A new approach to detect similar proteins
from 2D gel electrophoresis images
Source: Proceedings of the Third IEEE Symposium on
BioInformations and BioEngineering(BIBE’03), 2003
Authors: Nawaz Khan and Shahedur Rahman
Speaker: Chia-Chun Wu(吳佳駿)
Date: 2005/01/20
Outline
•
•
•
•
•
1. Introduction
2. Methodology
3. Experiments and results
4. Conclusion
5. Comments
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1. Introduction (1/5)
• GELLAB system (1981)
– Uses the point pattern comparison.
• MELANIE (1997)
– Compares spot clusters.
• Panek and Vohradsky (1999)
– Use the information from the neighborhood
spots for comparison.
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1. Introduction (2/5)
• Although the spot in the source and target
image can be identical or similar, but still
the following parameters can vary:
–
–
–
–
Background value
Protein spot intensity
Protein spot shape
Noise in the image
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1. Introduction (3/5)
Triosophosphate isomerase protein spots in two different
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images.
1. Introduction (4/5)
• This paper presents a novel approach for
identifying the identical or similar protein spot in
2D gel electrophoresis images by considering the
following factors:
– 2D gel electrophoresis protein spots differ significantly
in two different images even when they represent the
same protein.
– Same or similar protein spots will lie at the same line of
path because of their electrophoresis mobility (電泳淌度)
and molecular weight (分子量) .
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1. Introduction (5/5)
• This paper presents a novel approach for
identifying the identical or similar protein spot in
2D gel electrophoresis images by considering the
following factors:
– The intensity of the electrophoresis mobility (電泳淌度)
matched regions in both images can be different even
thought it shows a correct matching.
– The region of similar spot at the target image must lie at
the same or different directional vector on the line of
path.
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2. Methodology
1. Determining the position of the protein spot in
the source image
2. Defining the region of interest (ROI)
3. Matching the selected protein spot in the target
image
4. Searching for the protein spot in the
neighborhood area
5. Selecting the best matched spot
6. Retrieving 3D structure of a protein
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2.1. Determining the position of the
protein spot in the source image
Source image divided into four quadrants.
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2.2. Defining the region of interest (1/2)
Region of interest of point P in the source image.
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2.2. Defining the region of interest (2/2)
M = {X1, X2 ,………, Xn }
(a) A set of points defined by the user.
(b) Defining the region of interest.
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2.3. Matching the selected protein
spot in the target image
The target point will be considered as "matched point" when:
1.  s  T ;  s   T ; M s  M T
In other situation where
2.  s  T ;  s   T ; M T  M s or M T  M s
(where M T is within a threshold value), it will be considered as
"spot found" and it might be the same or similar protein.
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2.4. Searching for the protein spot in
the neighborhood area (1/2)
Non emptied straight line of path in the target image
to determine the neighborhood protein spot
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2.4. Searching for the protein spot in
the neighborhood area (2/2)
Directions of search for the neighborhood spot.
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2.5. Selecting the best matched spot
(where i = 1 to n and n is the number
of spots.)
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3. Experiments and results
1. Identifying a spot along the line of path
2. Identifying a spot of interest in the target
image
3. Matching on 2D gel electrophoresis image
4. Shape comparison
5. Retrieving 3D image
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3.1. Identifying a spot along the line
of path (1/2)
A line of path (x, y) is drawn on the image.
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3.1. Identifying a spot along the line
of path (2/2)
threshold
Identifying a spot along the line of path using the
low intensity values.
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3.2. Identifying a spot of interest in
the target image (1/3)
Identifying the spot at the same orientation as it is
in the source image.
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3.2. Identifying a spot of interest in
the target image (2/3)
Identifying the neighborhood spots at the least
variance position.
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3.2. Identifying a spot of interest in
the target image (3/3)
Vectors of each spot on experimental image to
determine the least variance.
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3.3. Matching on 2D gel
electrophoresis image (1/2)
Matching spot in the target image at the same
location as it is in the source
image.
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3.3. Matching on 2D gel
electrophoresis image (2/2)
90% successful
identification of
spots
Identifying the neighborhood spot in the target
image.
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3.4. Shape comparison
(a) Source spot
(b) Detected spot
in the target
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3.5. Retrieving 3D image (1/3)
• The following parameters are stored in the
target specific dedicated database (using
MELAINE software):
–
–
–
–
Coordinates
Intensity values
Positional orientation
Average shape radius
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3.5. Retrieving 3D image (2/3)
3D Protein structures retrieved from the dedicated
database.
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3.5. Retrieving 3D image (3/3)
3D Protein structures retrieved from the dedicated
database.
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4. Conclusion
• A combination of geometric and image
processing techniques have been used to
identify the spot which matches with the
features of the source image spot.
• This approach reduces the number of
candidate spot to be identified within the
image.
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5. Comments
• 作者在這篇論文中只有針對顏色較深的
蛋白質點去做偵測及比對的動作，卻忽
略了的微量蛋白質點。
• 同時影像的背景顏色、蛋白質點的大小、
形狀、強度及雜訊…，這些因素都會影
響實驗的準確度及結果。
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