Morphological algorithms on medical imaging

Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment. In this article, we focus on describing the new morphological algorithms by ITK (Insight Segmentation and Registration Toolkit). These morphological operators eliminate noise, detect good edges, and overcome the drawback of traditional edge detection methods.

Nuclear Science and Technology, Vol.8, No (2018), pp 01-08 Effect of morphological algorithms on medical imaging Phan Viet Cuong1,*, Ho Thi Thao2, Le Tuan Anh1, Nguyen Hong Ha2, Ha Quang Thanh3 Vietnam Atomic Energy Institute; Centre of Nuclear Physics, Institute of Physics, Vietnam Academy of Science and Technology, Hanoi, Vietnam National Institute of Medical Device and Construction *Corresponding author: pvcuong@iop.vast.ac.vn (Received 16 November 2018, accepted 28 November 2018) Abstract: Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment In this article, we focus on describing the new morphological algorithms by ITK (Insight Segmentation and Registration Toolkit) These morphological operators eliminate noise, detect good edges, and overcome the drawback of traditional edge detection methods Keywords: Medical image processing, edge detection, image enhancement, morphological algorithms, ITK I INTRODUCTION Most of the medical images (X-ray, CTComputed tomography, and MRI- Magnetic resonance imaging) have very low contrast and its grayscale values corresponding to the same tissue change dramatically in comparison to conventional image formats Because many objects are obscured or invaded by neighboring tissues, it is difficult to distinguish the edges of the object of interest and its surroundings Another cause, one of the most common degradations in medical images is noise [1] Image processing technology in health has attracted many researches in recent times Classic edge detection methods in [2,3,4,5] using first and second derivatives detect good edges but sensitive to noise Ishani Thakur and Manish Kansal [6] have summarized various methods for reducing noise on medical images The method proposed by Rohini Paul Joseph et al [7] used a series of algorithms to detect and extract brain tumors on MRI The edges are defined by the gradient and K-mean methods But this approach is overexposed and produces a lot of false edges, so the results are not accurate Gamma correction is also proposed to improve the quality of medical imaging By modifying the gamma value of each individual pixel [8] For improving the quality of such image, a technique that removes unwanted components but does not alter the structure of the original image is necessary This article proposes a new contrast enhancement technique from medical images using morphological algorithms, which replace the low-performance classical methods Its main idea is to use a structural element that moves across the image Morphology can be used instead of many pre-and post-processing techniques, depending on the purpose of the user [9] Because of the flexibility and variety of structural element shapes, morphological operators can remove noise and smooth images as preprocessing filters It can also enhance and highlight the boundaries as clearly as the edge detection methods Morphology depicts the relationship between objects in an image, especially complex gray-scale images such as ©2018 Vietnam Atomic Energy Society and Vietnam Atomic Energy Institute EFFECT OF MORPHOLOGICAL ALGORITHMS ON MEDICAL IMAGING medical images The ultimate aim is to enhance the image obtained from medical deices and assist the clinician in making accurate diagnosis conclusions overlapping ranges between HU values so that HU values not clearly distinguish between different types of soft tissues This is explained by the attenuation of X-rays in low energy regions that depend on Compton scattering and photoelectric effect The intensity of normal brain tissue and tumor region are divided into groups Medical images consist of pixels of varying intensity These regions have small boundaries and HU, so edge detection is difficult However, the pixel intensity values in these regions are still different [13] The efficacy of the proposed method has been demonstrated by experimental results performed on CT and MRI images on the basis of the ITK [10] tool This paper is divided into parts: Part introduces the approach Part presents the operation of morphological algorithms Part gives the results, advantages, and disadvantages of each method implemented in the medical image Noise is a part of the information that creates the actual image because the noise is irrelevant data and there is no direct relationship to the actual image So noise is a random phenomenon that is present in all real signal processing systems, with many sources of noise For example due to insufficient photon to detector; the change in sensitivity of the detector, transmission error, the overlap of different tissues at the same slice; the movement of the patient; beam hardening phenomenon, the metal (which exceeds the maximum attenuation value that the CT can be reconstructed), There are types of noise: additive noise, Gaussian noise, salt & pepper noise [8] CT, MRI images are affected by Gaussian noise (due to the discrete nature of radiation) and salt & pepper noise (due to errors in data transmission, the error pixels are alternately carrying value of or 1) As the number of projections increases, the "star" effect appears in the reconstruction process, called "artifact" [14] Noise affects image quality and reduces the effectiveness of subsequent processing methods Noise can be reduced by improving collimation, longer data acquisition time and circuit design, or applying image filters [15] The sections below describe those filters II METHODS A Factors affecting image quality CT, MRI scan The HU unit measures the attenuation of the X-ray beam in each projection in CT scan: I / I  e  x (1) Where: I0 is the initial intensity of the X beam, I is the intensity of the beam at the detector, μ is the linear attenuation coefficient, and x is the thickness of the reconstruction matrix [11] Because the thickness of the material along the X-ray beam transmitted through multiple pixels, the measurement is the sum of the attenuation of the individual pixels The pixel value assigned to the image is called the HU or pixel intensity (Hounsfield unit) If μ is the average linear attenuation coefficient for the interest pixel and μw is the attenuation value of the water, the HU is calculated by [12]: HU = (µ - µw) / µw (2) The tissue density can