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Lecture 9 Phylogenetic Prediction. Bioinformatics. Dr. Aladdin Hamwieh Khalid 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 Hamwieh Khalid Al-shamaa

Abdulqader Jighly

Aleppo University

Faculty of technical engineering

Department of Biotechnology

2010-2011

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
slide5
From sequences to a phylogenetic tree

Rat QEPGGLVVPPTDA

Rabbit QEPGGMVVPPTDA

Gorilla QEPGGLVVPPTDA

Cat REPGGLVVPPTEG

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

slide6
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)

upgma
UPGMA

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

slide15
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

slide16
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

slide18
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

slide19
Step 6: Again select pair of OTU with minimum distance as new OTU and recalculate distance matrix.

= 0.5714

slide20
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

slide21
Step 8: Again select pair of OTU with minimum distance as new OTU and recalculate distance matrix.

= 0.7857

slide22
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

slide23
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

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 joining3
طريقة Neighbor-joining

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

slide32
Example:

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

=0.179-0.057

slide35
dij

Mij

slide36
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

slide40
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

slide41
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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