1. Comparing biological sequences (3): Database searching and Multiple alignment

2. Database searching
Goal: find similar (homologous) sequences of a query sequence in a sequence of database
Input: query sequence & database
Output: hits (pairwise alignments)

3. Database searching Core: pair-wise alignment algorithm
Speed (fast sequence comparison)
Relevance of the search results (statistical tests)
Recovering all information of interest
The results depend of the search parameters like gap penalty, scoring matrix.
Sometimes searches with more than one matrix should be preformed

4. What program to use for searching?
1) BLAST is fastest and easily accessed on the Web
limited sets of databases
nice translation tools (BLASTX, TBLASTN)
2) FASTA
precise choice of databases
more sensitive for DNA-DNA comparisons
FASTX and TFASTX can find similarities in sequences with frameshifts
3) Smith-Waterman is slower, but more sensitive
known as a “rigorous” or “exhaustive” search
SSEARCH in GCG and standalone FASTA

5. FASTA 1) Derived from logic of the dot plot
compute best diagonals from all frames of alignment
2) Word method looks for exact matches between words in query and test sequence
hash tables (fast computer technique)
DNA words are usually 6 bases
protein words are 1 or 2 amino acids
only searches for diagonals in region of word matches = faster searching

6. FASTA Algorithm

7. Makes Longest Diagonal
3) after all diagonals found, tries to join diagonals by adding gaps
4) computes alignments in regions of best diagonals

8. FASTA Alignments

9. FASTA Results - Histogram
!!SEQUENCE_LIST 1.0
(Nucleotide) FASTA of: b2.seq from: 1 to: 693 December 9, :02
TO: /u/browns02/Victor/Search-set/*.seq Sequences: ,050 Symbols:
913,285 Word Size: 6
Searching with both strands of the query.
Scoring matrix: GenRunData:fastadna.cmp
Constant pamfactor used
Gap creation penalty: 16 Gap extension penalty: 4
Histogram Key:
Each histogram symbol represents 4 search set sequences
Each inset symbol represents 1 search set sequences
z-scores computed from opt scores
z-score obs exp
(=) (*)
< : : := :=
:==
:*==
:==*==
:=======*==
:===============*
:==================== *
:==================================*
:==========================================*
:===============================================*====
:===============================================*=====
:=============================================*

10. FASTA Results - List
The best scores are: init1 initn opt z-sc E( )..
SW:PPI1_HUMAN Begin: 1 End: 269
! Q00169 homo sapiens (human). phosph e-117
SW:PPI1_RABIT Begin: 1 End: 269
! P48738 oryctolagus cuniculus (rabbi e-116
SW:PPI1_RAT Begin: 1 End: 270
! P16446 rattus norvegicus (rat). pho e-116
SW:PPI1_MOUSE Begin: 1 End: 270
! P53810 mus musculus (mouse). phosph e-116
SW:PPI2_HUMAN Begin: 1 End: 270
! P48739 homo sapiens (human). phosph e-96
SPTREMBL_NEW:BAC Begin: 1 End: 270
! Bac25830 mus musculus (mouse). 10, e-95
SP_TREMBL:Q8N5W1 Begin: 1 End: 268
! Q8n5w1 homo sapiens (human). simila e-95
SW:PPI2_RAT Begin: 1 End: 269
! P53812 rattus norvegicus (rat). pho e-94

11. FASTA Results - Alignment
SCORES Init1: Initn: Opt: z-score: E(): 2.3e-58
>>GB_IN3:DMU (2038 nt)
initn: 1565 init1: 1515 opt: 1687 Z-score: expect(): 2.3e-58
66.2% identity in 875 nt overlap
(83-957: )
u39412.gb_pr CCCTTTGTGGCCGCCATGGACAATTCCGGGAAGGAAGCGGAGGCGATGGCGCTGTTGGCC
|| ||| | ||||| | ||| |||||
DMU AGGCGGACATAAATCCTCGACATGGGTGACAACGAACAGAAGGCGCTCCAACTGATGGCC
u39412.gb_pr GAGGCGGAGCGCAAAGTGAAGAACTCGCAGTCCTTCTTCTCTGGCCTCTTTGGAGGCTCA
||||||||| || ||| | | || ||| | || || ||||| ||
DMU GAGGCGGAGAAGAAGTTGACCCAGCAGAAGGGCTTTCTGGGATCGCTGTTCGGAGGGTCC
u39412.gb_pr TCCAAAATAGAGGAAGCATGCGAAATCTACGCCAGAGCAGCAAACATGTTCAAAATGGCC
||| | ||||| || ||| |||| | || | |||||||| || ||| ||
DMU AACAAGGTGGAGGACGCCATCGAGTGCTACCAGCGGGCGGGCAACATGTTTAAGATGTCC
u39412.gb_pr AAAAACTGGAGTGCTGCTGGAAACGCGTTCTGCCAGGCTGCACAGCTGCACCTGCAGCTC
|||||||||| ||||| | |||||| |||| ||| || ||| || |
DMU AAAAACTGGACAAAGGCTGGGGAGTGCTTCTGCGAGGCGGCAACTCTACACGCGCGGGCT

