DNA序列组建的计算机处理

本文给出了用于DNA序列组建的计算机程序,该程序能方便地将已测定的片段序列组合成一连续DNA序列。由此来组建DNA分子的一级结构。整个程序是由若干个相互关联的文件构成。在执行中它主要是通过对各片段序列中所存在的部分重叠的或者相同的子序列检索,比较后再将重叠的片段序列拼接组合成一连续的DNA序列。可以认为在长链DNA分子序列的测定中,使用这种处理是有益的。     

利用Taq DNA聚合酶直接从双链RNA模板中扩增靶序列

利用Taq DNA聚合酶既具有DNA聚合酶活性义具有反转录酶活性的特点,探索了在Taq DNA聚合酶单独作用下以双链RNA为模板进行PCR反应的条件。结果表明靶序列长度为277 bp、369 bp、987 bp时,均可直接进行PCR扩增;短片段序列扩增的退火温度在47．0℃、47．9℃、50．2℃、52．6℃、54．9℃、56．7℃条件下,均可有效扩增,而长片段序列扩增的退火温度在50．2℃、52．6℃、54．9℃、56．7℃条件下,也可扩增出相应的靶序列。这一结果提示利用Taq DNA聚合酶可以dsRNA为模板直接扩增目的片段,尤其是短片段的扩增。     

球形棕囊藻(Phaeocystis globosa)叶绿体psaA基因片段的序列分析

根据GenBank中检索到的南极棕囊藻(Phaeocystis globosa)psaA基因序列设计psaAL和psaAR引物,对球形棕囊藻(Phaeocystis globosa),的psaA基因片段进行PCR扩增并测序,获得了629bp的DNA序列。应用clustal X对球形棕囊藻P1、P2株系和南极棕囊藻的psaA基因片段序列进行比对,结果表明,球形棕囊藻psaA基因片段序列无插入／缺失,核苷酸差异率为3.34％。应用DNAstar分析软件推断球形棕囊藻和南极棕囊藻的psaA基因对应的氨基酸序列和RNA二级结构,发现它们的氨基酸序列差异不大,序列中209个氨基酸只有1个发生了变化,其氨基酸变异率为0．48％;除部分结构域比较相似外,RNA二级结构上体现一定程度的差异,这可能对棕囊藻的分子分类研究有参考价值。因所获得的psaA基因片段序列及氨基酸序列具有种的极端保守性,不适宜用作Phaeocystis属种间的分子分类研究。     

植物DNA条形码、物种形成和分类学

<正>DNA条形码最大的特点是用一段标准DNA序列就可以鉴定生物材料(特别是不具有分类特征的残缺或来自幼小个体的材料)和生物产品的物种来源(Hebert et al.,2003)。已有研究发现,动物中线粒体基因组的COI基因序列能鉴定多个动物类群的物种,被认为是理想的DNA条形码片段(Ward et al.,2005;TavaresBaker,2008)。而植物DNA条形码序列的确定还存在争议,例如应选择一个片段还是多     

基于后缀列的基因序列最大串联重复查找技术

重复序列分析在全基因组研究中起着重要作用,其首要任务就是在DNA序列中识别并定位所有的重复结构。本文提出了一种新的算法,此算法基于一种简单的数据结构——后缀数,用于查找给定的DNA序列中所有的最大串联重复。并且在该算法的基础上编写了一个有效实用的软件——RepLocate,同时给出了它应用到已知的DNA序列的实例。     

雄牛特异的SRY同源序列的扩增和分析

利用人、兔、鼠SRY序列设计引物,应用PCR扩增牛的SRY序列,获得200bp的雄牛特异的扩增片段。克隆该扩增片段,获得重组质粒pCH21,进行序列分析,并与人、兔和鼠SRY的对应区域比较,具有高度同源性。用pCH21 DNA作探针与牛的基因组DNA酶切图谱杂交,显示了雄牛特异的I．7kb的杂交带。分析200bp的PCR扩增片段是牛的SRY基因片段。用同一对引物扩增人和山羊的DNA样品,也获得雄性特异的200bp的扩增片段。     

