Multiple Sequence Alignment

Definition

• Homology: related by descent

• Homologous sequence positions

ATTGCGC ATTGCGC

ATCCGCC

ATTGCGC AT-CCGC

ATTGCGC

Reasons for aligning sets of sequences

• Organise data to reflect sequence homology

• Infer phylogenetic trees from homologous sites

• Highlight conserved sites/regions

• Highlight variable sites/regions

• Uncover changes in gene structure

• Summarise information

Alignments help to

Organise

Visualise

Analyze

Sequence Data

The process of aligning sequences is a game involving playing off gaps and mismatches

Ways of aligning multiple sequences

• By hand

• Automated

• Combination

Optimality criteria: some kind rule or scoring scheme to help you to decide what you consider to be the best alignment

Definition

Pairwise vs Multiple Sequences

• Pairs of sequences typically aligned using exhaustive algorithms (dynamic programming)– complexity of exhaustive methods is O(2n mn)

n = number of sequences

• Multiple sequence alignment using heuristic methods

ATTGCGC

The Correct Alignment

ATTGCGC

ATCCGCC

ATTGCGC AT-CCGC

ATTGCGC ATC-CGC

ATTGCGC

The Correct Alignment

Correct according to optimality criteria

Correct according to homology

Exhaustive methods

Always Not always

Heuristic methods

Not always Not always

• Sequence alignment is easy with sufficiently closely related sequences

• Below a certain level of identity sequence alignment may become meaningless – twilight zone for aa sequences ~ 30%

• In the twilight zone it is good to make use of additional information if possible (e.g. structure)

Consensus Sequences

• Simplest Form:A single sequence which represents the most common amino acid/base in that position

Y D D G A V - E A L

Y D G G - - - E A L

F E G G I L V E A L

F D - G I L V Q A V

Y E G G A V V Q A L

Y D G G A/I V/L V E A L

Multiple Alignment Formats

e.g. Clustal, Phylip, MSF, MEGA etc. etc.

Clustal Format

CLUSTAL X (1.81) multiple sequence alignment

CAS1_BOVIN MKLLILTCLVAVALARPKHPIKHQGLPQ--------EVLNEN-CAS1_SHEEP MKLLILTCLVAVALARPKHPIKHQGLSP--------EVLNEN-CAS1_PIG MKLLIFICLAAVALARPKPPLRHQEHLQNEPDSRE--------CAS1_HUMAN MRLLILTCLVAVALARPKLPLRYPERLQNPSESSE--------CAS1_RABBIT MKLLILTCLVATALARHKFHLGHLKLTQEQPESSEQEILKERKCAS1_MOUSE MKLLILTCLVAAAFAMPRLHSRNAVSSQTQ------QQHSSSECAS1_RAT MKLLILTCLVAAALALPRAHRRNAVSSQTQ------------- *:***: **.*.*:* : . :

7 100SOMA_BOVIN MMAAGPRTSL LLAFALLCLP WTQVVGAFPA MSLSGLFANA VLRAQHLHQL SOMA_SHEEP MMAAGPRTSL LLAFTLLCLP WTQVVGAFPA MSLSGLFANA VLRAQHLHQL SOMA_RAT_P -MAADSQTPW LLTFSLLCLL WPQEAGAFPA MPLSSLFANA VLRAQHLHQL SOMA_MOUSE -MATDSRTSW LLTVSLLCLL WPQEASAFPA MPLSSLFSNA VLRAQHLHQL SOMA_RABIT -MAAGSWTAG LLAFALLCLP WPQEASAFPA MPLSSLFANA VLRAQHLHQL SOMA_PIG_P -MAAGPRTSA LLAFALLCLP WTREVGAFPA MPLSSLFANA VLRAQHLHQL SOMA_HUMAN -MATGSRTSL LLAFGLLCLP WLQEGSAFPT IPLSRLFDNA MLRAHRLHQL

AADTFKEFER TYIPEGQRYS -IQNTQVAFC FSETIPAPTG KNEAQQKSDL AADTFKEFER TYIPEGQRYS -IQNTQVAFC FSETIPAPTG KNEAQQKSDL AADTYKEFER AYIPEGQRYS -IQNAQAAFC FSETIPAPTG KEEAQQRTDM AADTYKEFER AYIPEGQRYS -IQNAQAAFC FSETIPAPTG KEEAQQRTDM AADTYKEFER AYIPEGQRYS -IQNAQAAFC FSETIPAPTG KDEAQQRSDM AADTYKEFER AYIPEGQRYS -IQNAQAAFC FSETIPAPTG KDEAQQRSDV AFDTYQEFEE AYIPKEQKYS FLQNPQTSLC FSESIPTPSN REETQQKSNL

Phylip Format (Interleaved)

Phylip Format (Sequential)

3 100Rat ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGTTAATGGCCGTGGTGGCTGGAGTGGCCAGTGCCCTGGCTCACAAGTACCACTAAMouse ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGTCTCTTGCCTTGGGGAAAGGTGAACTCCGATGAAGTTGGTGGTGAGGCCCTGGGRabbit ATGGTGCATCTGTCCAGT---GAGGAGAAGTCTGCGGTCACTGCTGGGGCAAGGTGAATGTGGAAGAAGTTGGTGGTGAGGCCCTGGG

#megaTITLE: No title

#Rat ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGT#Mouse ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGT#Rabbit ATGGTGCATCTGTCCAGT---GAGGAGAAGTCTGC#Human ATGGTGCACCTGACTCCT---GAGGAGAAGTCTGC#Oppossum ATGGTGCACTTGACTTTT---GAGGAGAAGAACTG#Chicken ATGGTGCACTGGACTGCT---GAGGAGAAGCAGCT#Frog ---ATGGGTTTGACAGCACATGATCGT---CAGCT

