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Lecture 9 Phylogenetic Prediction. Bioinformatics. Dr. Aladdin HamwiehKhalid Al- shamaa Abdulqader Jighly. Aleppo University Faculty of technical engineering Department of Biotechnology. 2010-2011. Phylogenetic Trees and Dissimilarity estimation. Historical Note.

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Bioinformatics

Lecture 9

  • Phylogenetic Prediction

Bioinformatics

Dr. Aladdin HamwiehKhalid Al-shamaa

Abdulqader Jighly

Aleppo University

Faculty of technical engineering

Department of Biotechnology

2010-2011


Phylogenetic trees and dissimilarity estimation

Phylogenetic Trees and Dissimilarity estimation


Historical note

Historical Note

  • Until mid 1950’s phylogenies were constructed by experts based on their opinion (subjective criteria)

  • Since then, focus on objective criteria for constructing phylogenetic trees

    • Thousands of articles in the last decades

  • Important for many aspects of biology

    • Classification

    • Understanding biological mechanisms


Morphological vs molecular

Morphological vs. Molecular

  • Classical phylogenetic analysis: morphological features: number of legs, lengths of legs, etc.

  • Modern biological methods allow to use molecular features

    • Gene sequences

    • Protein sequences

    • DNA markers


Bioinformatics

From sequences to a phylogenetic tree

RatQEPGGLVVPPTDA

RabbitQEPGGMVVPPTDA

GorillaQEPGGLVVPPTDA

CatREPGGLVVPPTEG

There are many possible types of sequences to use (e.g. Mitochondrial vs Nuclear proteins).


Bioinformatics

Aardvark

Bison

Chimp

Dog

Elephant

Basic Assumptions

  • Closer related organisms have more similar genomes.

  • Highly similar genes are homologous (have the same ancestor).

  • Phylogenetic relation can be expressed by a dendrogram (a “tree”) .

.


Dangers in molecular phylogenies

Dangers in Molecular Phylogenies

  • We have to emphasize that gene/protein sequence can be homologous for several different reasons:

  • Orthologs -- are genes in different species that have evolved from a common ancestral gene via speciation.

  • Paralogs-- sequences diverged after a duplication event

  • Xenologs-- sequences diverged after a horizontal transfer (e.g., by virus)


Gene phylogenies

Gene Duplication

Speciation events

2B

1B

3A

3B

2A

1A

Species Phylogeny

Gene Phylogenies

Phylogenies can be constructed to describe evolution genes.

Three species termed 1,2,3.

Two paralog genes A and B.


Types of trees

Types of Trees

A natural model to consider is that of rooted trees

Common

Ancestor


Types of trees1

Types of trees

Unrooted tree represents the same phylogeny without the root node

Depending on the model, data from current day species does not distinguish between different placements of the root.


Distance based method

Distance-Based Method

Input: distance matrix between species

For two sequences si and sj, perform a pairwise (global)

alignment. Let f = the fraction of sites with different residues. Then

Outline:

  • Cluster species together

  • Initially clusters are singletons

  • At each iteration combine two “closest” clusters to get a new one

(Jukes-Cantor Model)


Human chimp gorilla orangutan and gibbon

Human, Chimp, Gorilla, Orangutan, and Gibbon


Upgma

UPGMA

Step 1: Generate data (Sequence/ Genotype/ Morphological) for each OTU.


Bioinformatics

Step 2: Calculate p- distance for all pairs of taxa

Distance can be calculated by using different substitution models:

1. # of nucleotide differences.

2. p-distance.

3. JC distance

4. K2P distance.

5. F81

6. HKY85

7.GTR etc

= 0.142857143


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Step 3: Calculate distance matrix for all pairs of taxa and select pair of taxa with minimum distance as new OTU.

0.0714

OTU-1

OTU-2

0.0714


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Step 4: Recalculate new distance matrix, assuming OTU-1 and OTU-2 as one OTU.

= 0.3571


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Step 5: Select pair of taxa with minimum distance as new OTU.

