1. K-tuple methods Statistics of alignments Phylogenetics
Sequence Alignment
K-tuple methods Statistics of alignments
Phylogenetics

2. Database searches What is the problem?
Large number of sequences to search your query sequence against.
Various indexing schemes and heuristics are used, one of which is BLAST.
heuristic is a technique to solve a problem that ignores whether the solution can be proven to be correct, but usually produces a good solution, are intended to gain computational performance or conceptual simplicity potentially at the cost of accuracy or precision.

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.

4. Important Terms for Sequence Similarity Searching with very different meanings
The extent to which nucleotide or protein sequences are related. 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.

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.

6. Blast
QUERY sequence(s)
BLAST results
BLAST program
BLAST
database

7. Topics: BLAST program There are different blast programs
Understanding the BLAST algorithm
Word size
HSPs (High Scoring Pairs)
Understanding BLAST statistics
The alignment score (S)
Scoring Matrices
Dealing with gaps in an alignment
The expectation value (E)

8. The BLAST algorithm
The BLAST programs (Basic Local Alignment Search Tools) are a set of sequence comparison algorithms introduced in 1990 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:

9.
blastp
blastn
blastx
tblastn
tblastx

10. Other BLAST programs BLAST 2 Sequences (bl2seq)
Aligns two sequences of your choice
Gives dot-plot like output

11. 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

12. How Does BLAST 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 “T".

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

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

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

16. 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.

17. 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.

18. 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.

19. 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.

20. 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.

21. Percent Sequence Identity
The extent to which two nucleotide or amino acid sequences are invariant
A C C T G A G – A G
A C G T G – G C A G
mismatch
indel
70% identical

22. BLAST algorithm
Keyword search of all words of length w in the query of default length n in database of length m with score above threshold
w = 11 for nucleotide queries, 3 for proteins
Do local alignment extension for each hit of keyword search
Extend result until longest match above threshold is achieved and output

23. BLAST algorithm (cont’d)
keyword
Query: KRHRKVLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLKIFLENVIRD
GVK 18
GAK 16
GIK 16
GGK 14
GLK 13
GNK 12
GRK 11
GEK 11
GDK 11
Neighborhood
words
neighborhood
score threshold
(T = 13)
extension
Query: 22 VLRDNIQGITKPAIRRLARRGGVKRISGLIYEETRGVLK 60
+++DN +G + IR L G+K I+ L+ E+ RG++K
Sbjct: 226 IIKDNGRGFSGKQIRNLNYGIGLKVIADLV-EKHRGIIK 263
High-scoring Pair (HSP)

24. Original BLAST Dictionary All words of length w Alignment
Ungapped extensions until score falls below statistical threshold T
Output
All local alignments with score > statistical threshold

25. Original BLAST: Example
A C G A A G T A A G G T C C A G T
w = 4, T = 4
Exact keyword match of GGTC
Extend diagonals with mismatches until score falls below a threshold
Output result
GTAAGGTCC
GTTAGGTCC
C T G A T C C T G G A T T G C G A
From lectures by Serafim Batzoglou (Stanford)

26. Gapped BLAST: Example Original BLAST exact keyword search, THEN:
A C G A A G T A A G G T C C A G T
Original BLAST exact keyword search, THEN:
Extend with gaps in a zone around ends of exact match
Output result
GTAAGGTCCAGT
GTTAGGTC-AGT
C T G A T C C T G G A T T G C G A
From lectures by Serafim Batzoglou (Stanford)

27. Gapped BLAST : Example (cont’d)
A C G A A G T A A G G T C C A G T
Original BLAST exact keyword search, THEN:
Extend with gaps around ends of exact match until score <T, then merge nearby alignments
Output result
GTAAGGTCCAGT
GTTAGGTC-AGT
C T G A T C C T G G A T T G C G A
From lectures by Serafim Batzoglou (Stanford)

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

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

30. 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.

31. 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

32. 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

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

34. More BLAST parameters and options to consider:
filtering
gap penalities
matrix

35. Run your BLAST search:
BLAST

36. The BLAST Queue:
click for more info
Note your RID

37. Formatting and Retrieving your BLAST results:
options

38. A graphical view of your BLAST results:

39. The BLAST “hit” list:
Score
E-Value
GenBank
alignment
EntrezGene

40. The BLAST pairwise alignments
Identity
Similarity

41. Sample BLAST output
Blast of human beta globin protein against zebra fish
Score E
Sequences producing significant alignments: (bits) Value
gi| |ref|NP_ | ba1 globin [Danio rerio] >gi| e-44
gi| |ref|NP_ | ba2 globin; SI:dZ118J2.3 [Danio rer e-44
gi| |emb|CAE | SI:bY187G17.6 (novel beta globin) [D e-44
gi| |gb|AAH | Ba1 protein [Danio rerio] e-43
ALIGNMENTS
>gi| |ref|NP_ | ba1 globin [Danio rerio]
Length = 148
Score = 171 bits (434), Expect = 3e-44
Identities = 76/148 (51%), Positives = 106/148 (71%), Gaps = 1/148 (0%)
Query: 1 MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK 60
MV T E++A+ LWGK+N+DE+G +AL R L+VYPWTQR+F +FG+LS+P A+MGNPK
Sbjct: 1 MVEWTDAERTAILGLWGKLNIDEIGPQALSRCLIVYPWTQRYFATFGNLSSPAAIMGNPK 60
Query: 61 VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG 120
V AHG+ V+G DN+K T+A LS +H +KLHVDP+NFRLL A FG
Sbjct: 61 VAAHGRTVMGGLERAIKNMDNVKNTYAALSVMHSEKLHVDPDNFRLLADCITVCAAMKFG 120
Query: 121 KE-FTPPVQAAYQKVVAGVANALAHKYH 147
+ F VQ A+QK +A V +AL +YH
Sbjct: 121 QAGFNADVQEAWQKFLAVVVSALCRQYH 148