土拨鼠肝炎病毒表面抗原基因的位置,核苷酸序列及其与人的乙型肝炎病毒表面抗原基因的比较

结果部分(摘登) 开始DNA序列分析的时候,首先要用几种最有效的限制性内切酶消化Eco WHV DHA~+,并用2％琼脂糖凝胶电泳把这些DNA片段分开。如图Ⅰ(从略见原文)所示:HaeⅢ和HinfⅠ消化的Eco WHV DNA。给出了一系列体积大小不同盼DNA片段,其中各有一个片段大于1000个碱基。用HaeⅢ和HinfⅠ混合消化时,这些大片段消失了。这     

基于变动窗口和移动序列的基因识别算法

采用信号处理技术来识别DNA碱基序列中的基因片段的方法,已经成为一种重要的基因识别途径,重新编码的DNA序列存在大量噪声信息,使得目前很多识别算法无法准确的识别外显子片段的起始位置。本研究通过对"固定长度滑动窗口-频谱曲线法"和"移动序列-信噪比法"的实现与改进,提出了一种基于变动窗口和移动序列的基因识别算法。首先,对已有基因识别算法进行编程实现;采用小波分析对识别结果进行消噪处理;探讨识别最优固定长度M的选择,提出基于变动窗口和移动序列的基因预测模型,并编程实现。最后使用该模型对已有基因序列进行识别,其识别准确度达到77.57%。     

转基因水稻T—DNA侧翼序列的扩增与分析

利用现有的转抗白叶枯病基因Xa21的水稻材料,通过TAIL－PCR技术扩增出携带Xa21基因的T－DNA的侧翼序列,对24个有效扩增片段的序列分析结果表明,其中14个侧翼序列是水稻DNA,9个含载体主干序列,1个是外源基因Xa21片段,14个T－DNA侧翼的水稻DNA序列与直接转化法外源基因整合位点的基因组序列具有不同的特点,这些T－DNA在水稻染色体上整合后其两端序列的特点类似于在转基因双子叶植物中观察到的现象,在含主干序列的侧翼序列（37．5％,9／24）,中,载体主干序列是以不同的类型出现的。     

大肠杆菌MG1655菌株ERIC-PCR图谱主带序列组成分析

ERIC-PCR已经在细菌分类,鉴定及混合菌群分析中得到广泛应用,但对其产物形成规律的认识仍存在分歧,以大肠杆菌MG1655为对象,对其ERIC-PCR指纹图谱中1.1kb主带中的DNA片段进行了克隆,测序,基因组定位以及引物匹配分析。结果表明,这条1.1kb主带由分布在基因组中不同位置的3种序列不同的片段组成,各片段的丰度差异较大,最高为97.89%;3种片段中的2种所在的基因组区域仅一端含有ERIC序列,推测对含有ERIC序列的基因组DNA进行扩增时,ERIC-PCR是一种非随机扩增。     

Comparison of Papanicolaou staining and Feulgen staining for automated prescreening

Feulgen staining is considered to be a quantitative DNA-specific cytochemical procedure. The applicability of this staining in high-resolution cytometry was tested in comparison with a regressive Papanicolaou staining. Papanicolaou-stained or Feulgen-stained intermediate and carcinoma cells selected by a cytologist were examined with a Zeiss scanning microscope photometer at 546 and 560 nm, respectively. After cell image segmentation and feature extraction, a statistical data evaluation was carried out by computer. Cell distributions with respect to four selected nuclear features demonstrated the influence of the staining procedure on cell feature measurements. The discriminatory power of the classification system as related to both staining procedures was studied using discriminant analysis. Using only nuclear features, a 7.3% improvement of the overall correct classification rate (from 85.0% to 92.3%) was achieved using Feulgen staining. The misclassification rate was simultaneously reduced by 50%. Using cytoplasmic as well as nuclear features, a 98% rate of correct classification was achieved.     

Assignment of position-specific error probability to primary DNA sequence data.

DNA sequence predicted from polyacrylamide gel-based technologies is inaccurate because of variations in the quality of the primary data due to limitations of the technology, and to sequence-specific variations due to nucleotide interactions within the DNA molecule and with the gel. The ability to recognize the probability of error in the primary data will be useful in reconstructing the target sequence of a DNA sequencing project, and in estimating the accuracy of the final sequence. This paper describes the use of linear discriminant analysis to assign position-specific probabilities of incorrect, over- and under-prediction of nucleotides for each predicted nucleotide position in primary sequence data generated by a gel-based DNA sequencing technology. Using this method, most of the error potential in primary sequence data can be assigned to a limited number of discrete positions. The use of probability values in the sequence reconstruction process, and in estimating the accuracy of consensus sequence determination is described.     

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.     