0.071

OTU-1

0.107

0.071

OTU-2

0.179

OTU-3

0.107 + 0.071 + 0.179 = 0.357


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Step 6: Again select pair of OTU with minimum distance as new OTU and recalculate distance matrix.

= 0.5714


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Step 7: Again select pair of taxa with minimum distance as new OTU.

0.071

OTU-1

0.107

0.071

OTU-2

0.107

0.179

OTU-3

0.286

OTU-4

0.107 + 0.107 + 0.071 + 0.286 = 0.571


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Step 8: Again select pair of OTU with minimum distance as new OTU and recalculate distance matrix.

= 0.7857


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Step 9: Again select pair of OTU with minimum distance as new OTU and make final rooted tree.

OTU-1

0.071

0.107

0.071

OTU-2

0.107

0.179

OTU-3

0.107

0.286

OTU-4

0.393

OTU-5

0.393 + 0.107 + 0.107 + 0.107 + 0.071 = 0.785


Bioinformatics

Jukes-Cantor distance

the rate of nucleotide substitution is the same for all pairs of the four nucleotides A, T, C, and G

A A

A C

A G

A T

C A

C C

C G

C T

G A

G C

G G

G T

T A

T C

T G

T T

25% similar (= distance of 0.75).

75% which is what you expect with random assignment of nucleotides to a pair of taxa


Bioinformatics

تفترض طريقة UPGMA نسبة ثابتة في طول أفرع شجرة القرابة الوراثية

=-(3/4)*LN(1-(((4/3)*0.1594)))


Neighbor joining

طريقة Neighbor-joining

لا تعتمد طريقة فيتش-مارغولياش على استخدام نسبة ثابتة في طول أفرع شجرة القرابة الوراثية كما هي في طريقة UPGMA

هذه الطريقة تعتمد على تحديد أقرب أزواج للوحدات المدروسة بأقل الأطوال للأفرع. ويمكن تعريف الزوج المقارب (Pair of neighbor) بأنه قيمة الارتباط بين وحدتين بعقدة غير جذرية (unrooted node).

مثال: الإنسان والشيمبانزي متحدان في وحدة على عكس الأنسان والغوريلا وعليه ندعو الوحدة الأولى (الإنسان والشيمبانزي) على تجاور مع الغوريلا، وبعد دراسة القرابة بين الوحدة الأولى والغوريلا نبحث عن القرابة مع باقي أفراد المجتمع المدروس.


Neighbor joining1

طريقة Neighbor-joining

  • مثال لدراسة ثمانية أفراد مدروسة: نبدأ المقارنة كما لو أنهم جميعا مرتبطون بعقدة واحدة، بعدها وعند إثبات الارتباط بين 1 و 2 تصبح الشجرة على


Neighbor joining2

طريقة Neighbor-joining


Neighbor joining3

طريقة Neighbor-joining

A:B = 0.015-(0.4010+0.35)/2


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Example:

=0.179/2+(0.18-0.245)/2

=0.179-0.057


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Human and chimpanzee have the smallest value of Mij and they are replaced by node 2.


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dij

Mij


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  • UPGMA

  • PHYLIP (Phylogeny Inference Package)

  • Neighbor-joining (NJ)


Genetic distance

Genetic distance

N= Fa+Fb+Fc+Fd

Simple Match distance = Fa/N= 3/7= 0.43

Genetic distance (Jaccard) = Fa/(Fa+Fb+Fc) = 3/6= 0.5


Bioinformatics

Dissimilarity indices – Continuous

Euclidean Distance is the most common use of distance. In most cases when people said about distance , they will refer to Euclidean distance. Euclidean distance or simply 'distance' examines the root of square differences between coordinates of a pair of objects.

Euclidean distance


Bioinformatics

Dissimilarity indices – Continuous

Example:

Point A has coordinate (0, 3, 4, 5) and point B has coordinate (7, 6, 3, -1).

The Euclidean Distance between point A and B is

Euclidean distance


Manhattan city block

Manhattan (City-Block)

It is also known as Manhattan distance, boxcar distance, absolute value distance. It examines the absolute differences between coordinates of a pair of objects.


Thank you

Thank you

PAST جلسة العملي تطبيق على برنامج


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