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Patterns, Profiles, and Multiple Alignment. OUTLINE. Profiles and Sequence Logos Profile Hidden Markov Models Aligning Profiles Multiple Sequence Alignments by Gradual Sequence Adition Other Ways of Obtaining Multiple Alignments Sequence Pattern Discovery. OUTLINE.

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Presentation Transcript
slide1

Patterns, Profiles, and

Multiple Alignment

outline
OUTLINE
  • Profiles and Sequence Logos
  • Profile Hidden Markov Models
  • Aligning Profiles
  • Multiple Sequence Alignments by Gradual Sequence Adition
  • Other Ways of Obtaining Multiple Alignments
  • Sequence Pattern Discovery
outline1
OUTLINE
  • Profiles and Sequence Logos
  • Profile Hidden Markov Models
  • Aligning Profiles
  • Multiple Sequence Alignments by Gradual Sequence Adition
  • Other Ways of Obtaining Multiple Alignments
  • Sequence Pattern Discovery
aligning profiles
Aligning Profiles

Comparing two PSSMs by alignment

Can not done by standard alignment techniques,

Consşder alignement of two columns, one from each PSSM:

Both are in fact scores,

Use measure of the similarity between the scores in the two columns.

aligning profiles1
Aligning Profiles

Comparing two PSSMs by alignment

The Program LAMA (Local Alignment of Multiple Alignments:)

Do not allow gaps in the alignment of PSSMs,

Uses Pearson correlation coefficient as similarity mesure,

The score of each column reanges from 1 to -1.

aligning profiles2
Aligning Profiles

Comparing two PSSMs by alignment

multiple sequence alignments by gradual sequence adition
Multiple Sequence Alignments by Gradual Sequence Adition

Modified pairwise dynamic programming:

Pairwise dynamic programming algorithms can be modified to find the optimal alignment of more than two sequences,

multiple sequence alignments by gradual sequence adition1
Multiple Sequence Alignments by Gradual Sequence Adition

Modified pairwise dynamic programming:

Align 3 sequences:

SEQUENCE 1

SEQUENCE 2

SEQUENCE 3

multiple sequence alignments by gradual sequence adition2
Multiple Sequence Alignments by Gradual Sequence Adition

Modified pairwise dynamic programming:

Align 3 sequences:

multiple sequence alignments by gradual sequence adition3
Multiple Sequence Alignments by Gradual Sequence Adition

Modified pairwise dynamic programming:

Align 3 sequences:

multiple sequence alignments by gradual sequence adition4
Multiple Sequence Alignments by Gradual Sequence Adition

Modified pairwise dynamic programming:

RESULT:

dynamic programming approach for alignment between two sequences is easily extended to k sequences,

For k sequences we need to deal with a k-dimensional matrix,

Therefore, it is impractical due to exponential running time

multiple sequence alignments by gradual sequence adition5
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment:

Multiple alignments are built up by gradually adding sequences,

The order in which they are aded can be crucial to the successful generation of an accurate alignment,

There are different ways to determine this addition.

multiple sequence alignments by gradual sequence adition6
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Dynamic programming,

Sum-of-pairs scoring method,

organize multiple sequence alignment using a guide tree where leaves represent sequences and internal nodes represent alignments,

multiple sequence alignments by gradual sequence adition8
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Steps:

Find similarity matrix.

multiple sequence alignments by gradual sequence adition9
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Steps:

Cluster analysis (tree construction).

multiple sequence alignments by gradual sequence adition10
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Steps:

Align sequences according to the order determined by the tree:

multiple sequence alignments by gradual sequence adition11
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Steps:

Align sequences according to the order determined by the tree:

multiple sequence alignments by gradual sequence adition12
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

depending on the internal node in the tree, we may have to align a

a sequence with a sequence 

a sequence with a profile 

a profile with a profile 

in all cases we can use dynamic programming

for the profile cases, use SP (sum-of-pairs) scoring

multiple sequence alignments by gradual sequence adition13
Multiple Sequence Alignments by Gradual Sequence Adition
  • Progressive alignment (ClustalW):
    • Sum of Pairs Scoring:
      • Consider all possible pairs.
multiple sequence alignments by gradual sequence adition14
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Sum of Pairs Scoring:

multiple sequence alignments by gradual sequence adition15
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Sum of Pairs Scoring:

