Download Molecular Systematics & Evolution of Microorganisms

Multiple Sequence
Alignment
Alignment can be easy or
difficult
GCGGCCCA
GCGGCCCA
GCGTTCCA
GCGTCCCA
GCGGCGCA
********
TCAGGTAGTT GGTGG
TCAGGTA GTT GGTGG
TCAGCTGGTT GGTGG
TCAGCTAGTT GGTGG
TTAGCTAGTT GGTGA
********** *****
TTGACATG
TTGACATG
TTGACATG
TTGACATG
TTGACATC
********
CCGGGG ---A
CCGGTG --GT
-CTAGG---A
-CTAGGGAAC
-CTCTG---A
?? ????????
AACCG
AAGCC
ACGCG
ACGCG
ACGCG
*****
Easy
Difficult due
to insertions
or deletions
(indels)
Homology: Definition
• Homology: similarity that is the result of inheritance
from a common ancestor - identification and analysis of
homologies is central to phylogenetic systematics.
• An Alignment is an hypothesis of positional homology
between bases/Amino Acids.
Multiple Sequence AlignmentGoals
• To generate a concise, information-rich summary of
sequence data.
• Sometimes used to illustrate the dissimilarity
between a group of sequences.
• Alignments can be treated as models that can be
used to test hypotheses.
• Used to identify homologous residues within
sequences.
Multiple sequence alignments problems
• All sequences show some similarity (even
random sequences).
• Similarity levels might be high in some
parts of the sequence and low in other
parts.
• Sequences might show substantial length
variation and presence/absence of
various domains.
SSU rRNA
• Structural RNA (not translated)
• Found in the small ribosomal subunit.
• Widely-used for phylogeny
reconstruction (found in every species)
• Contains stem and loop structures.
• Stem structures usually conform to
watson-crick base pairing.
Alignment of 16S rRNA can be guided
by secondary structure
<---------------(--------------------HELIX 19---------------------)
<---------------(22222222-000000-111111-00000-111111-0000-22222222
Thermus ruber
UCCGAUGC-UAAAGA-CCGAAG=CUCAA=CUUCGG=GGGU=GCGUUGGA
Th. thermophilus UCCCAUGU-GAAAGA-CCACGG=CUCAA=CCGUGG=GGGA=GCGUGGGA
E.coli
UCAGAUGU-GAAAUC-CCCGGG=CUCAA=CCUGGG=AACU=GCAUCUGA
Ancyst.nidulans UCUGUUGU-CAAAGC-GUGGGG=CUCAA=CCUCAU=ACAG=GCAAUGGA
B.subtilis
UCUGAUGU-GAAAGC-CCCCGG=CUCAA=CCGGGG=AGGG=UCAUUGGA
Chl.aurantiacus UCGGCGCU-GAAAGC-GCCCCG=CUUAA=CGGGGC=GAGG=CGCGCCGA
match
**
***
* ** ** *
**
Alignment of 16S rRNA sequences from different bacteria
Protein Alignment may be guided
by Tertiary Structure Interactions
Escherichia coli
DjlA protein
Homo sapiens
DjlA protein
Multiple Sequence AlignmentMethods
– 3 main methods of
alignment:
• Manual (using custom-built text editors).
• Automatic (using custom-built alignment
software).
• Combined
Manual Alignment - reasons
• Might be carried out because:
– Alignment is easy.
– There is some extraneous information
(structural).
– Automated alignment methods have
encountered the local minimum problem.
– An automated alignment method can be
“improved”.
Local minimum
GARFIELDTHEFAT---CAT
GARFIELDTHEFATFATCAT
Dotplots
• The dotplot provides a way of quickly visualizing the
similarities between all parts of two sequences
simultaneously.