12. FASTA on the Web Many websites offer FASTA searches
Various databases and various other services
Be sure to use FASTA 3
Each server has its limits
Be aware that you are depending on the kindness of strangers.

13. Institut de Génétique Humaine, Montpellier France, GeneStream server
Oak Ridge National Laboratory GenQuest server
European Bioinformatics Institute, Cambridge, UK
EMBL, Heidelberg, Germany
Munich Information Center for Protein Sequences (MIPS) at Max-Planck-Institut, Germany
Institute of Biology and Chemistry of Proteins Lyon, France
Institute Pasteur, France
GenQuest at The Johns Hopkins University
National Cancer Center of Japan

14. BLAST Searches GenBank
[BLAST= Basic Local Alignment Search Tool]
The NCBI BLAST web server lets you compare your query sequence to various sections of GenBank:
nr = non-redundant (main sections)
month = new sequences from the past few weeks
ESTs
human, drososphila, yeast, or E.coli genomes
proteins (by automatic translation)
This is a VERY fast and powerful computer. 27

15. BLAST Uses word matching like FASTA
Similarity matching of words (3 aa’s, 11 bases)
does not require identical words.
If no words are similar, then no alignment
won’t find matches for very short sequences
Does not handle gaps well
New “gapped BLAST” (BLAST 2) is better

16. BLAST Algorithm

17. BLAST Word Matching MEA Break query into words: Break database
MEAAVKEEISVEDEAVDKNI
MEA
EAA
AAV
AVK
VKE
KEE
EEI
EIS
ISV
...
Break query into words:
Break database
sequences into words:

18. Compare word lists by Hashing
Query Word List:
MEA
EAA
AAV
AVK
VKL
KEE
EEI
EIS
ISV
Database Sequence Word Lists
RTT AAQ
SDG KSS
SRW LLN
QEL RWY
VKI GKG
DKI NIS
LFC WDV
AAV KVR
PFR DEI
… … ?
Compare word lists by Hashing
(allow near matches)

19. Find locations of matching words in database sequences
ELEPRRPRYRVPDVLVADPPIARLSVSGRDENSVELTMEAT
MEA
EAA
AAV
AVK
KLV
KEE
EEI
EIS
ISV
TDVRWMSETGIIDVFLLLGPSISDVFRQYASLTGTQALPPLFSLGYHQSRWNY
IWLDIEEIHADGKRYFTWDPSRFPQPRTMLERLASKRRVKLVAIVDPH

20. Extend hits one base at a time

21. Then score the alignment.
HVTGRSAF_FSYYGYGCYCGLGTGKGLPVDATDRCCWA
Seq_XYZ:
Query:
QSVFDYIYYGCYCGWGLG_GK__PRDA
E-val=10-13
Use two word matches as anchors to build an alignment between the query and a database sequence.
Then score the alignment.

22. HSPs are Aligned Regions
The results of the word matching and attempts to extend the alignment are segments
- called HSPs (High-scoring Segment Pairs)
BLAST often produces several short HSPs rather than a single aligned region

23. BLAST 2 algorithm
The NCBI’s BLAST website now both use BLAST 2 (also known as “gapped BLAST”)
This algorithm is more complex than the original BLAST
It requires two word matches close to each other on a pair of sequences (i.e. with a gap) before it creates an alignment

24. Statistical tests
Evaluate the probability of an event taking place by chance (at random).
P-value
Randomized data
Distribution under the same setup
Z-score
Chebyshev Inequality

25. BLAST Statistics
E value is equivalent to standard P value (based on Karlin-Altschul theorem)
Significant if E < 0.05 (smaller numbers are more significant)
The E-value represents the likelihood that the observed alignment is due to chance alone. A value of 1 indicates that an alignment this good would happen by chance with any random sequence searched against this database.

