Coffee Shop

Coffee Shop

F91921025 黃仁暐F92921029 戴志華F92921041 施逸優R93921142 吳於芳R94921035 林與絜

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Menu

Coffee Shop OpeningWhy coffee shop?

Three FlavorsCOFFEET-Coffee3DCoffee

RemarksRecipes

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Multiple Sequence Alignment

Multiple sequence alignment is one of the most important tool for analyzing biological sequence.

structure predictionphylogenetic analysisfunction prediction polymerase chain reaction (PCR) primer design.

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Multiple Sequence Alignment

However, the accuracy is not good enough.difficult to evaluate the quality of a multiple alignmentalgorithmically very hard to produce the optimal alignment

In order to increase the accuracy of multiple sequence alignment, we opened a coffee shop to share three kinds of coffee.

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Before (drinking) COFFEEFor comparative genomics, and why?

Understanding the process of evolution at gross level and local levelTranslate DNA sequence data into proteins of known functionMeaning of conservative regions

E. coli, C. elegans, Drosophila, Human…What’s their relationship?

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阿拉伯芥

大腸桿菌酵母菌

集胞藻屬( 藍綠藻類 )

線蟲果蠅人類

Classification for genes of different function

Adapted from “Principles of genome analysis and genomics” Fig. 7.5 (p.129), by S. B. Primrose and R. M. Twyman, 3rd edition

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Comparative genomics vs. multiple sequence alignment

Alignment → conservative regionConservative region → gene locationEvolution evidence

http://www.public.iastate.edu/~semrich/compgen/

2005/12/14 8http://gchelpdesk.ualberta.ca/news/02jun05/cbhd_news_02jun05.php

A: human chromosome IB: human chromosome IIC: human chromosome III

Chromosome III region 125-128 Mb was magnified 120X

The alignment between the chromosomes

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Our FlavorsCOFFEE: A New Objective Function For Multiple Sequence Alignmnent.

C. Notredame, L. Holme and D.G. Higgins,Bioinformatics,Vol 14 (5) 407-422,1998

T-Coffee: A novel method for multiple sequence alignments.

C.Notredame, D. Higgins, J. Heringa,Journal of Molecular Biology,Vol 302, pp205-217,2000

3DCoffee: Combining Protein Sequences and Structures within Multiple Sequence Alignments.

O. O'Sullivan, K Suhre, C. Abergel, D.G. Higgins, C. Notredame. Journal of Molecular Biology,Vol 340, pp385-395,2004

COFFEE

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COFFEE

An objective function for multiple sequence alignments

Cédirc Notredame, Liisa Holm and Desmond G. Higgins

SAGA with COFFEE score

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Introduction COFFEE - Consistency based Objective Function For alignmEnt EvaluationAn objective function, COFFEE score, is proposed to measure the quality of multiple sequence alignmentsOptimize the COFFEE score of a multiple sequence alignment with the genetic algorithm package SAGA (Sequence Alignment Genetic Algorithm)

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Overview of their methodGiven

a set of sequences to be aligneda library containing all pairwise alignments between them,

the COFFEE score reflects the level of consistency between a multiple sequence alignment and the library.

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COFFEE score

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COFFEE score

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Using COFFEE in SAGAIteratively, a multiple sequence alignment with higher COFFEE score is generated by SAGA until the COFFEE score cannot be improved SAGA follows the general principle of genetic algorithm.

The notion of survival of the fittestSAGA iteratively does:

Evaluate the score of the alignmentsThe fitter an alignment, the more likely it is to survive and produce an offspringAlignments survived may be kept unchanged, randomly modified (mutation), or combined with another alignment (cross-over)

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ResultsCOFFEE function

SAGA

Optimization of COFFEE function

Effect of optimization

Comparison: COFFEE and others

Others: PRRP, Clustal W, PILEUP, SAGA MSA, SAM

COFFEE score & alignment accuracy

等下會看到一堆表格很枯燥，所以請忍耐…

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Optimization COFFEE function was optimized by SAGA