vary considerably and many soft tissues have PHAN VIET CUONG et al element is a matrix of two values of and 1, where is defined as neighboring pixels It depends on the object size and the resolution of the image According to the theory of image analysis, the smaller the structural element, the less the ability to filter noise, but can detect small edges and vice versa In the medical image, depending on the subject of interest that select different conservation details B Selection of structure element The morphological algorithms commonly used include erosion, dilation, opening, and closing Structural elements are the basic components of morphological algorithms [16] The selection of shape (square, disk, ring, ) and size (3x3, 5x5, 7x7, ) of the structural element directly affects the resulting image (Fig 1) The structure Fig Shapes of the structural element C Dilation, Erosion, Opening, Closing In the binary image, Binary morphology A – Original Image and, Erosion and dilation are new measures in image segmentation, edge detection, skeleton extraction, Dilation is the process of extending the features by scanning structural elements across the entire image Pixels are added to the boundaries of objects and are set in the maximum during dilation In contrast, the erosion process reduces the features The principle of operation of this process is that the pixels are set to the minimum to remove the corresponding pixels from the object boundary of the structural element B – Structuring Element They are defined as: Dilation: A is dilated by B, written as A⊕B, is defined as (3): A⨁B = {a+b| for some b∈ B and a∈ A} (3) Erosion: A is eroded by B, recorded as A⊝B, and defined as (4): A⊝B = {p|b+p∈A for every b∈B} Opening operation is the function of dilation and erosion in which the structural element is rolled along the inner boundary Closing operation is the function of erosion and dilation in which in which the structural element is moved along the outer boundary [17] (4) Opening is the implementation of turn B to erosion A and b continue to expand resulting matrix obtained, written as A◦B, is defined as (5): A∘B = (A⊝B)⨁B (5) EFFECT OF MORPHOLOGICAL ALGORITHMS ON MEDICAL IMAGING Closing is the opposite of opening, B dilates on A and B continue to erode the result obtained, written as A•B, is defined as (6): A•B = (A⨁B)⊝B Because the individual use of binary operators and rigid binary operators is not flexible at the user's discretion The combination of two operators solves a number of cases such as: dilation of critical detail while removing excess or interstitial space (the only dilation does not this) Or just erase noise, while the object size does not change (only erosion does not this) (6) Gray-scale morphology A(x,y) is a gray-scale two-dimensional image B (a,b) is structuring element Dilation, erosion, opening, and closing of a gray-scale image A(x,y) by a gray-scale structuring element B(a,b) are denoted respectively by (7), (8), (9), (10) [18]: (A⨁B)(x,y) = max{A(x-a,y-b) + B(a, b)} (7) (A⊝B)(x,y) = min{A(x-a,y+b) B(a,b)} (8) (A∘B)(x,y) = (A⊝B)(x,y) ⨁ B(a,b) (9) (A•B)(x,y) = (A⨁B)(x,y) ⊝ B(a,b) (10) III RESULTS We evaluated the effectiveness of these algorithms based on the ITK 4.11.0 (Insight Segmentation and Registration Toolkit) image processing library The images used in the article are taken from the Da Nang Hospital, 108 Military Central Hospital Figure 2, 3, 4, shows the results of images obtained after performing morphological algorithms Clearly, the output images are improved optimally over the original images The obtained images are calibrated to prevent boundary effects and maintain the background values of the original image From Figure 2, we can see that the dilation operator increases the number of bright pixels and decreases the number of dark pixels The final image has a uniform intensity distribution over the original image Figure 2, shows the different results of the 3x3, 5x5 structural elements Contrast increases with size, 3x3 mask gives best results But the edges are not preserved when the size of the structural element increases and results in hardly visible internal structures Erosion algorithms also perform enhanced over the original image It has darker pixels that are optimized along the edges and blur its edges Erosion eliminates small details but its downside is to darken the image Conversely, dilation will increase the brightness of all pixels Figure also increases the overall Similar to binary image, erosion reduces or thins the grayscale scale size of the object, eliminates excess noise and detail Dilation increases or expands increases the grayscale scale size of the object, breaks the segment, fills the gaps The effectiveness of the dilation and erosion algorithms is obvious for border detection, but the interference filtering is limited [19] As mentioned above, the combination of the erosion and dilation operators to overcome the disadvantages of using each single operator The open operator helps smooth the boundary, eliminates the narrow discontinuous (for areas smaller than the structural element), and removes the convex part As opposed to opening, closing operator also eliminates interference, clears up small holes, filling holes in the object border [20] PHAN VIET CUONG et al contrast The dalation creates some dark pixels at the bottom of the colon image This proves that there is a clear edge division and the wrong points are removed, but still preserve the structure of the image Figure 4, are the corresponding result of morphological opening, closing We can see that the dark pixels are removed but the bright pixels are not affected The opening is capable a, of extracting the skeleton of the image It fills in small gaps in the image and highlights the subject for easier identification Finally, the results of Figure show that the edges of MRI images of the brain, abdomen, and intestines are completely detected and distinguished when performing the subtraction of the original image and morphological image closing b, c, Fig (a) Original image; (b), (c) respectively, is the obtained image with the 3x3, 5x5 structure elements of morphological dilation (1,3) take from ITK example data with MRI of the abdomen and intestines [21];(2) of patient Le Quang T with head, take from Da Nang Hospital EFFECT OF MORPHOLOGICAL ALGORITHMS ON MEDICAL IMAGING In general, the structure of the liver, lungs, head, and other organs has been enhanced and shown clearly by morphometric operations Contrasts of the entire target area of interest are improved The