Multiple Sequence Alignment Alignment can be easy or

Multiple Sequence Alignment

Alignment can be easy or difficult 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 Alignment. Goals • 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 r. RNA • 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 16 S r. RNA can be guided by secondary structure Alignment of 16 S r. RNA sequences from different bacteria

Protein Alignment may be guided by Tertiary Structure Interactions Escherichia coli Djl. A protein Homo sapiens Djl. A protein

Multiple Sequence Alignment. Methods – 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 a simple identity use a scoring matrix

Dotplots in practice • The best tool is an applet* called dotlet 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. . . • *an • www. isrec. isb-sib. ch/java/dotlet/Dotlet. html • www. bip. bham. ac. uk/dotlet/Dotlet. html

Example dotplot - repeated domains in Drosophila melanogaster SLIT protein. • Protein has many repeats • SLIT_DROME (P 24014): 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 p 116 -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 Clustal. W Procedure CLUSTAL W Hbb_Human 1 Hbb_Horse 2. 17 Hba_Human 3. 59. 60 Hba_Horse 4. 59. 13 Myg_Whale 5. 77. 75 Hbb_Human Quick pairwise alignment: calculate distance matrix 2 4 3 Hbb_Horse Hba_Human Neighbor-joining tree (guide tree) 1 Hba_Horse Myg_Whale alpha-helices 1 2 3 4 5 PEEKSAVTALWGKVN--VDEVGG 2 GEEKAAVLALWDKVN--EEEVGG PADKTNVKAAWGKVGAHAGEYGA 1 AADKTNVKAAWSKVGGHAGEYGA EHEWQLVLHVWAKVEADVAGHGQ 3 4 Progressive alignment following guide tree

Clustal. W- Pairwise Alignments • First perform all possible pairwise alignments between each pair of sequences. There are (n-1)+(n-2). . . (n-n+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

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 1 Alignment score: 4 1

Alignment of 3 sequences

Clustal. W- 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 Distance Matrix

First Step PAM distance 3. 3 (Human - Monkey) is the minimum. So we'll join Human and Monkey to Mon. Hum 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, Mon. Hum] = (Dist[Spinach, Monkey] + Dist[Spinach, Human])/2 = (90. 8 + 86. 3)/2 = 88. 55 Mon-Hum Spinach Human Monkey

Next Cycle Mos-(Mon-Hum) Mon-Hum Rice Spinach Mosquito Human Monkey

Penultimate Cycle Mos-(Mon-Hum) Spin-Rice Spinach Mon-Hum Mosquito Human Monkey

Last Joining (Spin-Rice)-(Mos-(Mon-Hum)) Mos-(Mon-Hum) Spin-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.

Clustal. W- 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

Clustal. W- 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. + Clustal. W- 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. +

Clustal. W- 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. gctcgatacgatgactagcta 2. gctcgatacaagacgatgacagcta 3. gctcgatacacgatgactagcta 4. gctcgatacacgatgacgagcga 5. ctcgaacgatgactagct 1. gctcgatacgatgactagcta 2. gctcgatacaagacgatgac-agcta 1 2 3 4 5

Progressive alignment - step 2 1. gctcgatacgatgactagcta 2. gctcgatacaagacgatgacagcta 3. gctcgatacacgatgactagcta 4. gctcgatacacgatgacgagcga 5. ctcgaacgatgactagct 3. gctcgatacacgatgactagcta 4. gctcgatacacgatgacgagcga 1 2 3 4 5

Progressive alignment - step 3 1. gctcgatacgatgactagcta 2. gctcgatacaagacgatgac-agcta + 3. gctcgatacacgatgactagcta 4. gctcgatacacgatgacgagcga 1. gctcgatacgatgactagcta 2. gctcgatacaagacgatgac-agcta 3. gctcgatacacga---tgactagcta 4. gctcgatacacga---tgacgagcga 1 2 3 4 5

Progressive alignment - final step 1. gctcgatacgatgactagcta 2. gctcgatacaagacgatgac-agcta 3. gctcgatacacga---tgactagcta 4. gctcgatacacga---tgacgagcga + 5. ctcgaacgatgactagct 1. gctcgatacgatgactagcta 2. gctcgatacaagacgatgac-agcta 3. gctcgatacacga---tgactagcta 4. gctcgatacacga---tgacgagcga 5. -ctcga-acgatgactagct- 1 2 3 4 5

Clustal. W-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’.

Clustal. W-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.

Clustal. W- 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

Clustal. W- 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 Sequence. A GARFIELD THE LAST FAT CAT Sequence. B GARFIELD THE FAST CAT Sequence. C GARFIELD THE VERY FAST CAT Sequence. D THE FAT Clustal alignment Sequence A GARFIELD THE LAST FA-T CAT Sequence B GARFIELD THE FAST CA-T --Sequence C GARFIELD THE VERY FAST CAT Sequence D ---- THE ---- FA-T CAT

Primary library Seq. A GARFIELD THE LAST FAT CAT Seq. B GARFIELD THE ---- FAST CAT Seq. B GARFIELD THE FAST CAT --- 88 Seq. C GARFIELD THE VERY FAST CAT 100 Seq. A GARFIELD THE LAST FA-T CAT Seq. B GARFIELD THE FAST CAT Seq. C GARFIELD THE VERY FAST CAT 77 Seq. D ---- THE FA-T CAT 100 Seq. A GARFIELD THE LAST FAT CAT Seq. C GARFIELD THE VERY FAST CAT Seq. D ---- THE ---- FAT CAT 100 Seq. D ---- THE ---- FA-T CAT 100

Secondary library Seq. A GARFIELD THE LAST FAT CAT Seq. B GARFIELD THE FAST CAT Weight = 88 Seq. A GARFIELD THE LAST FAT CAT Seq. C GARFIELD THE VERY FAST CAT Seq. B GARFIELD THE FAST CAT Weight = 77 Seq. A GARFIELD THE LAST FAT CAT Seq. D THE FAT CAT Seq. B GARFIELD THE FAST CAT Weight = 100

Extended library Seq. A GARFIELD THE LAST FAT CAT Seq. B GARFIELD THE FAST CAT Dynamic programming Seq. A GARFIELD THE LAST FA-T CAT Seq. B 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. indiana. edu Se. Al for Macintosh, available from: – http: //evolve. zoo. ox. ac. uk/Se-Al. html Bio. Edit for PC, available from: – http: //www. mbio. ncsu. edu/RNase. P/info/programs/BIOEDIT/bioe dit. html