments with scores equivalent to or better than S that are expected to occur in a database search by chance. The lower the E value, the more significant the score.
Lecture 3.1 BLAST

26. Is the E-value the same P-value?
E value (E)
Expectation value. The number of different alignments with scores equivalent to or better than S that are expected to occur in a database search by chance. The lower the E value, the more significant the score.
When E < 0.01, P-values and E-value are nearly identical.
So, the E-value is the number of times you expect to see your hit occur in the database (with as good as or better score) due to randomn chance alone.
Lecture 3.1 BLAST

27. QUERY sequence(s) BLAST results BLAST program BLAST database
Lecture 3.1 BLAST

28. BLAST databases Topics:
The different blast databases provided by the NCBI
Protein databases
Nucleotide databases
Genomic databases
Considerations for choosing a BLAST database
Custom databases for BLAST
Lecture 3.1 BLAST

29. BLAST protein databases available at through blastp web interface @ NCBI
blastp db
Lecture 3.1 BLAST

30. BLAST nucleotide databases available at through blastn web interface @ NCBI
blastn db
Lecture 3.1 BLAST

31. Considerations for choosing a BLAST database
First consider your research question:
Are you looking for an ortholog in a particular species?
BLAST against the genome of that species.
Are you looking for additional members of a protein family across all species?
BLAST against nr, if you can’t find hits check wgs, htgs, and the trace archives.
Are you looking to annotate genes in your species of interest?
BLAST against known genes (RefSeq) and/or ESTs from a closely related species.
Lecture 3.1 BLAST

32. When choosing a database for BLAST…
It is important to know your reagents.
Changing your choice of database is changing your search space completely
Database size affects the BLAST statistics
record BLAST parameters, database choice, database size in your bioinformatics lab book, just as you would for your wet-bench experiments.
Databases change rapidly and are updated frequently
It may be necessary to repeat your analyses
Lecture 3.1 BLAST

33. Creating Custom Databases for BLAST
UBiC FAQ
Lecture 3.1 BLAST

34. QUERY sequence(s) BLAST results BLAST program BLAST database
Lecture 3.1 BLAST

35. BLAST results Topics: Choosing the right BLAST program
First & Last Name
February X, 2005
Topics:
BLAST results
Choosing the right BLAST program
Running a blastp search
BLAST parameters and options to consider
Viewing BLAST results
Look at your alignments
Using the BLAST taxonomy report
Lecture portion ends – should be 1hr to here (34 slides) that leaves 20 min for the remaining ~25 demo slides
Lecture 3.1 BLAST
(c) 2005 CGDN

36. http://www.ncbi.nlm.nih.gov/BLAST/ blastp blastn blastx tblastn
tblastx
Lecture 3.1 BLAST

37. http://www.ncbi.nlm.nih.gov/BLAST/ Program selection guide
Lecture 3.1 BLAST

38. “What BLAST program should I use
“What BLAST program should I use?” – check the NCBI’s BLAST Program selection guide
Lecture 3.1 BLAST

39.
blastp
Lecture 3.1 BLAST

40. Input your query (gi|231571) as FASTA, raw sequence, or Accession/ID and choose your database
Lecture 3.1 BLAST

41. Links to more information can be found on the BLAST page
Lecture 3.1 BLAST

42. BLAST parameters and options to consider:
conserved domains
Entrez query
E-value cutoff
Word size
Lecture 3.1 BLAST

43. More BLAST parameters and options to consider:
filtering
gap penalities
matrix
Lecture 3.1 BLAST

44. Run your BLAST search:
BLAST
Lecture 3.1 BLAST

45. The BLAST Queue:
click for more info
Note your RID
Lecture 3.1 BLAST

46. Formatting and Retrieving your BLAST results:
options
Lecture 3.1 BLAST

47. A graphical view of your BLAST results:
Lecture 3.1 BLAST

48. The BLAST “hit” list: Score E-Value GenBank alignment EntrezGene
Lecture 3.1 BLAST

49. The BLAST pairwise alignments
Identity
Similarity
Lecture 3.1 BLAST

50. Sorting BLAST results by Taxonomy
Report
Lecture 3.1 BLAST

51. Tax BLAST Report Summary hits by lineage BLAST hits by organism
Lecture 3.1 BLAST

52. BLAST statistics to record in your bioinformatics labbook
Record the statistics that are found at
bottom of your BLAST results page
Lecture 3.1 BLAST

53. Homology: Some Guidelines
First & Last Name
February X, 2005
Homology: Some Guidelines
Similarity can be indicative of homology
Generally, if two sequences are significantly similar over entire length they are likely homologous
Low complexity regions can be highly similar without being homologous
Homologous sequences not always highly similar
Suggested BLAST Cutoffs
(source: Chapter 11 – Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins)
For nucleotide based searches, one should look for hits with E-values of 10-6 or less and sequence identity of 70% or more
For protein based searches, one should look for hits with E-values of 10-3 or less and sequence identity of 25% or more
Need to bring class back together with 10 minutes to go in classroom time.
Lecture 3.1 BLAST
(c) 2005 CGDN

54. Advanced BLAST programs
The NCBI BLAST pages have several advanced BLAST methods available
PSI-BLAST
PHI-BLAST
RPS-BLAST
All are powerful methods based on protein similarities
Lecture 3.1 BLAST

55. PSI-BLAST Position Specific Iterated – BLAST
A cycling/iterative method
Gives increased sensitivity for detecting distantly related proteins
Can give insight into functional relationships
Very refined statistical methods
Fast – still based on BLAST methods
Simple to use
Lecture 3.1 BLAST

56. How does PSI-BLAST work?
First, a standard blastp is performed
The highest scoring hits are used to generate a multiple alignment
A Position Specific Scoring Matrix (PSSM) is generated from the multiple alignment.
Highly conserved residues get high scores
Less conserved residues get lower scores
The PSSM describes the sequence similarity between your query and all significant blastp hits
Another similarity search is performed, this time using the new PSSM instead of the standard BLOSUM or PAM matrices
- This PSSM (scoring matrix) is now customized to find sequences that are related to your original query
Steps 2-4 can be repeated until convergence
Convergence occurs when no new sequences appear after iteration
Lecture 3.1 BLAST

57.
PSI-BLAST
Lecture 3.1 BLAST

58. Format results for PSI-BLAST with inclusion E-value set at 0.005
Lecture 3.1 BLAST

59. Contributors
Special thanks to David Wishart, Andy Baxevanis, Stephanie Minnema, Sohrab Shah, and Francis Ouellette for their contributions to these materials
You are now ready to complete the BLAST assignment
Lecture 3.1 BLAST