greatest advantage is that the border areas have low contrast and visually impaired lungs, liver, injured parts, are clearly defined The morphological method yielded clear results in the diagnosis of laryngopharyngeal symptoms, aortic aneurysm, and colonic pain The disadvantage of the above methods is that the structural element moves in a fixed direction over the image, so noise can be created at the periphery of the object of interest The target image gets more complicated in this case Fig (a) Original image; (b), (c) respectively, is the obtained image with the 3x3, 5x5 structure elements of morphological erosion a, b, c, d, Fig (a) is the image obtained with the 3x3 structure element; (b) Original image - image (a); (c) is the image obtained with the 5x5 structure element; (d) Original image - image (c) of the morphological opening PHAN VIET CUONG et al a, b, c, d, Fig (a) is the image obtained with the 3x3 structure element; (b) Original image - image (a); (c) is the image obtained with the 5x5 structure element; (d) Original image - image (c) of the morphological closing biomedical structures: effective contrast enhancement" Journal of Synchrotron Radiation, pp 848 - 853, Vol 20, 2013 IV CONCLUSIONS By approach based on shape, morphology is the new method used in image analysis and processing instead of traditional methods Experimental results show that this method improves contrast and detects sensitive edges of medical images The advantage of this method is not to confuse the edges with neighboring objects, preserving the edges clearly Dilation, erosion, opening, and closing improve the quality of the entire image without distinguishing pixels from each other so noise is eliminated It effectively supports the segmentation and extraction of tumors and lesions at later stages [2] K Shri Sarika, P Sudha, "An Analysis of Edge Extraction for MRI Medical Images through Mathematical Morphological Operators Approaches" IJCA Proceedings on International Conference on Research Trends in Computer Technologies, 2013 [3] Beant Kaur, Anil Garg, “Comparative study of different edge detection techniques” International journal of Engineering Science and Technology (IJEST), Vol 3, No 3, 2011 [4] Raman Maini and Dr Himanshu Aggarwai, “Study and Comparison of various Image Edge Detection Techniques” International journal of Image Processing, Volume 3, Issue In the next time, we will study new algorithms to automatically detect tumors by combining morphological algorithms, edge detection algorithms, image segmentation algorithms, and image enhancement, in both anatomical and functional images By evaluating, simulating physical processes in reconstrution, image processing is a new direction for the application of nuclear technology in medicine [5] Bindu Bansal, Jasbir Singh Saini, Vipan Bansal and Gurjit Kaur, “Comparison of various edge detection techniques” Journal of Information and Operations Management, Vol 3, Issue 1, pp 103-106, 2012 [6] Ishani Thakur, Manish Kansal, “A review on noise reduction from medical images” International Research Journal of Engineering and Technology (IRJET), Volume 3, Issue 06, pp 2521-2523, 2016 [7] Rohini Paul Joseph, C Senthil Singh, M.Manikandan, “Brain tumor MRI image segmentation and detection in image processing” International Journal of Research REFERENCES [1] Yoshitaka Kimori, "Morphological image processing for quantitative shape analysis of EFFECT OF MORPHOLOGICAL ALGORITHMS ON MEDICAL IMAGING [15] M C de Andrade, “An Interactive Algorithm for Image Smoothing and Segmentation” Electronic Letters on Computer Vision and Image Analysis, 4(1), pp 32-48, 2004 in Engineering and Technology, Volume 03, Issue 01, pp 1-5, 2014 [8] Asadi Amiri, S and Hassanpour H., “A Preprocessing Approach for Image Analysis Using Gamma Correction” International Journal of Computer Applications, 38, pp 3846, 2012 [16] H J Johnson, M M McCormick, and L Ibanez, "The ITK Software Guide" The Insight Software Co Shahjahan Ali, M Nasir Uddin Khan, Md Khalid Hossain, Md Khairul nsortium, Chapter 2, pp 82 - 86, 2016 Available at: https://itk.org/ItkSoftwareGuide.pdf [9] Raihan Firoz, Md Islam, Md Shahinuzzaman, "Medical Image Enhancement Using Morphological Transformation" Journal of Data Analysis and Information Processing, pp - 12, Vol 4, 2016 [10] https://itk.org/ [17] Gaetan Lehmann, "Binary morphological closing and opening image filters" The Insight Journal, 2006 [11] Rachel A Powsner, Matthew R Palmer, and Edward R Powsner, “Essentials of Nuclear Medicine Physics and Instrumentation” Radio Science, 3rd Ed, 2013 [18] Mahesh Kumar, Sukhwinder Singh, "Edge detection and denoising medical image using morphology" International Journal of Engineering Sciences & Emerging Technologies, Vol 2, pp 66 - 72, 2012 [12] Bruni SG, Patafio FM, Dufton JA et-al., “The assessment of anemia from attenuation values of cranial venous drainage on unenhanced computed tomography of the head” Can Assoc Radiol J, 64 (1), pp.46-50, 2013 [19] Zhao Yu-qian, Gui Wei-hua, Chen Zhencheng, Tang Jing-tian, Li Ling yun, "Medical Images Edge Detection Based on Mathematical Morphology" Engineering in Medicine and Biology 27th Annual Conference, Proceedings of the 2005 IEEE, pp 6492 – 6495, 2005 [13] Lim K.O., Pfefferbaum A., “Segmentation of MR brain images into cerebrospinal fluid spaces, white and gray matter” Journal of Computer Assisted Tomography, 13, pp 588– 593, 1989 [20] W Li, Véronique Haese-Coat, Joseph Ronsin, "Object Detection in Medical Images Based on Improved Morphological Multiresolution Decomposition and Morphological Segmentation" Russian Journal of Biomechanics, pp 75 - 88, No 1, 1999 [14] C P Behrenbruch, S Petroudi, S Bond, I, D Declerck, F J Leong, J M Brady, “Image filtering techniques for medical image postprocessing: an overview” The British Journal of Radiology, 77, pp 126-132, 2004 [21] Available at : https://github.com/InsightSoftwareConsortium/ ITK/tree/master/Examples/Data Nuclear Science and Technology, Vol.8, No (2018), pp 09-13 Development of an alpha fast-slow coincidence counter for analysis of 223Ra and 224Ra in seawater Chau Thi Nhu Quynh, Pham Ngoc Tuan, Tran Anh Khoi and Tuong Thi Thu Huong Nuclear Research Institute, 01 Nguyen Tu Luc, Dalat, Lam Dong Email:quynhchaupr@gmail.com (Received 10 October 2018, accepted December 2018) Abstract: An alpha fast-slow coincidence counter has been designed and manufactured for measuring the low alpha activities of 223Ra and 224Ra in the seawater In this work, Radium from the seawater was absorbed onto a column of MnO2 coated fiber (Mn fiber) The short-lived Rn daughters of 223Ra and 224Ra which recoil from the Mn fiber are swept into a scintillation detector where alpha decays of Rn and Po occur Signals from the detector are sent to a delayed coincidence circuit which discriminates decays of the 224Ra daughters, 220Rn and 216Po, from decays of the 223Ra daughters, 219Rn and 215Po Keywords: Low alpha counting system, analysis of 223Ra and 224Ra I INTRODUCTION Giffin et al (1963) developed a highly sensitive system for the measurement of 219Rn and 220Rn by determining the delayed coincidence counting of the rare gas products of 231Pa [1] Based on the Giffin’s design, a similar system has been developed in the Dalat Nuclear Research Institute in order to measure 223 Ra and 224Ra in coastal water The counting system functioned based on the detection of alpha particles from the decaying scheme 223Ra, 224Ra and daughters shown in Fig The delayed circuits were established in order to open and close the gates following the decay times of Rn, about four half-lives of Po [2] By employing the method of conceptual analysis, an alpha fast-slow coincidence spectrometer has been designed and manufactured in the Dalat Nuclear Research Institute This system is used for the low alpha activity analysis of 223Ra and 224Ra in seawater II DESIGN AND MANUFACTURE The block diagram of the alpha fast-slow coincidence counter was shown in Fig Fig Schematic diagram of the delayed coincidence circuit ©2018 Vietnam Atomic Energy Society and Vietnam Atomic Energy Institute DEVELOPMENT OF AN ALPHA FAST-SLOW COINCIDENCE COUNTER FOR ANALYSIS… The detector was fabricated from a sealed plexiglass chamber The silver-activated zinc sulfide ZnS(Ag) is used as a scintillator [3] It was coated on the internal surface of the chamber wall in order to optimize the efficiency of detecting an emitted radiation The volume of the chamber is 1.7 L A scintillation detector coupled to a photomultiplier tube (PMT) R877 of Hamamatsu [4] The signals from PMT were sent to an amplifier and analyzed by a delayed coincidence circuit which includes of a buffer/timer, microcontrollers and connected to the PC via the RS-232 interface The above circuits are designed using the Xilinx ISE 10.1 toolkits and programmed by C++Builder language [5-7] When a Rn nuclear decays, an alpha particle is emitted If this alpha particle interacts with ZnS(Ag) of sealed chamber, it will create photons The PMT obtained the photons and formed electronic pulses The output signals must be shaped and amplified by a shaping amplifier and then converted into logic pulses A counter system analyzes the decay time of each pair of radon-polonium following the decay scheme shown in Fig [NuDat 2.7] Fig Simplified decay scheme of 223Ra and 224Ra gate time of 5.6ms (3T1/2 of 215Po) and the channel (Ch#3) is used to obtain total counts during the measuring time A block diagram of 220 Rn channel and a timing diagram for counter channels are presented in Fig and Fig The delayed coincidence circuit contains three separated counter channels The slow channel (Ch#1) is to measure 224Ra during the gate time of 600ms (4T1/2 of 216Po); the fast channel (Ch#2) is to determine 223Ra during the Fig Block diagram for 220Rn channel 10 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… Fig The structure of Waxy gene (BGIOSGA022241) mined from database (Source: http://www.gramene.org) Based on the mined information of Waxy gene, eight primer pairs (sixteen primers) were designed to amplify and sequence (Table I) The full length of the Waxy gene (BGIOSGA022241) was amplified by forward primer Wx-1F and reverse primer Wx-8R with the size between kb and kb (Fig 2) Table I The information of primers for Waxy gene study Name Sequence (5’-3’) Name Sequence (5’-3’) Wx-1F ACAGCAACAGCTAGACAACCACCAT Wx-5F AAGTACGACGCAACCACGGTAAGAA Wx-1R CTAATCGATCTTGTGATGATCTGA Wx-5R GTGGACTAGACGATCTGGGTTCAAA Wx-2F TGTGGTGCAATTCATTGCAGATCAA Wx-6F TTAGCCGGAAGACCTCTGAGCATTT Wx-2R CATCATGGATTCCTTCGAAGAAAGT Wx-6R GTAGTGTACCGACTTATCGGTATTA Wx-3F TGACAACAGGTGAGGATGTTGTGTT Wx-7F GTCTCAGCGTCGACGTAAGCCTATA Wx-3R ACGATGGACAGTAGTGCAGGGTTGT Wx-7R CCAGTTCTTCGCAGGCCCCTGAAAT Wx-4F CATCGACGGGTATGAGTAAGATTCT Wx-8F GAACAAGACGAACGGTCAAACATGT Wx-4R TTCGCCTCGATTGCCTGAAATTTGT Wx-8R CATATGTAGATCTCAGGCTCTTCAA Fig PCR products of Waxy gene on agarose gel 1.5% (1: DNA ladder 1kb; 2: PCR product of the original type; 3: PCR product of mutant type) Sequencing Standard Kit and read by ABI PRISM 3100 Genetic Analyzer and results were shown in Fig B Sequence the Waxy gene (BGIOSGA022241) The sequencing was conducted by Thermofisher's BigDye Terminator 38 NGUYEN THI HONG et al Fig The result of sequencing Waxy gene by Wx-3R primer (a Original type – a part sequences of Waxy gene in the original type; b Mutant type - a part sequences of Waxy gene in the mutant type) C The identification of nucleotides in Waxy (BGIOSGA022241) The result of comparations between Waxy genes of the original and the mutant variety was shown in Fig 4; Fig mutant gene (a) (b) (c) Fig BLASTN to identify mutation in coding region of Waxy gene (a): Mutation(s) in exon 3; (b): Mutation(s) in exon 4; (c): Mutation(s) in exon (Note: Query- mutant type; Subject- original type) 39 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… (a) (b) Fig The insertion of 32 nucleotides at the splipcing point of intron 12 (a): original sequences; (b): mutant seuqences Total 3480 nucleotides of Waxy gene were analyzed via BLASTN and the result was shown in table II D Development of new DNA marker for rice mutation breeding Based on these point mutations, new DNA marker was developed to improve effeciency of rice mutation breeding (Table III) Table II The discovery of mutation in Waxy gene through BLASTN Gene region Exon Total Identities (%) 1810 1806 (99,8%) Gaps (%) Reference (0,2%) - Exon 3: 34 (T/-); 71 (-/T) - Exon 4: 14 (C/T) - Exon 9: 115 (T/C) - Intron 3: 29 (T/-); 31 (T/-) - Intron 5: (T/C) - Intron 6: Exon6/intron6 junction (G/-); 53 (T/-); 59 (T/-); 63 (T/-) - Intron 8: 29 (A/G); 46 (-/T) - Intron 9: 81 (A/G); 95 (A/G); 99 (-/TAA); 139 (G/A); 142 (A/G); 148 (C/T); 161 (A/G); 165 (C/T); 177 (G/C); 193 (G/A) - Intron 11: 41 (T/C); 58 (A/G) - Intron 12: 83 (A/T); 98 (G/A); 134 (A/C); intron12/exon13 junction (insertion of 32 nucleotides) Intron 1670 1611 (96,5%) 59 (3,5%) Total 3480 3417 (98,2%) 63 (1,8%) Table III The information of new developed DNA marker Sequence (5’- 3’) Target mutation Wx-F: GATTTCAGGTTTGGGGAAAGAT Nucleotide T at position 14 in exon Wx-R: TGGCGGCGGCCATGACGTCAGG Nucleotide C at