Using ClustalW alignmentsUsing SAGA alignments

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Comparison Multiple alignments of SAGA COFFEE and 5 other methods

PRRP, ClustalW, PILEUP, SAGA MSA, SAM

Performance of SAGA and ClustalWComparison of other 5 methods

即使 SAGA-COFFEE 不是最好的結果 →跟最好的也相去不遠Identity level lower → better SAGA-COFFEE results

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Ratio of (E+H) residue correctly alignedBetter of worse alignment? SAGA-COFFEE & othersNO such thing as an ideal method

Correctly aligned ratio Better than PRRPWorse than PRRP

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COFFEE score and alignment accuracy

r=0.65

Coffee sequence score

E+H accuracy (%)E+H accuracy (%)

Average identity (%)

由 coffee score 去預測 alignment 的準確度Average identity 並沒有辦法預測 alignment 的準確度

>85% 的 sequence 都可預測 (error ~ ±10%)

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Correlation between score and accuracyHigher score → higher accuracySAGA produces more high-score sequence than ClustalW

Coffee Break ?

T-Coffee

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T-Coffee

A novel method for multiple sequence alignments

C.Notredame, D. Higgins, J. Heringa

ClustalW with extended library

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ClustalWClustalW is the core alignment stradegy of T-Coffee,

it follows the procedure below:Pairwise Alignment: calculate distance matrixGuide Tree

Unrooted Neighbor-Joining TreeRooted Neighbor-Joining Tree: guide tree with sequence weights

Progressive Alignment: align following the guide tree

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Calculate distance matrix

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Guide tree

Use Neighbor-Joining Method to build guide tree from distance matrix.First construct an unrooted Neighbor-Joining tree, then convert it to a rooted Neighbor-Joining tree, the guide tree.

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Unrooted Neighbor-Joining Tree

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Rooted Neighbor-Joining Tree

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Progressive Alignment: align following the guide tree

Seq1 Seq2 Seq3 Seq4 Seq5

Alignment 1 Alignment 2

Alignment 3 Final alignment

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Progressive-alignment strategy

ProsFaster and saving spaces. (compared with computing all possible multiple alignments)

Cons May not find optimum solution.Errors made in the rest alignments cannot be rectified later as the rest of the sequences are added in.

T-Coffee is an attempt to minimize that effect!“Once a gap, always a gap!”

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T-Coffee Algorithm

Generating a primary library of alignmentsDerivetion of the primary library weightsCombination of the librariesExtending the libraryProgressive alignment strategy

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ClustalW Primary Library (Global)

Lalign Primary Library (Local)

Weighting

Primary Library

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Primary Library

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Weighting

Primary Library

Extension

Extended Library

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Extended Library

A

Weight(A-C-B)= min( Weigh(A-C), Weight(B-C) )= min( 77, 100 ) = 77

Weight(A-D-B)= min( Weight(A-D), Weight(B-D) )= min( 100, 100 ) = 100

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Extended Library

SeqA: GARFIELD THE LAST FAT CATSeqB: GARFIELD THE FAST CAT


A

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Extended Library


ASeqA: GARFIELD THE LAST FAT CATSeqB: GARFIELD THE FAST CAT

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Progressive Alignment



Weighting

Primary Library

Extension

Extended Library

Multiple Alignment Information

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Progressive Assignment

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Complexity Analysiscomplexity of the whole procedure:O(N2L2) + O(N3L) + O(N3) + O(NL2)O(N2L2): computation of the pair-wise libraryO(N3L): computation of the extended pair-wise libraryO(N3): computation of the NJ treeO(NL2): computation of the progressive alignmentN sequences that can be aligned in a multiple alignment of length L

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Experiment

Implementation environmentResult 1: Effect of combining local and global alignments without extension; effect of the library extensionResult 2: compared with other multiple sequence alignment methods

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Implementation environment

Programming language: ANSI CHardware: LINUX platform with Pentium II processors (330 MHz).Test case: BaliBase database of multiple sequence alignment

