NTMG (N-terminal Truncated Mutants Generator for cDNA): an automatic multiplex PCR assays design for generating various N-terminal truncated cDNA mutants

The sequential deletion method is generally used to locate the functional domain of a protein. With this method, in order to find the various N-terminal truncated mutants, researchers have to investigate the ATG-like codons, to design various multiplex polymerase chain reaction (PCR) forward primers and to do several PCR experiments. This web server (N-terminal Truncated Mutants Generator for cDNA) will automatically generate groups of forward PCR primers and the corresponding reverse PCR primers that can be used in a single batch of a multiplex PCR experiment to extract the various N-terminal truncated mutants. This saves much time and money for those who use the sequential deletion method in their research. This server is available at http://oblab.cs.nchu.edu.tw:8080/WebSDL/

Spot Segmentation and Compression of 2D Gel Image

二維電泳(2-D electrophoresis, 2D Gel)常被用來分析細胞內蛋白質變異的主要工具。其在膠膜之左右兩端通電，利用不同蛋白質間其等電點(pI)值也不同，且分子量大小同樣不相同，二維電泳即利用此來分離出不同的蛋白質。在2D Gel影像中，每一個spot都代表著一種特定的蛋白質。Spotdetection的目的在偵測出一2D Gel影像中，每一個protein spot的輪廓、顏色強度與該spot在影像中的座標位置。一張2D Gel影像常含有數以千計的protein spots，因此透過電腦數位影像處理技術，來分析一張2D Gel影像裡所含有protein spots的相關訊息，是有其必要性的。Spot detection是分析2D Gel影像的一個基本動作，其主要是切割出protein spots，並決定出它們的座標位置與spot的大小、形狀以及濃度等資料。有很多方法常被用來切割出protein spots，如gradient filter、Laplacianfilter、Canny edge detector與watershed edge detector等。但因一2D Gel影像常含有許多與背景顏色強度非常相近的protein spots，故以上方法通常很難提供一良好的切割效果，此計畫因而將提出一spot segmentation of 2D Gelimage (SS2DGI method)來有效地對2D Gel image的protein spots進行有效地切割。SS2DGI method將含有四個主要處理步驟：gradient computation、edgeenhancement 、surrounding suppression 及object segmentation 。gradientcomputation步驟在突顯出影像中的邊緣像素點，並壓制非邊緣像素點。Edgeenhancement步驟在提升顏色強度與背景顏色強度極相近的spots之邊緣像素點的梯度值。Surrounding suppression步驟將利用像素點的梯度值、梯度方向與梯度曲率之特性來強化邊緣像素點，並壓抑非邊緣像素點。最後的object segmentation stage在從梯度影像中切割出spots的影像部分。VQB2DG method結合失真與無失真壓縮步驟來對2D Gel影像進行壓縮。在2D Gel影像中，絕大多數是background部份，且background通常是較偏向白色。故很適合採用VQ-based壓縮法，來對background部份進行壓縮;因為所訓練出來的codewords，大部份會很接近background。因此VQB2DGmethod將以VQ-based壓縮法，先對整張影像進行壓縮，接著再以本計畫所提議的SS2DGI method從2D Gel影像中分離出spots的部份，並以無失真壓縮法對protein spots的部份進行無失真壓縮。本計畫也將以實驗方式來比較VQB2DG method與JPEG-LS壓縮法的壓縮效果。2D Gel 影像能夠有效地被用來診斷疾病與協助生醫學術上的研究，因而能廣受許多生醫專家與研究學者所喜愛，使得每天都有大量的2D Gel 數位影像被產生。為確保影像清晰度以免造成醫生誤診，一般醫學數位影像都採極高的影像解析度。然而影像資料量也相對地快速增加，此不但需耗費龐大記憶體空間來對其作儲存，同時、因受網路頻寬(bandwidth)的限制，當其被置於網路上作傳輸時，也需耗費相當多的傳輸時間。因此發展出一個良好的影像壓縮法，來對2D Gel 數位影像進行壓縮，以降低儲存影像資料所須耗費的記憶體空間，與減少影像資料在網路上傳輸的時間，是有其必要性的。因此本計劃之下一件工作，將提出一VQ-based minute lossy 2DGel image compression (VQB2DG)方法，來對2D Gel 影像進行壓縮。該方法將對2D Gel 影像的背景部份採取低失真壓縮，而對protein spots 部份則採用無失真壓縮。Two-dimensional electrophoresis (2-DE) is an important technique inprotein research which separates different kinds of proteins, based on theirmolecular weights and iso-electric points (pI). In the resulting two-dimensionalspot patterns, each spot represents a specific protein. Each protein ischaracterized by its position in the gel, which determines its pI-values andmolecular weights, and by geometrical parameters describing the form and thevolume of a spot. The complexity of these spot patterns necessitates the use ofpowerful computers and image processing techniques to analyze the gels.Spot detection is a basic procedure for 2D Gel analysis. We must locate theprotein spots coordinates and then we record or compare their attributes. Thereare many methods for detecting the protein spots: gradient filter、Laplacianfilter、Canny edge detector, and watershed edge detector. However, in a 2D Gelimage there are generally numerous protein spots whose color intensity isconsiderably close to that of background. That makes it difficult to precisely cutoff all protein spots from a 2D Gel image by above image segmentation methods.This proposal therefore will propose a spot segmentation of 2D Gel image(SS2DGI method) to segment the protein spots from a 2D Gel image.SS2DGI method contains four stages: gradient computation, edgeenhancement, surrounding suppression, and object segmentation. Gradientcomputation stage is to stand out the edge pixels and suppress non-edge pixelsin an image to generate a gradient image. Edge enhancement stage plans toenhance the gradients of the protein spots whose color intensity is quite close tothat of image background. Surrounding suppression stage will apply the gradientmagnitudes, the gradient directions of pixels, and protein spot curves to suppressnon-edge pixels and stand out edge pixels further. Object segmentation stag

Block image retrieval based on a compressed linear quadtree

Based on a compressed linear quadtree, a data structure is proposed which is more than a structure to compress a gray-level or color image, but a structure, which can be applied to retrieve a block image. This compressed linear quadtree may directly extract a detailed block image without decompressing the compressed data of the original image. (C) 2004 Elsevier B.V. All rights reserved