Biological definitions for Biological definitions for rrelated sequenceselated sequences

• Homologues are similar sequences in two different organisms that have been derived from a common ancestor sequence. Homologues can be described as either orthologues or paralogues.

• Orthologues are similar sequences in two different organisms that have arisen due to a speciation event. Orthologs typically retain identical or similar functionality throughout evolution.

• Paralogues are similar sequences within a single organism that have arisen due to a gene duplication event.

• Xenologues are similar sequences that do not share the same evolutionary origin, but rather have arisen out of horizontal transfer events through symbiosis, viruses, etc.

So this means …So this means …

 

Source: http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/Orthology.html

Multiple sequence alignmentMultiple sequence alignment

Sequences can be mutated or rearranged to perform an altered function.

• which changes in the sequence have caused a change in the functionality.

Multiple sequence alignment: the idea is to take three or more sequences and align them so that the greatest number of similar characters are aligned in the same column of the alignment.

• hold information about which regions have high mutation rates over evolutionary time and which are evolutionarily conserved • identification of regions or domains that are critical to functionality.

Sequences can be conserved across species and perform similar or identical functions.

What to ask yourselfWhat to ask yourself

How do we get a multiple alignment? (three or more sequences)

What is our main aim?– Do we go for max accuracy, least

computational time or the best compromise? What do we want to achieve each time?

sequence

sequence

SequenceSequence-sequence alignment-sequence alignment

Multiple alignment methodsMultiple alignment methods

Multi-dimensional dynamic programmingMulti-dimensional dynamic programming Progressive alignmentProgressive alignment Iterative alignment Iterative alignment

Simultaneous multiple alignmentSimultaneous multiple alignmentMulti-dimensional dynamic programmingMulti-dimensional dynamic programming

The combinatorial explosion:The combinatorial explosion: 2 sequences of length n 2 sequences of length n

– nn22 comparisons comparisons

Comparison number increases exponentiallyComparison number increases exponentially– i.e. ni.e. nNN where n is the length of the sequences, and N is where n is the length of the sequences, and N is

the number of sequencesthe number of sequences

Impractical for even a small number of short Impractical for even a small number of short sequences quite quicklysequences quite quickly

Multi-dimensional dynamic Multi-dimensional dynamic programmingprogramming (Murata et al, 1985)(Murata et al, 1985)

Sequence 1

Seq

uenc

e 2

Sequence 3

The MSA approachThe MSA approach

MSA (Lipman et al., 1989, PNAS 86, 4412)MSA (Lipman et al., 1989, PNAS 86, 4412)• Calculate all pairCalculate all pair--wise alignment scorewise alignment scores.s.• Use the scores to predict a tree. Use the scores to predict a tree. • Calculate pair weights based on the tree. Calculate pair weights based on the tree. • Produce a heuristic alignment based on the tree. Produce a heuristic alignment based on the tree. • Calculate the maximum weight for each sequence pair. Calculate the maximum weight for each sequence pair. • Determine the spatial positions that must be calculated to obtain Determine the spatial positions that must be calculated to obtain

the optimal alignment. the optimal alignment. • Perform the optimal alignment. Perform the optimal alignment. • Report the weight found compared to the maximum weight Report the weight found compared to the maximum weight

previously foundpreviously found..• EExtremely slow and memory intensivextremely slow and memory intensive• Max 8-9 sequences of ~250 residuesMax 8-9 sequences of ~250 residues

The DCA approachThe DCA approach

DCA (Stoye et al 1997)DCA (Stoye et al 1997) Iteratively split at Iteratively split at

optimal cut pointsoptimal cut points Use MSA Use MSA ConcatenateConcatenate

So in effect …So in effect …Sequence 1

Seq

uenc

e 2

Sequence 3

Multiple alignment methodsMultiple alignment methods

Multi-dimensional dynamic programmingMulti-dimensional dynamic programming Progressive alignmentProgressive alignment Iterative alignment Iterative alignment

Multiple alignment profilesMultiple alignment profilesGribskov et al. 1987Gribskov et al. 1987

ACDWY

-

i

fA..fC..fD..fW..fY..Gapo, gapxGapo, gapx

Position dependent gap penalties

Core region Core regionGapped region

Gapo, gapx

fA..fC..fD..fW..fY..

fA..fC..fD..fW..fY..

Profile buildingProfile building

ACDWY

Gappenalties

i0.30.100.30.3

0.51.0

Position dependent gap penalties

0.50000.5

00.50.20.10.2

1.0

Example: Each aa is represented as a frequency, penalties as weights

ACD……VWY

sequence

profile

Profile-sequence alignmentProfile-sequence alignment

ACD..Y

ACD……VWY

profile

profileProfile-profile alignmentProfile-profile alignment

Scoring profilesScoring profiles Think of sequence-sequence alignment Same principles but more information for each

position

Reminder: The sequence pair alignment score S comes from

the sum of the positional scores M(aai,aaj) (i.e. the substitution matrix values at each alignment position minus penalties if applicable)

Profile alignment scores are exactly the same, but the positional scores are more complex

Scoring a profile positionScoring a profile position

At each position (column) we have different residue frequencies for each amino acid (rows)

SO: Instead of saying S=M(aa1, aa2) (one residue pair) For frequency f>0 (amino acid is actually there) we take:

ACD..Y

Profile 1ACD..Y

Profile 2

20 20

),(i j

jiji aaaaMfaafaaS

Log-average scoringLog-average scoring

Remember the substitution matrix formula?

In log-average scoring (von Ohsen et al, 2003)

Why is this so important? Think about it…

20 20

logi j aaaa

aaaa

ji

ji

ji

qq

pfaafaaS

20 20

logi j aaaa

aaaa

ji

ji

ji

qq

pfaafaaS

Progressive alignmentProgressive alignment

1) Perform pair-wise alignments of all of the sequences2) Use the alignment scores to produces a dendrogram using

neighbour-joining methods3) Align the sequences sequentially, guided by the

relationships indicated by the tree

Biopat (first method ever) MULTAL (Taylor 1987) DIALIGN (1&2, Morgenstern 1996) PRRP (Gotoh 1996) ClustalW (Thompson et al 1994) Praline (Heringa 1999) T Coffee (Notredame 2000) POA (Lee 2002) MUSCLE (Edgar 2004)

Progressive multiple alignmentProgressive multiple alignment1213

45

Guide tree Multiple alignment

Score 1-2

Score 1-3

Score 4-5

Scores Similaritymatrix5×5

Scores to distances Iteration possibilities

General progressive multiple General progressive multiple alignment techniquealignment technique (follow generated tree)(follow generated tree)

13

25

13

13

13

25

254

d

root

PPRALINERALINE progressive strategy progressive strategy

13

2

13

13

13

25

254

d

4

There are problems:There are problems:

Accuracy is very important !!!!Accuracy is very important !!!! Errors are propagated into theErrors are propagated into the progressiveprogressive stepssteps

“ “ Once a gap, always a gap”Once a gap, always a gap”Feng & Doolittle, 1987Feng & Doolittle, 1987