26. BLAST variants for different searchesa
(after S. Brenner, Trends Guide to Bioinformatics, 1998)

27. BLAST is Approximate
BLAST makes similarity searches very quickly because it takes shortcuts.
looks for short, nearly identical “words” (11 bases)
It also makes errors
misses some important similarities
makes many incorrect matches
easily fooled by repeats or skewed composition 30

28. Interpretation of output
very low E values (e-100) are homologs or identical genes
moderate E values are related genes
long list of gradually declining of E values indicates a large gene family
long regions of moderate similarity are more significant than short regions of high identity

29. Biological Relevance
It is up to you, the biologist to scrutinize these alignments and determine if they are significant.
Were you looking for a short region of nearly identical sequence or a larger region of general similarity?
Are the mismatches conservative ones?
Are the matching regions important structural components of the genes or just introns and flanking regions?

30. Borderline similarity
What to do with matches with E() values in the range?
this is the “Twilight Zone”
retest these sequences and look for related hits (not just your original query sequence)
similarity is transitive:
if A~B and B~C, then A~C

31. Position Specific Iterated BLAST
Collect all database sequence segments that have been aligned with query sequence with E-value below set threshold (default 0.01)
Construct position specific scoring matrix for collected sequences. Rough idea:
Align all sequences to the query sequence as the template.
Assign weights to the sequences
Construct position specific scoring matrix
Iterate

32. Motif finding
Observation : Some regions have been better conserved than others during evolution
Idea: By analyzing the constant and variable properties of such groups of similar sequences, it is possible to derive a signature for a protein family or domain (motifs)

33. PROSITE patterns Example [EDQH]-x-K-x-[DN]-G-x-R-[GACV] Rules:
PROSITE fingerprints are described by regular grammars
There is a number of programs that allow to search databases for PROSITE patterns (example GCG package)
Example [EDQH]-x-K-x-[DN]-G-x-R-[GACV]
Rules:
Each position is separated by a hyphen
One character denotes residuum at a given position
[…] denoted a set of allowed residues
(n) denotes repeat of n
(n,m) denoted repeat between n and m inclusive
Ex. ATP/GTP binding motive [SG]=X(4)-G-K-[DT]

34. Multiple sequence alignment

35. Generalizing the Notion of Pairwise Alignment
Alignment of 2 sequences is represented as a
2-row matrix
In a similar way, we represent alignment of 3 sequences as a 3-row matrix
A T _ G C G _
A _ C G T _ A
A T C A C _ A
Score: more conserved columns, better alignment

36. Alignments = Paths Align 3 sequences: ATGC, AATC,ATGC A -- T G C A T

37. Alignment Paths 1 2 3 4
x coordinate A -- T G C A T -- C -- A T G C

38. Alignment Paths Align the 3 sequences: ATGC, AATC,ATGC x coordinate1 2 3 4
x coordinate A -- T G C
y coordinate 1 2 3 4 A T -- C -- A T G C

39. Alignment Paths Resulting path in (x,y,z) space:1 2 3 4
x coordinate A -- T G C
y coordinate 1 2 3 4 A T -- C 1 2 3 4
z coordinate -- A T G C
Resulting path in (x,y,z) space:
(0,0,0)(1,1,0)(1,2,1) (2,3,2) (3,3,3) (4,4,4)

40. Aligning Three Sequences
source
Same strategy as aligning two sequences
Use a 3-D “Manhattan Cube”, with each axis representing a sequence to align
For global alignments, go from source to sink
sink

41. 2-D vs 3-D Alignment Grid V W
2-D edit graph
3-D edit graph

42. 2-D cell versus 2-D Alignment Cell
In 2-D, 3 edges in each unit square
In 3-D, 7 edges in each unit cube

43. Architecture of 3-D Alignment Cell
(i-1,j,k-1)
(i-1,j-1,k-1)
(i-1,j-1,k)
(i-1,j,k)
(i,j,k-1)
(i,j-1,k-1)
(i,j,k)
(i,j-1,k)

44. Multiple Alignment: Dynamic Programming
cube diagonal: no indels
si,j,k = max
(x, y, z) is an entry in the 3-D scoring matrix
si-1,j-1,k-1 + (vi, wj, uk)
si-1,j-1,k +  (vi, wj, _ )
si-1,j,k  (vi, _, uk)
si,j-1,k  (_, wj, uk)
si-1,j,k +  (vi, _ , _)
si,j-1,k +  (_, wj, _)
si,j,k  (_, _, uk)
face diagonal: one indel
edge diagonal: two indels

45. Multiple Alignment: Running Time
For 3 sequences of length n, the run time is 7n3; O(n3)
For k sequences, build a k-dimensional Manhattan, with run time (2k-1)(nk); O(2knk)
Conclusion: dynamic programming approach for alignment between two sequences is easily extended to k sequences but it is impractical due to exponential running time.