position 115 in exon Annealing temperature Expected size 49.4 °C 1271 bp (Bold and underline character – mutation point) 40 NGUYEN THI HONG et al total of 1670 non-coding nucleotides compared, it was shown 1611 identities (96.5%) and 59 gaps (3.5%) (table II) The changes were listed: deletions (T/-) at positions 29 and 31 in intron 3; the change (T/C) at position in intron 5; the deletions (T/-) at position 53, 59 and the deletion (A/-) at the position 63 of intron 6; the change (A/G) at position 29 and the insertion (-/T) between positions 45 and 46 in intron 8; the substitutions (A/G) at positions 81, 95, 142, 161, the changes (G/A) at positions 139 and 193, the changes (C/T) at positions 148 and 165, the change (G/C) at position 177 and the insertion (-/TAA) between positions 98 and 99 in intron 9; the alterations (T/C) at position 41 and (A/G) at position 58 in intron 11; the alterations (A/T) at position 83, (G/A) at position 98, (A/C) at position 134, (G/T) at position 206 and the addition of 32 nucleotides “GGGCCTGCGAAGAACTGGGAGAATGT GCTCCT” at the end of intron 12 IV DISSCUSION A Amplify and sequence the full length of Waxy gene Sixteen primers were designed in Table I with lengths from 24 to 25 nucleotides The Wx-1F primer was designed at boundary of 5’-UTR/exon and the Wx-8R primer was designed at boundary of exon 13/3’-UTR There was no failure in amplifying Waxy genes of both original variety and its mutant by Wx-1F and Wx-8R It was indicated that there was no difference at junction sites In agarose gel, there is only one band of PCR products and this band is bold and densitic (Fig 2) These criteria are very important for the accuracy of sequencing Results in Fig were good at reading: no sequences were miss-calls (N), high concentration, no spaced peaks, only one color for each peak and lack of baseline (noise) The full Waxy genes of original type and mutant type were sequenced successfully by sixteen primers (Table I) Four point mutations collected in coding regions (exons) (Fig 4) will result the effect on translation directly Because information of proteins for life is coded by triplets, thus with every mRNA there are three frame of translation In theoretical, the structure of DNA is double strands, thus there are total six frame of reading Based on the C/T mutation at position 14 in exon will cause the replacement of “T” in the original type to “I” the in mutant type; or “P” to “S” The T/C mutation at position 115 in exon resulted substitution of amino acid sequences “XAXNKX” in original type to “KALNKE” in mutant type; or “XXXTRX” to “RR*TRR”; or “XXX” to “GAE” Mutations in exon 3, the deletion of T nucleotide at point 34 and insertion of T at point between 70 and 71, will create the change of amino acids starting from the mutant site B Identify mutation in Waxy gene between the original type and its mutant type Sequences of thirteen exons (coding regions) and twelve introns (non-coding regions) of Waxy gene from original and mutant lines were compared via BLASTN to identify mutation(s) The result in table II shown that, mutant rate in the non-coding region (3,5%) was higer than that in the coding region (0,2%) In coding region, there were 1806 identities (99.8%) and gaps (0.2%) in coding region Four gaps mean point mutations including: the deletion of T nucleotide (T/-) at point 34 and insertion of T (-/T) between points 70 and 71 (in exon 3); the substitution (C/T) at position 14 in exon and the substitution T/C at position 115 in exon In 41 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… In total 59 gaps identified in noncoding regions, two types of mutation with more frequency than other ones were the deletion (T/-) (with five observations) and the substitution (A/G) (with six observations) Mutations at intron/exon junctions were also determined and listed: the deletion G/- at the first of intron and the insertion of 32 nucleotides at the end of intron12 (fig 5) These results leading to us the next research to interpret that if these changes are effective on the cutting of intron and intron 12 or not and how they regulate on the amylose content marker will be studied in further by being used back directly for its mutant population before applying for selection C Development of new DNA marker for rice breeding relevant to amylose content (3) It is important to study, utilize these mutants and new developed DNA marker to improve the efficiency of rice breeding with low amylose content V CONCLUSIONS (1) Four point mutations in coding regions (exon 3, exon and exon 9) of Waxy gene would lead to the difference of amino acids in polypeptide in obvious (2) Some alterations at the first of intron and the end of intron 12 will be done in more experiments to clarify their impact on expression of Waxy gene The forward primer Wx-F was designed based on the substitution C/T at position 14 in exon with the length of 22 nucleotides and 40.9% GC content The reverse primer Wx-R was designed based on the substitution T/C at position 115 in exon with the length of 22 nucleotides and 72.7% GC content The expected size of PCR product which is amplified by this new primer pair is 1271 bp and the recommended annealing temperature is 49.4oC (table III) ACKNOWLEGMENT This experiment was done at the Wakasa-wan Energy Research Center, Fukui, Japan, with the support of the Fukui International Human Resourses Development Center for Atomic Energy (FIHRDC) FY 2016 REFERENCES The new developed DNA marker which was designed with both point mutations at 3’ of two primers in pair: the forward primer Wx-F (5’- GATTTCAGGTTTGGGGAAAGAT - 3’) with the change C/T at position 14 in exon4 (nucleotide T – bold and underline) and the reverse primer Wx-R (5’ TGGCGGCGGCCATGACGTCAGG - 3’) with the substitution T/C at position 115 in exon (nucleotide G – bold and underline) The 3’ of primer which will be bind to the DNA strand firstly in transcription is better in conservating Thus, the mutations were set in the first triplet of 3’ to engage of the accuracy of mutant screening in PCR This new developed DNA [1] S X Tang, G.S Khush, and B.O Juliano, “Variation and correlation of four cooking and eating quality indices of rices” Philipp Journal Crop Science, 14, 45-49, 1989 [2] P D Larkin and W D Park, “Association of Waxy gene single nucleotit polymorphisms with starch characteristics in rice (Oryza sativa L.)” Molecular Breeding, 12 (4), 335– 339, 2003 [3] M Nakagahara and T Nagamine, “Spontaneous occurrence of low amylose genes and geographical distribution of amylose content in Asian rice” Rice