42. Sample BLAST output (cont’d)
Blast of human beta globin DNA against human DNA
Score E
Sequences producing significant alignments: (bits) Value
gi| |gb|AF | Homo sapiens gamma A hemoglobin (HBG e-75
gi|183868|gb|M |HUMHBG3E Human gamma-globin mRNA, 3' end e-75
gi| |gb|AY | Homo sapiens A-gamma globin (HBG1) ge e-72
gi|31726|emb|V |HSGGL1 Human messenger RNA for gamma-globin e-66
gi| |ref|NR_ | Homo sapiens hemoglobin, beta pseud e-34
gi| |gb|AF | Homo sapiens haplotype PB26 beta-glob e-33
ALIGNMENTS
>gi| |ref|NG_ | Homo sapiens beta globin region on chromosome 11
Length = 81706
Score = 149 bits (75), Expect = 3e-33
Identities = 183/219 (83%)
Strand = Plus / Plus
Query: ttgggagatgccacaaagcacctggatgatctcaagggcacctttgcccagctgagtgaa 326
|| ||| | || | || | |||||| ||||| ||||||||||| ||||||||
Sbjct: ttcggaaaagctgttatgctcacggatgacctcaaaggcacctttgctacactgagtgac 54468
Query: ctgcactgtgacaagctgcatgtggatcctgagaacttc 365
||||||||| |||||||||| ||||| ||||||||||||
Sbjct: ctgcactgtaacaagctgcacgtggaccctgagaacttc 54507

43. 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.

44. E value
E value (E)
Expectation value. The number of different alignments with scores equivalent to or better than S expected to occur in a database search by chance. The lower the E value, the more significant the score.

45. Assessing sequence homology
Need to know how strong an alignment can be expected from chance alone
“Chance” is the comparison of
Real but non-homologous sequences
Real sequences that are shuffled to preserve compositional properties
Sequences that are generated randomly based upon a DNA or protein sequence model (favored)

46. High Scoring Pairs (HSPs)
All segment pairs whose scores can not be improved by extension or trimming
Need to model a random sequence to analyze how high the score is in relation to chance

47. Expected number of HSPs
Expected number of HSPs with score > S
E-value E for the score S:
E = Kmne-lS
Given:
Two sequences, length n and m
The statistics of HSP scores are characterized by two parameters K and λ
K: scale for the search space size
λ: scale for the scoring system

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

49. Scoring matrices
Amino acid substitution matrices
PAM
BLOSUM

50. Bit Scores Normalized score to be able to compare sequences Bit score
S’ = lS – ln(K) ln(2)
E-value of bit score
E = mn2-S’

51. Assessing the significance of an alignment
How to assess the significance of an alignment between the comparison of a protein of length m to a database containing many different proteins, of varying lengths?
Calculate a "database search" E-value. Multiply the pairwise-comparison E-value by the number of sequences in the database N divided by the length of the sequence in the database n

52. 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
Need to bring class back together with 10 minutes to go in classroom time.

53. Homology: Some Guidelines
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.

54. Contributors

55. Odds score in sequence alignment
The chance of an aligned amino acid pair being found in alignments of related sequences compared to the chance of that pair being found in random alignments of unrelated sequences.

56. Statistical significance of an alignment
The probability that random or unrelated sequences could be aligned to produce the same score.
Smaller the probability is the better.

57. Alignment Statistics:
For two sequences of length n and m, n times m comparisons are being made; thus the longest length of the predicted match would be log1/p(mn).

58. Alignment Statistics:
Expectation value or the mean longest match would be
E(M) = log1/p(Kmn), where K is a constant that depends on amino acid or base composition and p is the probability of a match.
This is only true for ungapped local alignments.

59. Distribution of alignment scores
resembles Gumbel extreme value distribution.

60. Extreme Value Distribution

61. Alignment Statistics
E(M)=log1/p(Kmn) means that match length gets bigger as the log of the product of sequence lengths. Amino acid substitution matrices will turn match lengths into alignment scores (S).
More commonly  = ln(1/p) is used.
Number of longest run HSP will be estimated
E = Kmne-S
How good a sequence score is evaluated based on how many HSPs (i.e. E value) one would expect for that score.

62. Alignment Statistics Two ways to get K and  :
For random amino acid sequences with various gap penalties, K and lambda parameters have been tabulated.
Calculation of the distribution for two sequences being aligned by keeping one of them fixed and scrambling the other, thus preserving both the sequence length and amino acid composition.

63. Alignment Statistics

64. Alignment Statistics

65. Alignment Statistics

66. Alignment Statistics

67. Gene Structure

68. Mutation Rates

69. Functional Constraint

70. Synonymous vs nonsynonymous substitutions

71. Synonymous vs nonsynonymous substitutions

72. Synonymous vs nonsynonymous substitutions

73. Mutation vs substitution

74. Estimating substitutions

75. Jukes-Cantor model

76. Transitions vs transversions

77. Kimura’s 2-parameter model

78. Kimura’s 2-parameter model

79. Kimura’s 2-parameter model

80. Functional Constraints

81. Molecular Clocks

82. Relative Rate

83. Distance based phylogenetics

84. Distance based phylogenetics

85. Distance based phylogenetics

86. Distance based phylogenetics

87. Distance based phylogenetics

88. Distance based phylogenetics

89. Phylogenetics Programs