目录
摘要Ⅲ
关键词Ⅲ
AbstractⅣ
引言
引言
第一章 文献综述1
1 RNA引导的DNA甲基化1
2 DNA甲基化的维持1
3 主动的DNA去甲基化2
第二章 材料与设备4
1 材料与试剂4
1.1 植物材料4
1.2 图像材料4
1.3 实验试剂4
2 设备与环境4
2.1 设备4
2.2 环境4
第三章 方法与流程 5
1 材料预处理5
1.1 种植拟南芥5
1.2 获得荧光图像5
1.3 去除背景5
2叶片掩模建立6
2.1读取数据6
2.2边缘识别6
2.3形态学操作7
2.4空隙填充8
2.5噪点去除8
3统计学处理9
3.1选择图像中对照组所在区域9
3.2分析对照组叶片的荧光均值分布10
3.3筛选M2叶片11
3.4附加的筛选条件11
4输出结果12
5伪彩色处理对比12
6主要函数汇总13
第四章 结果分析与讨论15
1 CFF筛选效率高15
2 CFF能发挥定量系统的优势15
3 CFF潜在的应用范围15
4 CFF的缺陷和未来需改进的方案15
致谢16
参考文献17
附录A 源代码18
附录B 使用说明22
拟南芥叶片荧光强度分布统计与自动条件筛选
摘 要
启动子区域的DNA甲基化能够调控基因表达，因此DNA甲基化作为一种重要的表观遗传机制，对于植物和动物的生长发育至关重要。生物体内的DNA甲基化水平是由DNA甲基化与DNA去甲基化共同作用维持的动态平衡。在拟南芥中，使用叶片荧光报告系统能够有效检测出DNA去甲基化作用不活跃的突变体。然而在实际操作过程中， *51今日免费论文网|www.51jrft.com +Q: *351916072* 
该系统目前使用的选择方案主观随意性太强，缺乏有效的统计学依据、难以区分效应强度不同的因子，且选择效率低下。为解决这些问题，本研究开发了一个名为Conditional Fluorescence Filter (CFF)的软件。该软件能通过边缘提取、形态学操作等方式创建叶片掩模，并以此统计原叶片区域的灰度分布。通过对大量生长时间为7天和30天的拟南芥叶片分别进行识别、统计学比对、条件约束，并与原选择方案对比，确定了CFF的优越性。使用不同参数的组合，能够有效将目标叶片限制在某个特定的效应强度区间内，从而通过荧光强度与分布模式对潜在因子进行量化分组，提高了筛选工作的准确性和效率。但当图像排列过于密集、甚至重叠时，边缘识别系统无法工作，限制了一次能处理的叶片数量，要求更精确的边缘识别算法优化。
Fluorescence Distribution of Arabidopsis thaliana leaves and Automatically Conditional Screening
ABSTRACT
The dynamic level of DNA methylation is maintained by DNA methylation and DNA demethylation. Compared with DNA methylation, the reports on the mechanism of DNA demethylation are rare. In Arabidopsis, the use of leaf fluorescence reporting system can effectively detect the mutants of DNA demethylation. However, in the experiment processes, as a quantitative system, the selection scheme currently used is too subjective and arbitrary, lack of effective statistical basis, difficult to distinguish the factors with different effect intensity, and the selection efficiency is low. In order to solve these problems, a software named Conditional Fluorescence Filter (CFF) is developed. The CFF could create the mask of leaves to analyze the distribution of fluorescence by edge detection and morphology operation. Compared with former criteria, the results of recognition, statistics and condition limits of 7 and 30 days leaves generated with CFF are better. The combination of different parameters can effectively limit the target leaves to a specific effect intensity range, so that the potential factors can be quantized and grouped by fluorescence intensity and distribution mode, and thus improves the accuracy and efficiency of the screening work. However, the edge detection system will not work if the image arrangement is too dense or even overlapped. Therefore, the number of leaves screened at one time is limited and more accurate edge detection algorithm is required for optimizing.

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