• Lets consider a dotplot between sperm whale and
human myoglobins
Sperm whale myoglobin
GLSDGEWQLV
EKFDKFKHLK
HHEAEIKPLA
GDFGADAQGA
LNVWGKVEAD
SEDEMKASED
QSHATKHKIP
MNKALELFRK
IPGHGQEVLI
LKKHGATVLT
VKYLEFISEC
DMASNYKELG
RLFKGHPETL
ALGGILKKKG
IIQVLQSKHP
FQG
VAGHGQDILI
LKKHGVTVLT
IKYLEFISEA
DIAAKYKELG
RLFKSHPETL
ALGAILKKKG
IIHVLHSRHP
YQG
human myoglobin
VLSEGEWQLV
EKFDRFKHLK
HHEAELKPLA
GDFGADAQGA
LHVWAKVEAD
TEAEMKASED
QSHATKHKIP
MNKALELFRK
Dotplot example sperm whale vs human myg
H
u
m
a
n
m
y
o
g
l
o
b
i
n

Sperm whale myoglobin
G L S D G E W Q L V ...
V
*
L
*
*
S
*
E
*
G *
*
E
*
W
*
Q
*
L
*
*
V *
*
.
.
.
• Put one sequence
on top
• the other on the
side
• where residues are
identical put a dot
• Diagonal lines of
dots show
similarities
•just do the first 10 amino acids of each
•make a table with
–whale sequence on top
–human sequence on the side
Dotplot example sperm whale vs human myg
H
u
m
a
n
m
y
o
g
l
o
b
i
n

Sperm whale myoglobin
G L S D G E W Q L V ...
V
*
L
*
*
S
*
E
*
G *
*
E
*
W
*
Q
*
L
*
*
V *
*
.
.
.
• This is the result for
the whole sequence
• It is easy to see that
the diagonal is a line
of dots.
• So sperm whale and
human myoglobin are
very similar
• But the picture is
noisy can smooth using
a sliding window which
considers neighbouring
residues as well
16
Dotplot example sperm whale vs human myg
• can smooth noise
using a sliding
window which
considers
neighbouring
residues as well
• Have done this here
can see the diagonal
is highly similar
• Also instead of
using using a simple
identity use a
scoring matrix
Dotplots in practice
• The best tool is an applet* called dotlet
• www.isrec.isb-sib.ch/java/dotlet/Dotlet.html
• www.bip.bham.ac.uk/dotlet/Dotlet.html
• *an applet is a program that runs in a
web browser. This means that you can
produce dotplots within a netscape/IE
window.
• Dotplots are often useful to identify
things like repeated domains or
duplications in big proteins...
Example dotplot - repeated domains in Drosophila
melanogaster SLIT protein.
• Protein has many repeats
•
SLIT_DROME (P24014):
MAAPSRTTLMPPPFRLQLRLLILPILLLLRHDAVHAEPYSGGFGSSAVSSGGLGSVGIHIPGGGVGVITEARCPRVCSCT
GLNVDCSHRGLTSVPRKISADVERLELQGNNLTVIYETDFQRLTKLRMLQLTDNQIHTIERNSFQDLVSLERLDISNNVI
TTVGRRVFKGAQSLRSLQLDNNQITCLDEHAFKGLVELEILTLNNNNLTSLPHNIFGGLGRLRALRLSDNPFACDCHLSW
LSRFLRSATRLAPYTRCQSPSQLKGQNVADLHDQEFKCSGLTEHAPMECGAENSCPHPCRCADGIVDCREKSLTSVPVTL
PDDTTDVRLEQNFITELPPKSFSSFRRLRRIDLSNNNISRIAHDALSGLKQLTTLVLYGNKIKDLPSGVFKGLGSLRLLL
LNANEISCIRKDAFRDLHSLSLLSLYDNNIQSLANGTFDAMKSMKTVHLAKNPFICDCNLRWLADYLHKNPIETSGARCE
SPKRMHRRRIESLREEKFKCSWGELRMKLSGECRMDSDCPAMCHCEGTTVDCTGRRLKEIPRDIPLHTTELLLNDNELGR
ISSDGLFGRLPHLVKLELKRNQLTGIEPNAFEGASHIQELQLGENKIKEISNKMFLGLHQLKTLNLYDNQISCVMPGSFE