Assume c(match) = 1 ,

c(mismatch) = -1 ,

and c(gap) = -2 ,

also assume c(-, -) = 0

to prevent the double counting of gaps.

multiple sequence alignments by gradual sequence adition16
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (ClustalW):

Sum of Pairs Scoring:

Assume c(match) = 1 , c(mismatch) = -1 , and c(gap) = -2 , also assume c(-, -) = 0 to prevent the double counting of gaps.

multiple sequence alignments by gradual sequence adition17
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select a sequence c as the center of the star,

For each sequencex1, …, xk such that index i ≠

c, perform a Needleman-Wunsch global alignment

Aggregate alignments with the principle “once a gap, always a gap.”

multiple sequence alignments by gradual sequence adition18
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence:

multiple sequence alignments by gradual sequence adition19
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence:

Simply choose as xc (center sequence) the sequence xithat maximizes the following

multiple sequence alignments by gradual sequence adition20
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence EXAMPLE:

multiple sequence alignments by gradual sequence adition21
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence EXAMPLE:

Compute all pairwise alignments (global alignments) and scores.

multiple sequence alignments by gradual sequence adition22
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence EXAMPLE:

Compute all pairwise alignments (global alignments) and scores.

sequence most

similar to the rest

multiple sequence alignments by gradual sequence adition23
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence EXAMPLE:

multiple sequence alignments by gradual sequence adition24
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence EXAMPLE:

Build the alignment:

multiple sequence alignments by gradual sequence adition25
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence EXAMPLE:

Build the alignment:

multiple sequence alignments by gradual sequence adition26
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

Select the center sequence EXAMPLE:

Build the alignment:

multiple sequence alignments by gradual sequence adition27
Multiple Sequence Alignments by Gradual Sequence Adition

Progressive alignment (Star Alignment):

For highly similar sequences this method can generate a reasonable alignment,

When the percentage identity between sequences is low, multiple alignment obtained by star alignment can be very poor.

other ways of obtaining multiple alignments
Other Ways of Obtaining Multiple Alignments

DIALIGN

Focuses on short ungapped alignments,

Complete alignment can be constructed from ungapped local alignments between pairs of sequences.

other ways of obtaining multiple alignments1
Other Ways of Obtaining Multiple Alignments

DIALIGN

All possible diagonals between each pair of sequences are considered,

other ways of obtaining multiple alignments2
Other Ways of Obtaining Multiple Alignments

SAGA

Use genetig algorithm to find the optimal alignment.

other ways of obtaining multiple alignments3
Other Ways of Obtaining Multiple Alignments

SAGA

Steps in genetic algorithm (GENERAL):

other ways of obtaining multiple alignments4
Other Ways of Obtaining Multiple Alignments

SAGA

Crossover operations in SAGA:

other ways of obtaining multiple alignments5
Other Ways of Obtaining Multiple Alignments

SAGA

Crossover operations (another way) in SAGA:

sequence pattern discovery
Sequence Pattern Discovery

From multiple sequence alignments

By searching for possible patterns in the set of sequences

sequence pattern discovery4
Sequence Pattern Discovery

eMOTIF:

Uses 20 groups of amino acids to denote amino acids that can be substituted by each other

sequence pattern discovery5
Sequence Pattern Discovery

eMOTIF:

For every position of the alignment determine which single group can cover the whole column

By examining the possible column combinations, identify patterns

references
References
  • M. Zvelebil, J. O. Baum, “Understanding Bioinformatics”, 2008, Garland Science
  • Andreas D. Baxevanis, B.F. Francis Ouellette, “Bioinformatics: A practical guide to the analysis of genes and proteins”, 2001, Wiley.
  • Barbara Resch, “Hidden Markov Models - A Tutorial for the Course Computational Intelligence”, 2010.