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Result 1

Table 1: The effect of combining local and global alignments

Name global/local/extend Cat1(81) Cat2(23) Cat3(4) Cat4(12) Cat5(11) Total(141) Significance

C ClustalW pw /.../... 70.6 26.7 43.0 56.0 60.0 58.9 7.8CE ClustalW pw/…/ex 77.1 33.6 47.6 64.8 75.9 66.3 17.7L .../Lalign pw/... 65.4 12.1 22.8 53.9 66.0 52.0 7.8LE .../Lalign pw/ex 72.6 25.6 47.2 77.5 85.5 64.2 16.3CL ClustalW pw/Lalign pw/.. 76.2 32.0 48.3 76.2 74.6 66.5 12.1g

CLE ClustalW pw/Lalign pw /ex 80.6 37.1 52.9 83.2 88.6 72.0

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Result 2

Table 2: T-coffee compared with other multiple sequence alignment methods

Method Cat1(81) Cat2(23) Cat3(4) Cat4(12) Cat5(11) Total1(141) Total2(141) Significance

Dialign 71.0 25.2 35.1 74.7 80.4 61.5 57.3 11.3ClustalW 78.5 32.2 42.5 65.7 74.3 66.4 58.6 26.2Prrp 78.6 32.5 50.2 51.1 82.7 66.4 59.0 36.9 T-Coffee 80.6 37.1 52.9 83.2 88.6 72.0 68.6

3DCoffee

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3DCoffee

Combining protein sequences and structures within multiple sequence alignments

O. O'Sullivan, K Suhre, C. Abergel, D.G. Higgins, C. Notredame

T-Coffee with structure information

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3DCoffeeStructural information can help to improve the quality of multiple sequence alignments

3DCoffeeCombines protein sequences and structuresIs based on T-Coffee version 2.00Uses a mixture of pairwise sequence alignments and pairwise structure comparison methods.

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3DCoffee

Use T-Coffee to compileA primary library: a list of weighted pairs of residues.An extended library: usage the column consistency relationship between all sequences

According to the structure informationFugue, SAP, LSQman

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3DCoffee

Fugue – a threading method that aligns a protein sequence with a 3D-structureSAP – uses DP to compute a pairwise alignment based on a non-rigid structure superpositionLSQman – a rigid body structure superposition package

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3DCoffee

Set the weight of new alignment as 100which is the most score of primary library

Add the weighted alignments into the libraryCarry out progressive alignment the same as T-Coffee

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RemarksCOFFEE : An objective function for multiple sequence alignments

SAGA with COFFEE scoreT-Coffee : A novel method for multiple sequence alignments

ClustalW with extended library3DCoffee : Combining protein sequences and structures within multiple sequence alignmentsT-Coffee with structure information

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RecipesCLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

Julie D.Thompson, Desmond G.Higgins+ and Toby J.Gibson*. 1994COFFEE: A New Objective Function For Multiple Sequence Alignmnent.

C. Notredame, L. Holme and D.G. Higgins,Bioinformatics,Vol 14 (5) 407-422,1998

T-Coffee: A novel method for multiple sequence alignments.C.Notredame, D. Higgins, J. Heringa,Journal of Molecular Biology,Vol 302, pp205-217,2000

3DCoffee: Combining Protein Sequences and Structures within Multiple Sequence Alignments.

O. O'Sullivan, K Suhre, C. Abergel, D.G. Higgins, C. Notredame. Journal of Molecular Biology,Vol 340, pp385-395,2004

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Q & A

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Thank You

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Residue scoreSequence score measurement

Global measurement

Residue was scored 9 >90% of the pairs involved in were also present in the reference library

Residue score evaluated → substitution defined

Class 5 substitution → residue score ≥ 5

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5566677788888888899999877- - - - -66666666788888888887

vsdvprdlevvaatptslliswdap gslevvaatptslliswdap

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• Correct substitution: SAGA > ClustalW

• Lower accuracy: more false positive in SAGA alignment

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High-scoring residues with high accuracy Higher substitution

category → smaller number of prediction

Documents

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