HLNSLTSLNLASNPFNCNCHLAWFAECVRKKSLNGGAARCGAPSKVRDVQIKDLPHSEFKCSSENSEGCLGDGYCPPSCT
CTGTVVACSRNQLKEIPRGIPAETSELYLESNEIEQIHYERIRHLRSLTRLDLSNNQITILSNYTFANLTKLSTLIISYN
KLQCLQRHALSGLNNLRVVSLHGNRISMLPEGSFEDLKSLTHIALGSNPLYCDCGLKWFSDWIKLDYVEPGIARCAEPEQ
MKDKLILSTPSSSFVCRGRVRNDILAKCNACFEQPCQNQAQCVALPQREYQCLCQPGYHGKHCEFMIDACYGNPCRNNAT
CTVLEEGRFSCQCAPGYTGARCETNIDDCLGEIKCQNNATCIDGVESYKCECQPGFSGEFCDTKIQFCSPEFNPCANGAK
CMDHFTHYSCDCQAGFHGTNCTDNIDDCQNHMCQNGGTCVDGINDYQCRCPDDYTGKYCEGHNMISMMYPQTSPCQNHEC
KHGVCFQPNAQGSDYLCRCHPGYTGKWCEYLTSISFVHNNSFVELEPLRTRPEANVTIVFSSAEQNGILMYDGQDAHLAV
ELFNGRIRVSYDVGNHPVSTMYSFEMVADGKYHAVELLAIKKNFTLRVDRGLARSIINEGSNDYLKLTTPMFLGGLPVDP
AQQAYKNWQIRNLTSFKGCMKEVWINHKLVDFGNAQRQQKITPGCALLEGEQQEEEDDEQDFMDETPHIKEEPVDPCLEN
KCRRGSRCVPNSNARDGYQCKCKHGQRGRYCDQGEGSTEPPTVTAASTCRKEQVREYYTENDCRSRQPLKYAKCVGGCGN
QCCAAKIVRRRKVRMVCSNNRKYIKNLDIVRKCGCTKKCY
• Perform a dotplot of the SLIT protein against itself
www.bio.bham.ac.uk/dotlet/Dotlet.html.
Example dotplot - repeated domains in Drosophila
melanogaster SLIT protein
Swiss-prot entry
For further discussion of dotplot see Attwood and Parry-Smith p116-8
20
Dynamic programming
2 methods:
• Dynamic programming
– Consider 2 protein sequences of 100 amino acids in length.
– If it takes 1002 seconds to exhaustively align these sequences,
then it will take 1003 seconds to align 3 sequences, 1004 to align
4 sequences...etc.
– More time than the universe has existed to align 20 sequences
exhaustively.
• Progressive alignment
Progressive Alignment
• Devised by Feng and Doolittle in 1987.
• Essentially a heuristic method and as such
is not guaranteed to find the ‘optimal’
alignment.
• Requires n-1+n-2+n-3...n-n+1 pairwise
alignments as a starting point
• Most successful implementation is Clustal
(Des Higgins). This software is cited
3,000 times per year in the scientific
literature.
Overview of ClustalW Procedure
Hbb_Human
Hbb_Horse
Hba_Human
Hba_Horse
Myg_Whale
1
2
3
4
5
.17
.59
.59
.77
CLUSTAL W
.60
.59
.77
.13
.75
Hbb_Human
.75
-
2
3
Quick pairwise alignment:
calculate distance matrix
4
Hbb_Horse
Hba_Human
Neighbor-joining tree
(guide tree)
1
Hba_Horse
Myg_Whale
alpha-helices
1
2
3
4
5
PEEKSAVTALWGKVN--VDEVGG
GEEKAAVLALWDKVN--EEEVGG
PADKTNVKAAWGKVGAHAGEYGA
AADKTNVKAAWSKVGGHAGEYGA
EHEWQLVLHVWAKVEADVAGHGQ
2
1
3
4
Progressive alignment
following guide tree
ClustalW- Pairwise Alignments
• First perform all possible pairwise
alignments between each pair of
sequences. There are (n-1)+(n-2)...(nn+1) possibilities.
• Calculate the ‘distance’ between each pair
of sequences based on these isolated
pairwise alignments.
• Generate a distance matrix.
Path Graph for aligning two
sequences.