Mega Format

Progressive Multiple Alignment

• Heuristic

• Perform pairwise alignments

• Align sequences to alignments or alignments to existing alignments (profile alignments

• Do the alignments in some sensible order

Iterative methods

• Several progressive alignment methods can be iterated– e.g. Barton-Sternberg, ClustalX

ClustalX Algorithm

• Perform alignments and calculate distances for all pairs of sequences

• Construct guide tree (dendrogram) joining the most similar sequences using Neighbour Joining

• Align sequences, starting at the leaves of the guide tree. This involves the pair-wise comparisons as well as comparison of single sequence with a group of seqs (Profile)

• ClustalX is not optimal

• There are known areas in which ClustalX performs badly e.g. – errors introduced early cannot be corrected by

subsequent information– alignments of sequences of differing lengths

cause strange guide trees and unpredictable effects

– edges: ClustalX does not penalise gaps at edges

• There are alternatives to ClustalX available

Using ClustalX

• Start with sequences in FASTA format (or an existing alignment in Clustal format

• [Do Alignment] on the alignment menu

ClustalX Parameters

• Scoring Matrix

• Gap opening penalty

• Gap extension penalty

• Protein gap parameters

• Additional algorithm parameters

• Secondary structure penalties

Score Matrices

• Pairwise matrices and multiple alignment matrix series

• PAM (Dayhoff), BLOSUM (Hennikof), GONNET (default), user defined

• Transition (A<->G)/Transversion (C<-T) ratio – low for distantly related sequences

Gap Penalties

• Linear gap penalties – Affine gap penaltiesp = (o + l.e)

• Gap opening • Gap extension• Protein specific penalties (on by default)

– Increase the probability of gaps associated with certain residues

– Increase the chances of gaps in loop regions (> 5 hydrophilic residues)

Algorithm parameters

• Slow-accurate pair-wise alignment

• Do alignment from guide tree

• Reset gaps before aligning (iteration)

• Delay Divergent sequences (%)

Additional displays

• Column Scores

• Low quality regions

• Exceptional residues

Multiple Alignment Strategies

• Align pairs of sequences using an optimal method• Choose representative sequences to align carefully• Choose sequences of comparable lengths• Progressive alignment programs such as ClustalX

for multiple alignment• Progressive alignment programs may be combined• Review alignment by eye and edit

Alignment of coding regions

• Nucleotide sequences much harder to align accurately than proteins

• Protein coding sequences can be aligned using the protein sequences

Multiple Alignments and Phylogenetic Trees

– You can make a more accurate multiple sequence alignment if you know the tree already

– A good multiple sequence alignment is an important starting point for drawing a tree

– The process of constructing a multiple alignment (unlike pair-wise) needs to take account of phylogenetic relationships

Editing a multiple sequence alignment

• It is NOT fraud to edit a multiple sequence alignment

• Incorporate additional knowledge if possible

• Alignment edititors help to keep the data organised and help to prevent unwanted mistakes

Alignment Editors

• e.g. GDE, Bioedit, Seaview, Jalview etc.

• Alignment editors can function as an organisational tool (analyses tools on BioEdit)

• Construct sub-sequences (GDE, Seaview)

• Annotate sequences (Seaview)

Aligning weakly similar sequences

Sequence contains conserved regions

• e.g. DIALIGN (Morgenstern, Dress, Werner) – re-aligns regions between conserved blocks

http://bibiserv.techfak.uni-bielefeld.de/

useful if sequences contains consistent conserved blocks

• Block Maker – searches for conserved words that may be inconsistent http://blocks.fhcrc.org/

Profile AlignmentGribskov et al. 1987

• Position specific scores• Allows alignment of alignments• Gaps introduced as whole columns in the separate

alignments• Optimal alignment in time O(a2l2)a = alphabet size, l = sequence length• Information about the degree of conservation of

sequence positions is included

Good reasons to use profile alignments

– Adding a new sequence to an existing multiple alignment that you want to keep the same(align sequence to profile)

– Searching a database for new members of your protein family(pfsearch)

– Searching a database of profiles to find out which one your sequence belongs to(pfscan)

– Combining two multiple sequence alignments(profile to profile)

Profile Alignment Using ClustalX

• Profile Alignment Mode

• Align sequence to profile

• Align profile 1 to profile 2

• Secondary structure parameters

Profile searching using PSI-BLAST

• Position Specific Iterative

• Perform search – construct profile – perform search

• Convergence (hopefully…)

• Increased sensitivity for distantly related sequences

• Available on-line (NCBI)

Databases of Aligned Sequences• Hovergen http://pbil.univ-lyon1.fr/databases/

hovergen.html (vertebrate alignments)• Pfam http://www.sanger.ac.uk/Software/Pfam/

(protein domain alignments and profile HMMs)• BLOCKS http://blocks.fhcrc.org/• Ribosomal Database Project http://rdp.cme.msu.edu

/html/ alignments and trees derived from rRNA sequences

• Interpro – combines information from other sources• Many more…

Probabilistic Models of Sequence Alignment

• Hidden Markov Models– sequence of states and associated symbol probabilities

• Produces a probabilistic model of a sequence alignment

• Align a sequence to a Profile Hidden Markov Model– Algorithms exist to find the most efficient pathway

through the model

Markov Chain: A chain of things. The probability of the next thing depends only on the current thing

Hidden Markov Model: A sequence of states which form a Markov Chain. The states are not observable. The observable characters have “emission” probabilities which depend on the current state.