lesson 2
Download
Skip this Video
Download Presentation
Lesson 2

Loading in 2 Seconds...

play fullscreen
1 / 52

Lesson 2 - PowerPoint PPT Presentation


  • 149 Views
  • Uploaded on

Lesson 2. Aligning sequences and searching databases . Homology and sequence alignment. Homology. Homology = Similarity between objects due to a common ancestry. Hund = Dog, Schwein = Pig. Sequence homology. Similarity between sequences as a result of common ancestry. .

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Lesson 2' - mireya


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
lesson 2

Lesson 2

Aligning sequences and searching databases

homology
Homology

Homology = Similarity between objects due to a common ancestry

Hund = Dog,

Schwein = Pig

sequence homology
Sequence homology

Similarity between sequences as a result of common ancestry.

VLSPAVKWAKVGAHAAGHG

||| || |||| | ||||

VLSEAVLWAKVEADVAGHG

sequence alignment
Sequence alignment

Alignment:Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences.

why align
Why align?

VLSPAVKWAKV

||| || |||| VLSEAVLWAKV

  • To detect if two sequences are homologous. If so, homology may indicate similarity in function (and structure).
  • Required for evolutionary studies (e.g., tree reconstruction).
  • To detect conservation (e.g., a tyrosine that is evolutionary conserved is more likely to be a phosphorylation site).
  • Given a sequenced DNA, from an unknown region, align it to the genome.
sequence alignment1
Sequence alignment

If two sequences share a common ancestor – for example human and dog hemoglobin, we can represent their evolutionary relationship using a tree

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

perfect match
Perfect match

A perfect match suggests that no change has occurred from the common ancestor (although this is not always the case).

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

a substitution
A substitution

A substitution suggests that at least one change has occurred since the common ancestor (although we cannot say in which lineage it has occurred).

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

indel
Indel

Option 1: The ancestor had L and it was lost here. In such a case, the event was a deletion.

VLSEAVLWAKV

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

indel1
Indel

Option 2: The ancestor was shorter and the L was inserted here. In such a case, the event was an insertion.

L

VLSEAVWAKV

VLSPAV-WAKV

||| || |||| VLSEAVLWAKV

VLSEAVLWAKV

VLSPAV-WAKV

indel2
Indel

Normally, given two sequences we cannot tell whether it was an insertion or a deletion, so we term the event as an indel.

Deletion?

Insertion?

VLSEAVLWAKV

VLSPAV-WAKV

indels in protein coding genes
Indels in protein coding genes

Indels in protein coding genes are often of 3bp, 6bp, 9bp, etc...

Gene Search

In fact, searching for indels of length 3K (K=1,2,3,…) can help algorithms that search a genome for coding regions

global vs local
Global vs. Local

Global alignment– finds the best alignment across the entire two sequences.

Local alignment– finds regions of similarity in parts of the sequences.

Global alignment: forces alignment in regions which differ

ADLGAVFALCDRYFQ

|||| |||| |

ADLGRTQN-CDRYYQ

Local alignment will return only regions of good alignment

ADLG CDRYFQ

|||| |||| |

ADLG CDRYYQ

global alignment
Global alignment

PTK2 protein tyrosine kinase 2 of human and rhesus monkey

proteins are comprised of domains
Proteins are comprised of domains

Human PTK2 :

Domain A

Domain B

Protein tyrosine kinase domain

protein tyrosine kinase domain
Protein tyrosine kinase domain

In leukocytes, a different gene for tyrosine kinase is expressed.

Domain A

Domain X

Protein tyrosine kinase domain

slide20

The sequence similarity is restricted to a single domain

PTK2

Domain A

Protein tyrosine kinase domain

Domain B

Domain X

Protein tyrosine kinase domain

Leukocyte TK

conclusions
Conclusions

Use global alignment when the two sequences share the same overall sequence arrangement.

Use local alignment to detect regions of similarity.

pairwise alignment
Pairwise alignment

AAGCTGAATTCGAA

AGGCTCATTTCTGA

One possible alignment:

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

slide26

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

This alignment includes:

2mismatches

4 indels (gap)

10 perfect matches

choosing an alignment for a pair of sequences
Choosing an alignment for a pair of sequences

Many different alignments are

possible for 2 sequences:

AAGCTGAATTCGAA

AGGCTCATTTCTGA

A-AGCTGAATTC--GAA

AG-GCTCA-TTTCTGA-

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

Which alignment is better?

scoring system na ve
Scoring system (naïve)