Possible alignment
Scoring Scheme:
•Match:
+1
•Mismatch: 0
•Indel:
-1
1
1
0
1
Score for this path= 2
0
-1
Alignment using this path
1
GATTCGAATTC
1
0
1
0
-1
Optimal Alignment 1
Alignment using
this path
1
1
GA-TTC
GAATTC
-1
1
1
Alignment score: 4
1
Optimal Alignment 2
Alignment using
this path
1
-1
G-ATTC
GAATTC
1
1
Alignment score: 4
1
1
Alignment of 3 sequences
ClustalW- Guide Tree
• Generate a Neighbor-Joining ‘guide
tree’ from these pairwise distances.
• This guide tree gives the order in
which the progressive alignment will
be carried out.
Neighbor joining method
•The neighbor joining method is a greedy heuristic which
joins at each step, the two closest sub-trees that are not
already joined.
•It is based on the minimum evolution principle.
•One of the important concepts in the NJ method is
neighbors, which are defined as two taxa that are
connected by a single node in an unrooted tree
Node 1
A
B
What is required for the Neighbour joining method?
Distance matrix
PAM
Spinach
Rice
Mosquito
Monkey
Human
Spinach
0.0
84.9
105.6
90.8
86.3
Distance Matrix
Rice
84.9
0.0
117.8
122.4
122.6
Mosquito
105.6
117.8
0.0
84.7
80.8
Monkey
90.8
122.4
84.7
0.0
3.3
Human
86.3
122.6
80.8
3.3
0.0
First Step
PAM distance 3.3 (Human - Monkey) is the minimum. So we'll
join Human and Monkey to MonHum and we'll calculate the new
distances.
Mon-Hum
Mosquito
Spinach
Rice Human
Monkey
Calculation of New Distances
After we have joined two species in a subtree we have to compute the
distances from every other node to the new subtree. We do this with a
simple average of distances:
Dist[Spinach, MonHum]
= (Dist[Spinach, Monkey] + Dist[Spinach, Human])/2
= (90.8 + 86.3)/2 = 88.55
Mon-Hum
Spinach
Human
Monkey
Next Cycle
PAM
Spinach
Rice
Mosquito
MonHum
Spinach
0.0
84.9
105.6
88.6
Rice
84.9
0.0
117.8
122.5
Mosquito
105.6
117.8
0.0
82.8
MonHum
88.6
122.5
82.8
0.0
Mos-(Mon-Hum)
Mon-Hum
Rice
Spinach
Mosquito
Human
Monkey
Penultimate Cycle
PAM
Spinach
Rice
MosMonHum
Spinach
0.0
84.9
97.1
Rice
84.9
0.0
120.2
MosMonHum
97.1
120.2
0.0
Mos-(Mon-Hum)
Spin-Rice
Rice
Spinach
Mon-Hum
Mosquito
Human
Monkey
Last Joining
PAM
Spinach
MosMonHu m
SpinRice
0.0
108.7
MosMonHu m
108.7
0.0
(Spin-Rice)-(Mos-(Mon-Hum))
Mos-(Mon-Hum)
Spin-Rice
Rice
Mon-Hum
Spinach
Mosquito
Human
Monkey
Unrooted Neighbor-Joining
Tree
Human
Spinach
Monkey
Rice
Mosquito
Multiple Alignment- First pair
• Align the two most closely-related
sequences first.
• This alignment is then ‘fixed’ and
will never change. If a gap is to be
introduced subsequently, then it will
be introduced in the same place in
both sequences, but their relative
alignment remains unchanged.
ClustalW- Decision time
• Consult the guide tree to see what alignment is performed next.
– Align a third sequence to the first two
Or
– Align two entirely different sequences to each other.
Option 1
Option 2
ClustalW- Alternative 1
If the situation arises where a third
sequence is aligned to the first two,
then when a gap has to be introduced to
improve the alignment, each of these
two entities are treated as two single
sequences.
+
ClustalW- Alternative 2
• If, on the other hand, two separate
sequences have to be aligned together, then
the first pairwise alignment is placed to one
side and the pairwise alignment of the other
two is carried out.
