Multiple sequence alignment(MSA)

Usean sekvenssin rinnastus

Petri TörönenHelp contributed by: Liisa Holm & Ari Löytynoja

What is MSA?• MSA is an alignment generated from three

or more sequences.

• MSA is usually a more global alignment, i.e., the aim is to align homologous residues (nucleotides or amino acids) in columns across the length of the whole sequences.

GA--GTACA

CAC-GTATA

CACGGTAT-

G-CGGTCTA

What is MSA?

Picture shows protein multiple sequence alignmenthttp://en.wikipedia.org/wiki/Multiple_sequence_alignment

Why MSA• ”MSA emphasises signal observed in the

pairwise alignment” (Liisa Holm)

• Improved alignments!!

• Alignment of more distant sequences with the help from intermediate sequences

• Highlight the conserved regions in sequences

http://ekhidna.biocenter.helsinki.fi/users/petri/public/opetus_jutut/Bioinf_Per_Lects/urease_output.txt

Why MSAMSA is input to many analysis tasks:

•Detection of active site

•Generation sequence profiles

•Detection of protein domains and motifs

•Phylogenetics

…

Remember• First step of MSA:

• Good selection of sequences to the analysis

• Sequences need to be functionally/evolutionarily related

• Sometimes it is good to have some variation in the sequences (depends on the analysis task)

• Alternative: Rubbish in → Rubbish out

MSA methods

• Finding optimal multiple sequence alignment is computationally hard task

• “Correct” answer would always come by extending dynamic algorithm to multiple sequences

• In practice dynamic algorithm cannot be applied to MSA problems

• We need approximate solutions (heuristics)

http://en.wikipedia.org/wiki/Multiple_sequence_alignment#Dynamic_programming_and_computational_complexity

MSA methods: heuristics

• Progressive Alignment (not much used)

• Iterative Alignment (most popular)

• Hidden Markov Models

• Pattern Based methods

Progressive alignment

• Divide unsolvable task into subtasks that can be solved

• Align first most similar pairs of sets of sequences– Sequence sets can have 1 or many sequences– First the sets include only single sequences

• Move progressively to more bigger sets and to more difficult pairs of sets

• Always align only two pairs of sets at the time

Progressive alignment

• Produce pairwise alignments between all the sequences you want to align with MSA.– Dynamic programming, ktup-methods..

• Produce a “guide tree” on the basis of the pairwise distances calculated from pairwise alignments– UPGMA, neighbor joining

• Produce an MSA using the “guide tree”.– Sequences are aligned in the same order as the

guide tree instructs.

Set of sequences All against all pairwise alignment Here demonstrated for 1. sequence

Get pairwise similarities from alignmentsCreate a cluster tree from similarities Join sequences in the order obtained

From the cluster tree

Guide tree construction: UPGMA

• Unweighted Pair Group Method with Arithmetic mean

• One of the fastest tree construction methods

An example: Pairwise alignments

Pairwise distances, based on pairwise alignments

Number of nucleotide differences

Absolute distances, used in Pileup/

Clustal

JC-distance

UPGMA based on JC-distances*

0,107 / 2

JC-distances = Jukes-Cantor distances. The observed distances, D, are corrected for multiple substitutions via correction function –(3/4)*ln(1-(4/3)D)

UPGMA, distance updatesd(human,chimp),gorilla = [d(human, gorilla) + d(chimp, gorilla)] / 2 =

[0,383 + 0,232] / 2 = 0,3075

UPGMA

UPGMA

UPGMA

U

d(human & chimp),U =

0,3923/2 = 0,1962

d(gorilla & orangutan),U

= 0,3923/2 = 0,1962

0,1962 - 0,0537 = 0,1426

0,1962 - 0,116 = 0,080

UPGMA

0.7083 / 2

0,3541 - 0,1426 - 0,0537

0,3541 - 0,080 - 0,116or

Constructing MSA

human ACGTACGTCCchimp ACCTACGTCCgorilla ACCACCGTCCorangutan ACCCCCCTCCmaqaque CCCCCCCCCC

human ACGTACGTCCchimp ACCTACGTCC

gorilla ACCACCGTCCorangutan ACCCCCCTCC

human ACGTACGTCC

chimp ACCTACGTCC

gorilla ACCACCGTCC

orangutan ACCCCCCTCC

Alignment score• 1234• ACGT match=1• ACGA mismatch=0• AGGA

• 1: A-A + A-A + A-A = 1+1+1 = 3

• 2: C-C + C-G + C-G =1+0+0 = 1

• 3: G-G + G-G + G-G = 1+1+1 = 3

• 4: T-A + T-A + A-A = 0+0+1 =1

• S(alignment) = S(1) + S(2) + S(3) + S(4) = 3+1+3+1 = 8

• The higher the score, the better the alignment

Progressive alignment - pros and cons

• Pros:– Fast

• Cons:– Once gaps are opened they can never be closed– Errors in the alignment of the first few

sequences can have catastrophic effects on the whole alignment

– Not much used (to my knowledge)

Iterative alignment

• Create a progressive alignment

• After obtaining the alignment calculate a quality score

• REPEAT THE FOLLOWING STEPS:– Redo the cluster tree– Realign the sequences using the new cluster

tree– Calculate a quality score

• Loop above can be stopped when a maximum number is reached or when quality score is not improved

Iterative alignment

• Allows correction of errors that was not possible in progressive alignment

• Very popular among the MSA methods

• Increases the running time of the method

Diagram of typical iterative MSA program workflow. Figure from Do & Katoh 2008 http://ai.stanford.edu/~chuongdo/papers/alignment_review.pdf

Iterative alignment

Iteration loop

What MSA program(s) to use?• Depends on the application

– Phylogenetic studies– Structure based studies

• Depends on the size of the data– Some programs cannot handle large dataset

• Remember to evaluate the alignment by eye

What MSA program(s) to use?

• Collection of MSA programs at EBI

• http://www.ebi.ac.uk/Tools/msa/

Summary of MSA

• MSA is relevant for many analysis tasks– Improved signal from the alignment

• Solving MSA requires heuristics

• Selection of MSA methods depends on the application

• Results should be evaluated by eye– And the errors should be corrected with MSA

editors

Manual editing of MSAs?

• Let’s say that your performed an MSA witn computer. However, biologically, it has some faults - needs manual editing ->

• Editors: Jalview and Seaview http://www.csc.fi/english/research/sciences/bioscience/programs/index_html

• Input data can be in any of the most common MSA formats (Mase, Phylip, Clustal, MSF, Fasta, NEXUS, PIR and BCL)