A new approach to detect similar proteins from 2D gel electrophoresis images

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

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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 spo

ts 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 images.

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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

image4. Searching for the protein spot in the

neighborhood area5. Selecting the best matched spot6. 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)

(a) A set of points defined by the user.

(b) Defining the region of interest.

M = {X1, X2 ,………, Xn }

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2.3. Matching the selected protein spot in the target image

The target point will be considered as " " when:

1. ; ;

In other situation where

2. ; ; or

(where is within a threshold value), i

matched point

t will be

s T s T s T

s T s T T s T s

T

M M

M M M M

M

considered as

" " and it might be the same or similarspot protfound ein.

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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)

Identifying a spot along the line of path using the low intensity values.

threshold

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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)

Identifying the neighborhood spot in the target image.

90% successful identification of

spots

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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。