Genetics Newsletter, 3, 46-48, 1986 42 NGUYEN THI HONG et al [4] L Liu, X Ma, S Liu, C Zhu, L Jiang, Y Wang, Y Shen, Y Ren, H Dong, L Chen, X Liu, Z Zhao, H Zhai, J Wan, “Identification and characterization of a novel Waxy allele from Yunnan rice landrace” Plant Molecular Biology, 71, 609–626, 2009 [12] X L Cai, Z Y Wang, Y Y Xing, J L Zhang, M M Hong, “Aberrant splicing of intron leads to the heterogeneous 5' UTR and decreased expression of Waxy gene in rice cultivars of intermediate amylose content” The Plant Journal, 14(4), 459-465, 1998 [5] Kharabian Ardashir Masouleh, Daniel L E Waters, Russell F Reinke, Rachelle Ward & Robert J Henry, ”SNP in starch biosynthesis genes associated with nutritional and functional properties of rice” Scientific Reports, 2, Article number: 557, 2012 [13] Z Y Wang, F Q Zheng, G Z Shen, J P Gao, D P Snustad, M G Li, J L Zhang, M M Hong, “The amylose content in rice endosperm is related to the posttranscriptional regulation of the Waxy gene” The Plant Journal, 7(4), 613-622, 1995 [14] M Dobo, N Ayres, G Walker, W D Park, “Polymorphism in the GBSS gene affects amylose content in US and European rice germplasm” Journal Cereal Science, 52(3), 450–456, 2010 [6] M H Chen, C J Bergman, S R M Pinson, R G Fjellstrom, “Waxy gene haplotypes: Associations with apparent amylose content and the effect by the environment in an international rice germplasm collection” Journal of Cereal Science, 47(3), 536-545, 2008 [15] N M Ayres, A M Mc Clung, P D Larkin, H F J Bligh, C A Jones, W D Park, “Microsatellites and a single-nucleotit polymorphism differentiate apparent amylase classes in an extended pedigree of US rice germplasm” Theoretical and Applied Genetics, 94, 773–781, 1997 [7] Cheng Zai Quan, Liu Yan Ping, Chen Rui, Peng Bo, Xiong Hua Bin, Zhang Cheng, Zhong Qiao Fang and Huang Xing Qi, “Diversity of Waxy gene alleles in the wild rice species of the Oryza genus” Botanical Studies,51, 403-411, 2010 [16] C Biselli, D Cavalluzzo, R Perrini, A Gianinetti, P Bagnaresi, S Urso, G Orasen, F Desiderio, E Lupotto, L Cattivelli, “Improvement of marker-based predictability of Apparent Amylose Content in japonica rice through GBSSI allele mining” Rice, (1), 2014 [8] H Y Hirano, Y Sano, “Molecular Characterization of the Waxy Locus of Rice (Oryza sativa)” Plant and Cell Physiology, 32 (7), 989-997, 1991 [9] I Mikami, N Uwatoko, Y Ikeda, J Yamaguchi, H Y Hirano, Y Suzuki and Y Sano, “Allelic diversification at the Wx locus in landraces of Asian rice” Theoretical and Applied Genetics, 116 (7), 979–89, 2008 [17] P D Larkin and W D Park, “Transcript accumulation and utilization of alternate and non-consensus splice sites in rice granulebound starch synthase are temperaturesensitive and controlled by a single-nucleotit polymorphism” Plant Molecular Biology, 40 (4), 719–727, 1999 [10] M Isshiki, K Morino, M Nakajima, R J Okagaki, S R Wessler, T Izawa and K Shimamoto, “A naturally occurring functional allele of the rice Waxy locus has a GT to TT mutation at the 5′ splice site of the first intron” The Plant Journal, 15 (1), 133–138, 1998 [18] Tran Thi Thu Hoai, Hiroaki Matsusaka, Yoshiko Toyosawa, Tran Danh Suu, Hikaru Satoh and Toshihiro Kumamaru, “Influence of single-nucleotit polymorphisms in the gene encoding granule-bound starch synthase I on amylose content in Vietnamese rice cultivars” Breeding science, 64(2), 142–148, 2014 [11] Y Sano, “Differential regulation of Waxy gene expression in rice endosperm” Theoretical and Applied Genetics, 68 (5), 467-473, 1985 43 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… [19] A Kharabian, “An efficient computational method for screening functional SNPs in plants” Journal of Theoretical Biology, 265, 55–6, 2010 levels in North Vietnam local rice cultivars” Rice Genetics Newsletter, 24, 62–64, 2008 [23] M S Jahan, T Kumamaru, A Hamid and H Satoh, “Diversity of granule bound starch synthase (GBSS) level in Bangladesh rice cultivars” Rice Genetics Newsletter, 19, 69– 71, 2002 [20] H Sato, Y Suzuki, M Sakai, T Imbe, “Molecular characterization of Wx-mq, anovel mutant gene for low-amylose content in endosperm of rice (Oryza sativa L.)” Breeding Science, 52, 131–135, 2002 [24] H Satoh, R X Ronald and T C Katayama, “On amylose content of cultivated rice collected in Madagasca, Kagoshima University Research Center South Pacific”, Occasional Papers, 18, 83–91, 1990 [21] J S Bao, H Corke, M Sun, “Nucleotit diversity in starch synthase IIa and validation of single nucleotit polymorphisms in relation to starch gelatinization temperature and other physicochemical properties in rice (Oryza sativa L.)” Theoretical and Applied Genetics, 113, 1171–1183, 2006 [25] https://www.qiagen.com [26] http://www.gramene.org APPENDIX [22] T T Hoai, A Nishi and H Satoh, “Diversity of granule bound starch synthesis (GBSS) The comparison between Waxy genes of original and mutant types Exon1 Score Expect Identities Gaps Strand 627 bits(339) 0.0 339/339(100%) 0/339(0%) Plus/Plus Query ATGTCGGCTCTCACCACGTCCCAGCTCGCCACCTCGGCCACCGGCTTCGGCATCGCCGAC 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct ATGTCGGCTCTCACCACGTCCCAGCTCGCCACCTCGGCCACCGGCTTCGGCATCGCCGAC 60 Query 61 AGGTCGGCGCCGTCGTCGCTGCTCCGCCACGGGTTCCAGGGCCTCAAGCCCCGCAGCCCC 120 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 61 AGGTCGGCGCCGTCGTCGCTGCTCCGCCACGGGTTCCAGGGCCTCAAGCCCCGCAGCCCC 120 Query 121 GCCGGCGGCGACGCGACGTCGCTCAGCGTGACGACCAGCGCGCGCGCGACGCCCAAGCAG 180 Sbjct 121 GCCGGCGGCGACGCGACGTCGCTCAGCGTGACGACCAGCGCGCGCGCGACGCCCAAGCAG 180 Query 181 CAGCGGTCGGTGCAGCGTGGCAGCCGGAGGTTCCCCTCCGTCGTCGTGTACGCCACCGGC 240 Sbjct 181 CAGCGGTCGGTGCAGCGTGGCAGCCGGAGGTTCCCCTCCGTCGTCGTGTACGCCACCGGC 240 Query 241 GCCGGCATGAACGTCGTGTTCGTCGGCGCCGAGATGGCCCCCTGGAGCAAGACCGGCGGC 300 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 241 GCCGGCATGAACGTCGTGTTCGTCGGCGCCGAGATGGCCCCCTGGAGCAAGACCGGCGGC Query 301 CTCGGTGACGTCCTCGGTGGCCTCCCCCCTGCCATGGCT 44 339 300 NGUYEN THI HONG et al ||||||||||||||||||||||||||||||||||||||| Sbjct 301 CTCGGTGACGTCCTCGGTGGCCTCCCCCCTGCCATGGCT 339 Exon Score Expect Identities Gaps Strand 150 bits(81) 2e-42 81/81(100%) 0/81(0%) Plus/Plus Query GCGAATGGCCACAGGGTCATGGTGATCTCTCCTCGGTACGACCAGTACAAGGACGCTTGG Sbjct GCGAATGGCCACAGGGTCATGGTGATCTCTCCTCGGTACGACCAGTACAAGGACGCTTGG Query 61 GATACCAGCGTTGTGGCTGAG Sbjct 61 GATACCAGCGTTGTGGCTGAG 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 60 81 ||||||||||||||||||||| 81 Exon Score Expect Identities Gaps Strand 174 bits(94) 2e-49 99/101(98%) 2/101(1%) Plus/Plus Query ATCAAGGTTGCAGACAGGTACGAGAGGGTGAGG-TTTTTCCATTGCTACAAGCGTGGAGT Sbjct