46. Profile Representation of Multiple Alignment
- A G G C T A T C A C C T G
T A G – C T A C C A G
C A G – C T A C C A G
C A G – C T A T C A C – G G
C A G – C T A T C G C – G G A C G T

47. Profile Representation of Multiple Alignment
- A G G C T A T C A C C T G
T A G – C T A C C A G
C A G – C T A C C A G
C A G – C T A T C A C – G G
C A G – C T A T C G C – G G A C G T
In the past we were aligning a sequence against a sequence
Can we align a sequence against a profile?
Can we align a profile against a profile?

48. Aligning alignments Given two alignments, can we align them?
x GGGCACTGCAT
y GGTTACGTC-- Alignment 1
z GGGAACTGCAG
w GGACGTACC-- Alignment 2
v GGACCT-----

49. Aligning alignments Given two alignments, can we align them?
Hint: use alignment of corresponding profiles
x GGGCACTGCAT
y GGTTACGTC-- Combined Alignment
z GGGAACTGCAG
w GGACGTACC--
v GGACCT-----

50. Multiple Alignment: Greedy Approach
Choose most similar pair of strings and combine into a profile , thereby reducing alignment of k sequences to an alignment of of k-1 sequences/profiles. Repeat
This is a heuristic greedy method
u1= ACg/tTACg/tTACg/cT…
u2 = TTAATTAATTAA… …
uk = CCGGCCGGCCGG…
u1= ACGTACGTACGT…
u2 = TTAATTAATTAA…
u3 = ACTACTACTACT… …
uk = CCGGCCGGCCGG
k-1 k

51. Greedy Approach: Example
Consider these 4 sequences
s1 GATTCA
s2 GTCTGA
s3 GATATT
s4 GTCAGC

52. Greedy Approach: Example (cont’d)
There are = 6 possible alignments
s2 GTCTGA
s4 GTCAGC (score = 2)
s1 GAT-TCA
s2 G-TCTGA (score = 1)
s3 GATAT-T (score = 1)
s1 GATTCA--
s4 G—T-CAGC(score = 0)
s2 G-TCTGA
s3 GATAT-T (score = -1)
s3 GAT-ATT
s4 G-TCAGC (score = -1)

53. Greedy Approach: Example (cont’d)
s2 and s4 are closest; combine:
s2 GTCTGA
s4 GTCAGC
s2,4 GTCt/aGa/cA (profile)
new set of 3 sequences:
s1 GATTCA
s3 GATATT
s2,4 GTCt/aGa/c

54. Progressive Alignment
Progressive alignment is a variation of greedy algorithm with a somewhat more intelligent strategy for choosing the order of alignments.
Progressive alignment works well for close sequences, but deteriorates for distant sequences
Gaps in consensus string are permanent
Use profiles to compare sequences

55. ClustalW Popular multiple alignment tool today
‘W’ stands for ‘weighted’ (different parts of alignment are weighted differently).
Three-step process
1.) Construct pairwise alignments
2.) Build Guide Tree
3.) Progressive Alignment guided by the tree

56. Step 1: Pairwise Alignment
Aligns each sequence again each other giving a similarity matrix
Similarity = exact matches / sequence length (percent identity)
v1 v2 v3 v4
v1 - v v v
(.17 means 17 % identical)

57. Step 2: Guide Tree Create Guide Tree using the similarity matrix
ClustalW uses the neighbor-joining method
Guide tree roughly reflects evolutionary relations

58. Step 2: Guide Tree (cont’d)v1 v3 v4 v2
v1 v2 v3 v4
v1 - v v v
Calculate: v1,3 = alignment (v1, v3) v1,3,4 = alignment((v1,3),v4) v1,2,3,4 = alignment((v1,3,4),v2)