+
ClustalW- Progression
• The alignment is progressively built
up in this way, with each step being
treated as a pairwise alignment,
sometimes with each member of a
‘pair’ having more than one sequence.
Progressive alignment - step 1
1. gctcgatacgatacgatgactagcta
2. gctcgatacaagacgatgacagcta
3. gctcgatacacgatgactagcta
4. gctcgatacacgatgacgagcga
5. ctcgaacgatacgatgactagct
1. gctcgatacgatacgatgactagcta
2. gctcgatacaagacgatgac-agcta
1
2
3
4
5
Progressive alignment - step 2
1.
2.
3.
4.
5.
gctcgatacgatacgatgactagcta
gctcgatacaagacgatgacagcta
gctcgatacacgatgactagcta
gctcgatacacgatgacgagcga
ctcgaacgatacgatgactagct
3. gctcgatacacgatgactagcta
4. gctcgatacacgatgacgagcga
1
2
3
4
5
Progressive alignment - step 3
1.
2.
+
3.
4.
gctcgatacgatacgatgactagcta
gctcgatacaagacgatgac-agcta
gctcgatacacgatgactagcta
gctcgatacacgatgacgagcga
1.
2.
3.
4.
gctcgatacgatacgatgactagcta
gctcgatacaagacgatgac-agcta
gctcgatacacga---tgactagcta
gctcgatacacga---tgacgagcga
1
2
3
4
5
Progressive alignment - final step
1.
2.
3.
4.
+
5.
gctcgatacgatacgatgactagcta
gctcgatacaagacgatgac-agcta
gctcgatacacga---tgactagcta
gctcgatacacga---tgacgagcga
1.
2.
3.
4.
5.
gctcgatacgatacgatgactagcta
gctcgatacaagacgatgac-agcta
gctcgatacacga---tgactagcta
gctcgatacacga---tgacgagcga
-ctcga-acgatacgatgactagct-
ctcgaacgatacgatgactagct
1
2
3
4
5
ClustalW-Good points/Bad
points
• Advantages:
– Speed.
• Disadvantages:
– No objective function.
– No way of quantifying whether or not
the alignment is good
– No way of knowing if the alignment is
‘correct’.
ClustalW-Local Minimum
• Potential problems:
– Local minimum problem. If an error
is introduced early in the alignment
process, it is impossible to correct
this later in the procedure.
– Arbitrary alignment.
Increasing the sophistication of
the alignment process.
• Should we treat all the sequences in the same
way? - even though some sequences are closelyrelated and some sequences are distant relatives.
• Should we treat all positions in the sequences as
though they were the same? - even though they
might have different functions and different
locations in the 3-dimensional structure.
ClustalW- Caveats
• Sequence weighting
• Varying substitution matrices
• Residue-specific gap penalties and reduced penalties
in hydrophilic regions (external regions of protein
sequences), encourage gaps in loops rather than in
core regions.
• Positions in early alignments where gaps have been
opened receive locally reduced gap penalties to
encourage openings in subsequent alignments
ClustalW- User-supplied values
• Two penalties are set by the user (there are
default values, but you should know that it is
possible to change these).
• GOP- Gap Opening Penalty is the cost of opening
a gap in an alignment.
• GEP- Gap Extension Penalty is the cost of
extending this gap.
Position-Specific gap penalties
• Before any pair of (groups of) sequences are
aligned, a table of GOPs are generated for each
position in the two (sets of) sequences.
• The GOP is manipulated in a position-specific
manner, so that it can vary over the sequences.
• If there is a gap at a position, the GOP and GEP
penalties are lowered, the other rules do not apply.
• This makes gaps more likely at positions where gaps
already exist.
Discouraging too many gaps
• If there is no gap opened, then the GOP is increased if the
position is within 8 residues of an existing gap.
• This discourages gaps that are too close together.
• At any position within a run of hydrophilic residues, the GOP is
decreased.
• These runs usually indicate loop regions in protein structures.
• A run of 5 hydrophilic residues is considered to be a hydrophilic
stretch.