ATCAAGGTTGCAGACAGGTACGAGAGGGTGAGGTTTTTTCCATTGCTACAAGCGTGGAGT Query 60 CGACCGTGTGTTTCATCGACCATCCGTCATTCCTGGAGAAG 59 ||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||| 60 100 |||||||||| |||||||||||||||||||||||||||||| Sbjct 61 CGACCGTGTG-TTCATCGACCATCCGTCATTCCTGGAGAAG 100 Exon Score Expect Identities Gaps Strand 161 bits(87) 1e-45 89/90(99%) 0/90(0%) Plus/Plus Query GTTTGGGGAAAGATCGGAGAGAAGATCTACGGACCTGACACTGGAGTTGATTACAAAGAC Sbjct GTTTGGGGAAAGACCGGAGAGAAGATCTACGGACCTGACACTGGAGTTGATTACAAAGAC Query 61 AACCAGATGCGTTTCAGCCTTCTTTGCCAG 60 ||||||||||||| |||||||||||||||||||||||||||||||||||||||||||||| 60 90 |||||||||||||||||||||||||||||| Sbjct 61 AACCAGATGCGTTTCAGCCTTCTTTGCCAG 90 Exon Score Expect Identities Gaps Strand 119 bits(64) 4e-33 64/64(100%) 0/64(0%) Plus/Plus Query GCAGCACTCGAGGCTCCTAGGATCCTAAACCTCAACAACAACCCATACTTCAAAGGAACT 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GCAGCACTCGAGGCTCCTAGGATCCTAAACCTCAACAACAACCCATACTTCAAAGGAACT 45 60 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… Query 61 TATG Sbjct 61 TATG 64 |||| 64 Exon Score Expect Identities Gaps Strand 187 bits(101) 3e-53 101/101(100%) 0/101(0%) Plus/Plus Query GTGAGGATGTTGTGTTCGTCTGCAACGACTGGCACACTGGCCCACTGGCGAGCTACCTGA Sbjct GTGAGGATGTTGTGTTCGTCTGCAACGACTGGCACACTGGCCCACTGGCGAGCTACCTGA Query 61 AGAACAACTACCAGCCCAATGGCATCTACAGGAATGCAAAG 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 60 101 ||||||||||||||||||||||||||||||||||||||||| Sbjct 61 AGAACAACTACCAGCCCAATGGCATCTACAGGAATGCAAAG 101 Exon Score Expect Identities Gaps Strand 226 bits(122) 9e-65 122/122(100%) 0/122(0%) Plus/Plus Query TTTTCACTGCAGGTTGCTTTCTGCATCCACAACATCTCCTACCAGGGCCGTTTCGCTTTC 60 Sbjct TTTTCACTGCAGGTTGCTTTCTGCATCCACAACATCTCCTACCAGGGCCGTTTCGCTTTC 60 Query 61 GAGGATTACCCTGAGCTGAACCTCTCCGAGAGGTTCAGGTCATCCTTCGATTTCATCGAC 120 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 61 GAGGATTACCCTGAGCTGAACCTCTCCGAGAGGTTCAGGTCATCCTTCGATTTCATCGAC Query 121 GG 120 122 || Sbjct 121 GG 122 Exon Score Expect Identities Gaps Strand 451 bits(244) 6e-132 244/244(100%) 0/244(0%) Plus/Plus Query GTATGACACGCCGGTGGAGGGCAGGAAGATCAACTGGATGAAGGCCGGAATCCTGGAAGC 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GTATGACACGCCGGTGGAGGGCAGGAAGATCAACTGGATGAAGGCCGGAATCCTGGAAGC 60 Query 61 CGACAGGGTGCTCACCGTGAGCCCGTACTACGCCGAGGAGCTCATCTCCGGCATCGCCAG 120 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 61 CGACAGGGTGCTCACCGTGAGCCCGTACTACGCCGAGGAGCTCATCTCCGGCATCGCCAG 120 Query 121 GGGATGCGAGCTCGACAACATCATGCGGCTCACCGGCATCACCGGCATCGTCAACGGCAT 180 46 NGUYEN THI HONG et al |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 121 GGGATGCGAGCTCGACAACATCATGCGGCTCACCGGCATCACCGGCATCGTCAACGGCAT 180 Query 181 GGACGTCAGCGAGTGGGATCCCAGCAAGGACAAGTACATCACCGCCAAGTACGACGCAAC 240 Sbjct 181 GGACGTCAGCGAGTGGGATCCCAGCAAGGACAAGTACATCACCGCCAAGTACGACGCAAC Query 241 CACG |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 240 244 |||| Sbjct 241 CACG 244 Exon Score Expect Identities Gaps Strand 322 bits(174) 2e-93 176/177(99%) 0/177(0%) Plus/Plus Query GCAATCGAGGCGAAGGCGCTGAACAAGGAGGCGTTGCAGGCGGAGGCGGGTCTTCCGGTC 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GCAATCGAGGCGAAGGCGCTGAACAAGGAGGCGTTGCAGGCGGAGGCGGGTCTTCCGGTC 60 Query 61 GACAGGAAAATCCCACTGATCGCGTTCATCGGCAGGCTGGAGGAACAGAAGGGCCCTGAC 120 |||||||||||||||||||||||||||||||||||||||||||||||||||||| ||||| Sbjct 61 GACAGGAAAATCCCACTGATCGCGTTCATCGGCAGGCTGGAGGAACAGAAGGGCTCTGAC Query 121 GTCATGGCCGCCGCCATCCCGGAGCTCATGCAGGAGGACGTCCAGATCGTTCTTCTG 120 177 ||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 121 GTCATGGCCGCCGCCATCCCGGAGCTCATGCAGGAGGACGTCCAGATCGTTCTTCTG 177 Exon 10 Score Expect Identities Gaps Strand 355 bits(192) 3e-103 192/192(100%) 0/192(0%) Plus/Plus Query GGTACTGGAAAGAAGAAGTTCGAGAAGCTGCTCAAGAGCATGGAGGAGAAGTATCCGGGC 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GGTACTGGAAAGAAGAAGTTCGAGAAGCTGCTCAAGAGCATGGAGGAGAAGTATCCGGGC 60 Query 61 AAGGTGAGGGCCGTGGTGAAGTTCAACGCGCCGCTTGCTCATCTCATCATGGCCGGAGCC 120 Sbjct 61 AAGGTGAGGGCCGTGGTGAAGTTCAACGCGCCGCTTGCTCATCTCATCATGGCCGGAGCC 120 Query 121 GACGTGCTCGCCGTCCCCAGCCGCTTCGAGCCCTGTGGACTCATCCAGCTGCAGGGGATG 180 Sbjct 121 GACGTGCTCGCCGTCCCCAGCCGCTTCGAGCCCTGTGGACTCATCCAGCTGCAGGGGATG Query 181 AGATACGGAACG |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 192 |||||||||||| Sbjct 181 AGATACGGAACG 192 47 180 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… Exon 11 Score Expect Identities Gaps Strand 161 bits(87) 1e-45 87/87(100%) 0/87(0%) Plus/Plus Query CCCTGTGCTTGCGCGTCCACCGGTGGGCTCGTGGACACGGTCATCGAAGGCAAGACTGGT 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct CCCTGTGCTTGCGCGTCCACCGGTGGGCTCGTGGACACGGTCATCGAAGGCAAGACTGGT Query 61 TTCCACATGGGCCGTCTCAGCGTCGAC Sbjct 61 TTCCACATGGGCCGTCTCAGCGTCGAC 60 87 ||||||||||||||||||||||||||| 87 Exon 12 Score Expect Identities Gaps Strand 239 bits(129) 1e-68 129/129(100%) 0/129(0%) Plus/Plus Query TGCAAGGTGGTGGAGCCAAGCGACGTGAAGAAGGTGGCGGCCACCCTGAAGCGCGCCATC 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct TGCAAGGTGGTGGAGCCAAGCGACGTGAAGAAGGTGGCGGCCACCCTGAAGCGCGCCATC 60 Query 61 AAGGTCGTCGGCACGCCGGCGTACGAGGAGATGGTCAGGAACTGCATGAACCAGGACCTC 120 Sbjct 61 AAGGTCGTCGGCACGCCGGCGTACGAGGAGATGGTCAGGAACTGCATGAACCAGGACCTC Query 121 TCCTGGAAG Sbjct 121 TCCTGGAAG |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 120 129 ||||||||| 129 Exon 13 Score Expect Identities Gaps Strand 158 bits(85) 2e-44 85/85(100%) 0/85(0%) Plus/Plus Query GGGCCTGGGCGTCGCCGGCAGCGCGCCGGGGATCGAAGGCGACGAGATCGCGCCGCTCGC Sbjct GGGCCTGGGCGTCGCCGGCAGCGCGCCGGGGATCGAAGGCGACGAGATCGCGCCGCTCGC Query 61 CAAGGAGAACGTGGCTGCTCCTTGA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 60 85 ||||||||||||||||||||||||| Sbjct 61 CAAGGAGAACGTGGCTGCTCCTTGA 85 Intron Score Expect Identities Gaps Strand 209 bits(113) 8e-60 113/113(100%) 0/113(0%) Plus/Plus Query GTAAGCACACACAAACTTCGATCGCTCGTCGTCGCTGACCGTCGTCGTCTTCAACTGTTC 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GTAAGCACACACAAACTTCGATCGCTCGTCGTCGCTGACCGTCGTCGTCTTCAACTGTTC 48 60 NGUYEN THI HONG et al Query 61 TTGATCATCGCATTGGATGGATGTGTAATGTTGTGTTCTTGTGTTCTTTGCAG Sbjct 61 TTGATCATCGCATTGGATGGATGTGTAATGTTGTGTTCTTGTGTTCTTTGCAG 113 ||||||||||||||||||||||||||||||||||||||||||||||||||||| 113 Intron Score Expect Identities Gaps Strand 198 bits(107) 2e-56 107/107(100%) 0/107(0%) Plus/Plus Query GTAGGAGCATATGCGTGATCAGATCATCACAAGATCGATTAGCTTTAGATGATTTGTTAC 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GTAGGAGCATATGCGTGATCAGATCATCACAAGATCGATTAGCTTTAGATGATTTGTTAC Query 61 ATTTCGCAAGATTTTAACCCAAGTTTTTGTGGTGCAATTCATTGCAG 60 107 ||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 61 ATTTCGCAAGATTTTAACCCAAGTTTTTGTGGTGCAATTCATTGCAG 