59. Step 3: Progressive Alignment
Start by aligning the two most similar sequences
Following the guide tree, add in the next sequences, aligning to the existing alignment
Insert gaps as necessary
FOS_RAT PEEMSVTS-LDLTGGLPEATTPESEEAFTLPLLNDPEPK-PSLEPVKNISNMELKAEPFD
FOS_MOUSE PEEMSVAS-LDLTGGLPEASTPESEEAFTLPLLNDPEPK-PSLEPVKSISNVELKAEPFD
FOS_CHICK SEELAAATALDLG----APSPAAAEEAFALPLMTEAPPAVPPKEPSG--SGLELKAEPFD
FOSB_MOUSE PGPGPLAEVRDLPG-----STSAKEDGFGWLLPPPPPPP LPFQ
FOSB_HUMAN PGPGPLAEVRDLPG-----SAPAKEDGFSWLLPPPPPPP LPFQ
. . : ** :.. *:.* * . * **:
Dots and stars show how well-conserved a column is.

60. Multiple Alignments: Scoring
Number of matches (multiple longest common subsequence score)
Entropy score
Sum of pairs (SP-Score)

61. Multiple LCS Score
A column is a “match” if all the letters in the column are the same
Only good for very similar sequences
AAA
AAT
ATC

62. Entropy
Define frequencies for the occurrence of each letter in each column of multiple alignment
pA = 1, pT=pG=pC=0 (1st column)
pA = 0.75, pT = 0.25, pG=pC=0 (2nd column)
pA = 0.50, pT = 0.25, pC=0.25 pG=0 (3rd column)
Compute entropy of each column
AAA
AAT
ATC

63. Entropy: Example
Best case
Worst case

64. Multiple Alignment: Entropy Score
Entropy for a multiple alignment is the sum of entropies of its columns:
 over all columns  X=A,T,G,C pX logpX

65. Entropy of an Alignment: Example
column entropy: -( pAlogpA + pClogpC + pGlogpG + pTlogpT) A C G T
Column 1 = -[1*log(1) + 0*log0 + 0*log0 +0*log0] = 0
Column 2 = -[(1/4)*log(1/4) + (3/4)*log(3/4) + 0*log0 + 0*log0] = -[ (1/4)*(-2) + (3/4)*(-.415) ] =
Column 3 = -[(1/4)*log(1/4)+(1/4)*log(1/4)+(1/4)*log(1/4) +(1/4)*log(1/4)] = 4* -[(1/4)*(-2)] = +2.0
Alignment Entropy = =

66. Multiple Alignment Induces Pairwise Alignments
Every multiple alignment induces pairwise alignments
x: AC-GCGG-C
y: AC-GC-GAG
z: GCCGC-GAG
Induces:
x: ACGCGG-C; x: AC-GCGG-C; y: AC-GCGAG
y: ACGC-GAC; z: GCCGC-GAG; z: GCCGCGAG

67. Sum of Pairs Score(SP-Score)
Consider pairwise alignment of sequences
ai and aj
imposed by a multiple alignment of k sequences
Denote the score of this suboptimal (not necessarily optimal) pairwise alignment as
s*(ai, aj)
Sum up the pairwise scores for a multiple alignment:
s(a1,…,ak) = Σi,j s*(ai, aj)

68. Computing SP-Score Aligning 4 sequences: 6 pairwise alignments
Given a1,a2,a3,a4:
s(a1…a4) = s*(ai,aj) = s*(a1,a2) + s*(a1,a3) s*(a1,a4) + s*(a2,a3) s*(a2,a4) + s*(a3,a4)

69. SP-Score: Example a1 ATG-C-AAT . A-G-CATAT ak ATCCCATTT
To calculate each column: s
s*(
Pairs of Sequences A G 1
Score=3 1 -m 1
Score = 1 – 2m A A C G 1 -m
Column 1
Column 3

70. Multiple Alignment: History
1975 Sankoff
Formulated multiple alignment problem and gave dynamic programming solution
1988 Carrillo-Lipman
Branch and Bound approach for MSA
1990 Feng-Doolittle
Progressive alignment
1994 Thompson-Higgins-Gibson-ClustalW
Most popular multiple alignment program
1998 Morgenstern et al.-DIALIGN
Segment-based multiple alignment
2000 Notredame-Higgins-Heringa-T-coffee
Using the library of pairwise alignments
2004 MUSCLE