• The default hydrophilic residues are:
– D, E, G, K, N, Q, P, R, S
– But this can be changed by the user.
Divergent Sequences
• The most divergent sequences (most different, on average
from all of the other sequences) are usually the most
difficult to align.
• It is sometimes better to delay their aligment until later
(when the easier sequences have already been aligned).
• The user has the choice of setting a cutoff (default is 40%
identity).
• This will delay the alignment until the others have been
aligned.
T-COFFEE
Tree-based consistency objective function for alignment evaluation)
• Generate a library of all the pairwise
alignments between the sequences.
• This gives positional information concerning
which residues are homologous to which other
residues.
• This can then be used to guide progressive
alignments.
An example dataset
SequenceA
SequenceB
SequenceC
SequenceD
GARFIELD THE LAST FAT CAT
GARFIELD THE FAST CAT
GARFIELD THE VERY FAST CAT
THE FAT CAT
Clustal alignment
Sequence
Sequence
Sequence
Sequence
A
B
C
D
GARFIELD
GARFIELD
GARFIELD
--------
THE
THE
THE
THE
LAST
FAST
VERY
----
FA-T
CA-T
FAST
FA-T
CAT
--CAT
CAT
Primary library
SeqA GARFIELD THE LAST FAT CAT
SeqB GARFIELD THE FAST CAT --- 88
SeqB GARFIELD THE ---- FAST CAT
SeqC GARFIELD THE VERY FAST CAT
100
SeqA GARFIELD THE LAST FA-T CAT
SeqC GARFIELD THE VERY FAST CAT 77
SeqB GARFIELD THE FAST CAT
SeqD -------- THE FA-T CAT
SeqA GARFIELD THE LAST FAT CAT
SeqD -------- THE ---- FAT CAT 100
SeqC GARFIELD THE VERY FAST CAT
SeqD -------- THE ---- FA-T CAT 100
100
Secondary library
SeqA GARFIELD THE LAST FAT CAT
SeqB GARFIELD THE FAST CAT
Weight = 88
SeqA GARFIELD THE LAST FAT CAT
SeqC GARFIELD THE VERY FAST CAT
SeqB GARFIELD THE
FAST CAT
Weight = 77
SeqA GARFIELD THE LAST FAT CAT
SeqD
THE
FAT CAT
SeqB GARFIELD THE
FAST CAT
Weight = 100
Extended library
SeqA GARFIELD THE LAST FAT CAT
SeqB GARFIELD THE
FAST CAT
Dynamic programming
SeqA GARFIELD THE LAST FA-T CAT
SeqB GARFIELD THE ---- FAST CAT
Advice on progressive alignment
• Progressive alignment is a mathematical process that is completely
independent of biological reality.
• Can be a very good estimate
• Can be an impossibly poor estimate.
• Requires user input and skill.
• Treat cautiously
• Can be improved by eye (usually)
• Often helps to have colour-coding.
• Depending on the use, the user should be able to make a judgement
on those regions that are reliable or not.
• For phylogeny reconstruction, only use those positions whose
hypothesis of positional homology is unimpeachable
Alignment of protein-coding
DNA sequences
• It is not very sensible to align the DNA
sequences of protein-coding genes.
ATGCTGTTAGGG
ATGACTCTGTTAGGG
ATG-CT--GTTAGGG
ATGACTCTGTTAGGG
The result might be highly-implausible and might not reflect what is
known about biological processes.
It is much more sensible to translate the sequences to their
corresponding amino acid sequences, align these protein sequences
and then put the gaps in the DNA sequences according to where
they are found in the amino acid alignment.
Manual Alignment- software
GDE- The Genetic Data Environment (UNIX)
CINEMA- Java applet available from:
– http://www.biochem.ucl.ac.uk
Seqapp/Seqpup- Mac/PC/UNIX available from:
– http://iubio.bio.indiana.edu
SeAl for Macintosh, available from:
– http://evolve.zoo.ox.ac.uk/Se-Al/Se-Al.html
BioEdit for PC, available from:
– http://www.mbio.ncsu.edu/RNaseP/info/programs/BIOEDIT/bio
edit.html