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An Algorithm for Gene Fragment Reconstruction.

ABSTRACT: Gene sequencing technology has been playing an important role in many aspects, such as life science, disease medicine and health medicine, particularly in the extremely tough process of fighting against 2019-novel coronavirus. Drawing DNA restriction map is a particularly important technology in genetic biology. The simplified partial digestion method (SPDP), a biological method, has been widely used to cut DNA molecules into DNA fragments and obtain the biological information of each fragment. In this work, we propose an algorithm based on 0-1 planning for the location of restriction sites on a DNA molecule, which is able to solve the problem of DNA fragment reconstruction just based on data of fragments' length. Two specific examples are presented in detail. Furthermore, based on 1000 groups of original DNA sequences randomly generated, we define the coincidence rate and unique coincidence rate between the reconstructed DNA sequence and the original DNA sequence, and then analyze separately the effect of the number of fragments and the maximum length of DNA fragments on the coincidence rate and unique coincidence rate as defined. The effectiveness of the algorithm is proved. Besides, based on the existing optimization solution obtained, we simulate and discuss the influence of the error by computation method. It turns out that the error of position of one restriction site does not affect other restriction sites and errors of most restriction sites may lead to the failure of sequence reconstruction. Matlab 7.1 program is used to solve feasible solutions of the location of restriction sites, derive DNA fragment sequence and carry out the statistical analysis and error analysis. This paper focuses on basic computer algorithm implementation of rearrangement and sequencing rather than biochemical technology. The innovative application of the mathematical idea of 0-1 planning to DNA sequence mapping construction, to a certain extent, greatly simplifies the difficulty and complexity of calculation and accelerates the process of 'jigsaw' of DNA fragments.

SUBMITTER: Fang N

PROVIDER: S-EPMC7896547 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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An Algorithm for Gene Fragment Reconstruction.

Fang Ningyuan N Wang Kaifa K Tong Dali D

Interdisciplinary sciences, computational life sciences 20210220 1

Gene sequencing technology has been playing an important role in many aspects, such as life science, disease medicine and health medicine, particularly in the extremely tough process of fighting against 2019-novel coronavirus. Drawing DNA restriction map is a particularly important technology in genetic biology. The simplified partial digestion method (SPDP), a biological method, has been widely used to cut DNA molecules into DNA fragments and obtain the biological information of each fragment. ...[more]

PMID: 33609237

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Project description:BackgroundComputed tomography (CT) is an advanced medical imaging technology. The images obtained by CT are helpful for improving diagnostic accuracy. Currently, CT is widely used in clinical settings for diagnosis and health examinations. However, full angle CT scanning has the disadvantage of causing radiation damage to the human body. Sparse angle projection CT scanning is the most effective way to minimize this damage, but the quality of the reconstructed image is reduced. Therefore, it is important to improve the reconstructed image quality produced by sparse angle projection.MethodsIn this paper, we focused on the algebraic reconstruction algorithm. To reduce the accumulation of random noise, we formulated a modified algebraic reconstruction algorithm. Firstly, the algebraic reconstruction algorithm was used to compute two consecutive results, and then the weighted sum of these two results was used to correct the reconstructed image, and an iterative result was obtained. Using this method, we aimed to reduce the noise accumulation caused by iteration.ResultsIn this study, 20 angle projections were used for the reconstruction. The experimental object was the Shepp-Logan phantom test image. The experiments were implemented under two conditions: without noise and with noise. The peak signal to noise ratio (PSNR) and the mean squared error (MSE) of the reconstructed image from projections without noise were 76.0896 and 0.0016, respectively. The PSNR and MSE of the reconstructed image from projections with noise were 75.8263 and 0.0017, respectively. The reconstructed performance was superior to the previous algebraic reconstruction algorithm.ConclusionsThe performance of the proposed method was superior to other algorithms, which confirms that noise accumulation caused by iteration can be effectively reduced by the weighted summation of two consecutive reconstruction results. Moreover, the reconstruction performance under noisy projection is superior to other algorithms, which demonstrates that the proposed method improves anti-noise performance.

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An Algorithm for Gene Fragment Reconstruction.

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An Algorithm for Gene Fragment Reconstruction.

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