107 Intron Score Expect Identities Gaps Strand 169 bits(91) 1e-47 96/98(98%) 2/98(2%) Plus/Plus Query GTGGAGTCATCATTAGTTTACCtttttt-g-tttttACTGAATTATTAACAGTGCATTTA Sbjct GTGGAGTCATCATTAGTTTACCTTTTTTTGTTTTTTACTGAATTATTAACAGTGCATTTA Query 59 GCAGTTGGACTGAGCTTAGCTTCCACTGGTGATTTCAG 58 |||||||||||||||||||||||||||| | ||||||||||||||||||||||||||||| 60 96 |||||||||||||||||||||||||||||||||||||| Sbjct 61 GCAGTTGGACTGAGCTTAGCTTCCACTGGTGATTTCAG 98 Intron Score Expect Identities Gaps Strand 182 bits(98) 1e-51 98/98(100%) 0/98(0%) Plus/Plus Query GTCAGTGATTACTTCTATCTGATGATGGTTGGAAGCATCACGAGTTTACCATAGTATGTA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GTCAGTGATTACTTCTATCTGATGATGGTTGGAAGCATCACGAGTTTACCATAGTATGTA Query 61 TGGATTCATAACTAATTCGTGTATTGATGCTACTGCAG 60 98 |||||||||||||||||||||||||||||||||||||| Sbjct 61 TGGATTCATAACTAATTCGTGTATTGATGCTACTGCAG 98 Intron Score Expect Identities Gaps Strand 165 bits(89) 1e-46 91/92(99%) 0/92(0%) Plus/Plus Query GTGAGTTACAATTGATCTCAAGATCTTATAACTTTCTTCGAAGGAATCCATGATGATCAG |||||||| ||||||||||||||||||||||||||||||||||||||||||||||||||| 49 60 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… Sbjct GTGAGTTATAATTGATCTCAAGATCTTATAACTTTCTTCGAAGGAATCCATGATGATCAG Query 61 ACTAATTCCTTCCGGTTTGTTACTGACAACAG 60 92 |||||||||||||||||||||||||||||||| Sbjct 61 ACTAATTCCTTCCGGTTTGTTACTGACAACAG 92 Intron Score Expect Identities Gaps Strand 130 bits(70) 3e-36 78/81(96%) 3/81(3%) Plus/Plus Query GTCTATGCTTGTTCTTGCCATACCAACTCAAATCTGCATGCACACTGCATT-CTGTT-CA 58 ||||||||||||||||||||||||||||||||||||||||||||||||||| ||||| || Sbjct GTCTATGCTTGTTCTTGCCATACCAACTCAAATCTGCATGCACACTGCATTTCTGTTTCA Query 59 G-AAACTGACTGTCTGAATCT Sbjct 62 GAAAACTGACTGTCTGAATCT 61 78 | ||||||||||||||||||| 82 Intron Score Expect Identities Gaps Strand 224 bits(121) 3e-64 121/121(100%) 0/121(0%) Plus/Plus Query GTATGAGTAAGATTCTAAGAGTAACTTACTGTCAATTCGCCATATATCGATTCAATCCAA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GTATGAGTAAGATTCTAAGAGTAACTTACTGTCAATTCGCCATATATCGATTCAATCCAA 60 Query 61 GATCCTTTTGAGCTGACAACCCTGCACTACTGTCCATCGTTCAAATCCGGTTAAATTTCA 120 Sbjct 61 GATCCTTTTGAGCTGACAACCCTGCACTACTGTCCATCGTTCAAATCCGGTTAAATTTCA Query 121 G Sbjct 121 G |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 120 121 | 121 Intron Score Expect Identities Gaps Strand 204 bits(110) 4e-58 115/117(98%) 1/117(0%) Plus/Plus Query GTAAGAACGAATGCATTCTTCACAAGATGTGCAATCTGAATTTTCTTTGAAAAAGAAATT Sbjct GTAAGAACGAATGCATTCTTCACAAGATATGCAATCTGAATTTTC-TTGAAAAAGAAATT Query 61 ATCATCTGTCACTTCTTGATTGATTCTGACAAGGCAAGAATGAGTGACAAATTTCAG 60 |||||||||||||||||||||||||||| |||||||||||||||| |||||||||||||| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||| 50 117 59 NGUYEN THI HONG et al Sbjct 60 ATCATCTGTCACTTCTTGATTGATTCTGACAAGGCAAGAATGAGTGACAAATTTCAG 116 Intron Score Expect Identities Gaps Strand 375 bits(203) 4e-109 231/243(95%) 3/243(1%) Plus/Plus Query GTATAATATAATACACTACAAGACACACTTGCACGATATGCCAAAAATTCAGAACAAATT 60 Sbjct GTATAATATAATACACTACAAGACACACTTGCACGATATGCCAAAAATTCAGAACAAATT 60 Query 61 CAGTGGCaaaaaaaaaaCTCGAATATTAGGGAAGGACCTAATAATATCAAATAATTAGAA 120 Sbjct 61 CAGTGGCAAAAAAAAAACTCAAATATTAGGGAAGAACC -TAATATCAAATAATTAGAA 117 Query 121 GGGGTGAGGCTTTGAACCCAGATCGTCTAGTCCACCACCTTGTGGAGTTAGCCGGAAGAC 180 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||| ||||||||||||| ||| ||||||||||||||||||| ||||||||||||||||||||| || ||||| |||||||||||| ||| ||||||||||| Sbjct 118 GGGGTGAGGCTTTGAACCCAGGTCATCTAGCCCACCACCTTGTAGAGCTAGCCGGAAGAG 177 Query 181 CTCTGAGCATTTCTCAATTCAGTGGCAAATGATGTGTATAATTTTGATCCGTGTGTGTTT 240 ||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||| Sbjct 178 CTCTGAGCATTTCTCGATTCAGTGGCAAATGATGTGTATAATTTTGATCCGTGTGTGTTT Query 241 CAG 237 243 ||| Sbjct 238 CAG 240 Intron 10 Score Expect Identities Gaps Strand 196 bits(106) 5e-56 106/106(100%) 0/106(0%) Plus/Plus Query GTATACAATTTCCATCTATCAATTCGATTGTTCGATTTCATCTTTGTGCAATGCAATGCA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GTATACAATTTCCATCTATCAATTCGATTGTTCGATTTCATCTTTGTGCAATGCAATGCA Query 61 ATTGCAAATGCAAATGCATGATGATTTTCCTTGTTGATTTCTCCAG Sbjct 61 ATTGCAAATGCAAATGCATGATGATTTTCCTTGTTGATTTCTCCAG 60 106 |||||||||||||||||||||||||||||||||||||||||||||| 106 Intron 11 Score Expect Identities Gaps Strand 191 bits(103) 3e-54 107/109(98%) 0/109(0%) Plus/Plus Query GTAAGCCTATACATTTACATAACAATCAGATATGACACATCCTAATACCGATAAGTCGGT |||||||||||||||||||||||||||||||||||||||| |||||||||||||||| || 51 60 IDENTIFICATION OF SOME NUCLEOTIDE MUTATIONS IN WAXY GENE (BGIOSGA022241)… Sbjct GTAAGCCTATACATTTACATAACAATCAGATATGACACATTCTAATACCGATAAGTCAGT Query 61 ACACTACTACACATTTACATGGTTGCTGGTTATATGGtttttttGGCAG 60 109 ||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 61 ACACTACTACACATTTACATGGTTGCTGGTTATATGGTTTTTTTGGCAG 109 Intron 12 Score Expect Identities Gaps Strand 638 bits(345) 0.0 353/357(99%) 0/357(0%) Plus/Plus Query GTATAAATTACGAAACAAATTTAACCCAAACATATACTATATACTCCCTCCGCTTCTAAA 60 |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct GTATAAATTACGAAACAAATTTAACCCAAACATATACTATATACTCCCTCCGCTTCTAAA 60 Query 61 TATTCAACGCCGTTGTCTTTTTTAAATATGTTTGACCATTCGTCTTATTaaaaaaaTTAA 120 Sbjct 61 TATTCAACGCCGTTGTCTTTTTAAAATATGTTTGACCGTTCGTCTTATTAAAAAAATTAA 120 Query 121 ATAATTATAAATTCTTTTCCTATCATTTGATTCATTGTTAAATATACTTATATGTATACA 180 Sbjct 121 ATAATTATAAATTATTTTCCTATCATTTGATTCATTGTTAAATATACTTATATGTATACA 180 Query 181 TATAGTTTTACATATTTCATAAAATTTTTTGAACAAGACGAACGGTCAAACATGTGCTAA 240 Sbjct 181 TATAGTTTTACATATTTCATAAAAGTTTTTGAACAAGACGAACGGTCAAACATGTGCTAA 240 Query 241 AAAGTTAACGGTGTCGAATATTCAGAAACGGAGGGAGTATAAACGTCTTGTTCAGAAGTT 300 |||||||||||||||||||||| |||||||||||||| |||||||||||||||||||||| ||||||||||||| |||||||||||||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||| ||||||||||||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 241 AAAGTTAACGGTGTCGAATATTCAGAAACGGAGGGAGTATAAACGTCTTGTTCAGAAGTT Query 301 CAGAGATTCACCTGTCTGATGCTGATGATGATTAATTGTTTGCAACATGGATTTCAG 357 300 ||||||||||||||||||||||||||||||||||||||||||||||||||||||||| Sbjct 301 CAGAGATTCACCTGTCTGATGCTGATGATGATTAATTGTTTGCAACATGGATTTCAG 52 357 ... mutations in coding regions and 59 nucleotide mutations in noncoding regions Four point mutations in coding regions were: the deletion of T/- at position 34 and the insertion of -/T between positions... 71 in exon 3; the substitution of C/T at position 14 in exon and the substitution of T/C at position 115 in exon In 59 mutant nucleotides in non-coding regions, some significant alterations were... listed: deletions (T/-) at positions 29 and 31 in intron 3; the change (T/C) at position in intron 5; the deletions (T/-) at position 53, 59 and the deletion (A/-) at the position 63 of intron 6; the