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data

Background  

Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant).     

A quantitative analysis of the human bone marrow granulocytic cell lineage using the SAMBA 200 cell image processor. I. The normal maturation sequence

A quantitative image analysis of the human normal bone marrow granulocytic line was performed using the SAMBA 200 image analyzer. The steps of image acquisition, preprocessing, segmentation and parametrization are described. Forty-one parameters were computed on 941 cell images belonging to the various maturation stages. The automated classification of these cells based upon a stepwise linear discriminant analysis resulted in 77% correctly classified cells; the five most discriminating parameters were the nuclear area, the nuclear convexity degree, the average cytoplasmic hue, the regularity of the nuclear boundary and the average cytoplasmic luminance. The evolution of the parameters correlates well with the cytologic evolution and the biochemical and functional events during the maturation process. It can be inferred from our results that the maturation sequence can be subdivided into two phases according to the evolution of the cell profiles. The first phase, from myeloblast to myelocyte, is discontinuous and appears as the critical point with regard to the expression of genes. The second phase, from myelocyte to polymorphonuclear cell, is a continuous sequence of transformations leading to the functional granulocyte.     

基于机器学习的蛋白质编码区识别

针对DNA序列编码区的识别问题,本研究提出一个特征向量和逻辑回归的组合模型。首先对DNA序列进行数值处理转化为特征向量,并结合k字符相对频率技术提取特征向量的元素特征,之后利用二分类逻辑回归算法,对编码区和非编码区进行准确区分。选取了HMR195和BG570两个基准数据集进行五折交叉验证,结果表明,平均AUC(Area Under Curve)值分别为0.981 3和0.987 4,明显优于传统的贝叶斯判别法和VOSSDFT等方法。此外,本文提出的特征向量的维度很低,提高了运算效率。因此,本文组合模型能够较为高效准确地识别蛋白质编码区。     

基于多源遥感数据的面向对象林分类型识别

林分类型的识别是森林资源监测的核心问题之一.为研究多源遥感数据协同的面向对象林分类型分类识别,采用Radarsat-2数据和QuickBird遥感影像协同进行面向对象分类.在面向对象分类过程中,采用3种分割方案:单独使用QuickBird遥感影像分割;单独使用Radarsat-2数据分割;Radarsat-2&QuickBird协同分割.3种分割方案均采用10种分割尺度(25～250,步长25),应用修正的欧式距离3指标评价不同分割方案的分割结果,确定最优分割方案及最优分割尺度.在最优分割结果的基础上,基于地形、高度、光谱及共同特征的不同特征组合,应用带有径向基(RBF)核函数的支持向量机(SVM)分类器进行杉木林、马尾松林、阔叶林3种林分类型识别.结果表明:与单独使用一种数据相比,Radarsat-2数据和QuickBird遥感影像协同方案在面向对象林分类型分类方面具有优势.Radarsat-2&QuickBird协同分割方案,以最优尺度参数100进行分割时,分割结果最好.在最优分割结果的基础上,应用两种数据源提取的全部特征进行面向对象林分类型识别的精度最高(总精度为86%,Kappa值为0.86).本研究结果不仅可为多源遥感数据结合进行林分类型识别提供参考和借鉴,而且对于森林资源调查和监测有现实意义.     

A quantitative analysis of the human bone marrow erythroblastic cell lineage using the SAMBA 200 cell image processor. I. The normal maturation sequence

A quantitative image analysis of the normal maturation sequence for the human bone marrow erythroblastic lineage was performed using the SAMBA 200 cell image processor. The different image analysis steps (image acquisition, preprocessing, segmentation, parametrization and data analysis) are briefly described. Thirty-three parameters related to geometry, color, texture and densitometry were computed on 638 cell images belonging to the five erythroblastic maturation stages. The automated classification of these cells, based upon a stepwise linear discriminant analysis, resulted in 80% correctly classified cells. Acceptance of confusions between successive maturation stages enhanced the rate of correctly classified cells to 100%. Among the ten most discriminating parameters, the nuclear area showed the highest correlation with the changes throughout the maturation process. The projection of the maturation sequence onto the factorial plane resulting from the canonical analysis emphasizes the existence of three phases of the maturation process, a finding that correlates well with the cytologic evolution and the biochemical and functional events during the maturation. The trajectory of cells within this factorial plane is thus regarded as a differentiation path from which a measure of the maturation could be derived.     

A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images.

Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.