Perfect match: +1

Mismatch: -2

Indel (gap): -1

AAGCTGAATT-C-GAA

AGGCT-CATTTCTGA-

A-AGCTGAATTC--GAA

AG-GCTCA-TTTCTGA-

Score: =(+1)x10 + (-2)x2 + (-1)x4= 2

Score: =(+1)x9 + (-2)x2 + (-1)x6 = -1

Higher score  Better alignment

alignment scoring scoring of sequence similarity
Alignment scoring - scoring of sequence similarity:
    • Assumes independence between positions:
    • each position is considered separately
  • Scores each position:
    • Positive if identical (match)
    • Negative if different (mismatch or gap)
  • Total score = sum of position scores
    • Can be positive or negative
scoring system
Scoring system
  • In the example above, the choice of +1 for match,-2 for mismatch, and -1 for gap is quite arbitrary
  • Different scoring systems  different alignments
  • We want a good scoring system…
scoring matrix
Scoring matrix
  • Representing the scoring system as a table or matrix n X n (n is the number of letters the alphabet contains. n=4 for nucleotides, n=20 for amino acids)
  • symmetric
dna scoring matrices
DNA scoring matrices

Uniform substitutions between all nucleotides:

Match

Mismatch

dna scoring matrices1
DNA scoring matrices

Can take into account biological phenomena such as:

Transition-transversion

amino acid scoring matrices
Amino-acid scoring matrices

Take into account physico-chemical properties

scoring gaps i
Scoring gaps (I)

In advanced algorithms, two gaps of one amino-acid are given a different score than one gap of two amino acids. This is solved by giving a penalty to each gap that is opened.

Gap extension penalty < Gap opening penalty

scoring gaps ii
Scoring gaps (II)

The dependency between the penalty and the length of the gap need not to be linear.

AGGGTTC—GA

AGGGTTCTGA

Score = -2

AGGGTT-—GA

AGGGTTCTGA

Score = -4

Linear penalty

AGGGT--—GA

AGGGTTCTGA

Score = -6

AGGG---—GA

AGGGTTCTGA

Score = -8

scoring gaps ii1
Scoring gaps (II)

The dependency between the penalty and the length of the gap need not to be linear.

AGGGTTC—GA

AGGGTTCTGA

Score = -4

AGGGTT-—GA

AGGGTTCTGA

Score = -6

Non-linear penalty

AGGGT--—GA

AGGGTTCTGA

Score = -7

AGGG---—GA

AGGGTTCTGA

Score = -8

amino acid substitution matrices
Amino-acid substitution matrices

Actual substitutions:

Based on empirical data

Commonly used by many bioinformatics programs

PAM & BLOSUM

protein matrices actual substitutions
Protein matrices – actual substitutions

The idea: Given an alignment of a large number of closely related sequences we can score the relation between amino acids based on how frequently they substitute each other

M G Y D E

M G Y D E

M G Y E E

M G Y D E

M G Y Q E

M G Y D E

M G Y E E

M G Y E E

In the fourth column

E and D are found in 7 / 8

pam matrix p oint a ccepted m utations
PAM Matrix - Point Accepted Mutations
  • The Dayhoff PAM matrix is based on a database of 1,572 changes in 71 groups of closely related proteins (85% identity => Alignment was easy and reliable).
  • Counted the number of substitutions per amino-acid pair (20 x 20)
  • Found that common substitutions occurred between chemically similar amino acids
pam matrices
PAM Matrices

Family of matrices PAM 80, PAM 120, PAM 250

The number on the PAM matrix represents evolutionary distance

Larger numbers are for larger distances

example pam 250
Example: PAM 250

Similar amino acids have greater score

pam limitations
PAM - limitations

Based only on a single, and limited dataset

Examines proteins with few differences (85% identity)

Based mainly on small globular proteins so the matrix is biased

blosum
BLOSUM

Henikoff and Henikoff (1992) derived a set of matrices based on a much larger dataset

BLOSUM observes significantly more replacements than PAM, even for infrequent pairs

blosum blo cks su bstitution m atrix
BLOSUM:BlocksSubstitutionMatrix

Based on BLOCKS database

~2000 blocks from 500 families of related proteins

Families of proteins with identical function

Blocks are short conserved patterns of 3-60 amino acids without gaps

AABCDA----BBCDA

DABCDA----BBCBB

BBBCDA-AA-BCCAA

AAACDA-A--CBCDB

CCBADA---DBBDCC

AAACAA----BBCCC

blosum1
BLOSUM

Each block represents a sequence alignment with different identity percentage

For each block the amino-acid substitution rates were calculated to create the BLOSUM matrix

blosum matrices
BLOSUM Matrices

BLOSUMn is based on sequences that share at least n percent identity

BLOSUM62 represents closer sequences than BLOSUM45

example blosum62
Example : Blosum62

Derived from blocks where the sequences

share at least 62% identity

pam vs blosum
PAM vs. BLOSUM

PAM100 = BLOSUM90

PAM120 = BLOSUM80

PAM160 = BLOSUM60

PAM200 = BLOSUM52

PAM250 = BLOSUM45

More distant sequences

intermediate summary
Intermediate summary
  • Scoring system = substitution matrix + gap penalty.
  • Used for both global and local alignment
  • For amino acids, there are two types of substitution matrices: PAM and Blosum
ad