Visualization of DNA methylation results through a GPU-based parallelization of the wavelet transform

Different statistical approaches have been proposed last years for finding differentially methylated DNA regions, starting from the outputs of DNA methylation analysis tools. However, these approaches do not allow an interactive and flexible exploration of these regions. Additionally, they add a high computation workload when used with large datasets. In this paper, we propose a new approach consisting in the transformation of DNA methylation results into a methylation signal and the Haar wavelet transformation of that signal for the displaying of the methylation results at different scales. Additionally, we propose the parallelization of the Haar wavelet transform on the GPU, as well as the GPU-based visualization of the methylation signal. The performance evaluation results show that this is the first proposal which allows the interactive visualization of different methylation signals with different resolution levels, in such a way that it can be used to visually detect differentially methylated regions accurately, in a user-friendly and flexible way, and with a very low computational workload.

     

Imputing DNA Methylation by Transferred Learning Based Neural Network

DNA methylation is one important epigenetic type to play a vital role in many diseases including cancers. With the development of the high-throughput sequencing technology, there is much progress to disclose the relations of DNA methylation with diseases. However, the analyses of DNA methylation data are challenging due to the missing values caused by the limitations of current techniques. While many methods have been developed to impute the missing values, these methods are mostly based on the correlations between individual samples, and thus are limited for the abnormal samples in cancers. In this study, we present a novel transfer learning based neural network to impute missing DNA methylation data, namely the TDimpute-DNAmeth method. The method learns common relations between DNA methylation from pan-cancer samples, and then fine-tunes the learned relations over each specific cancer type for imputing the missing data. Tested on 16 cancer datasets, our method was shown to outperform other commonly-used methods. Further analyses indicated that DNA methylation is related to cancer survival and thus can be used as a biomarker of cancer prognosis.     

变分自编码器对甲基化缺失数据的填补

针对高通量测序技术因各种原因导致的DNA甲基化测序数据中包含部分缺失值的问题。提出一种基于变分自编码器的DNA甲基化缺失数据填补模型VAE-MethImp。VAE-MethImp是一种深度隐含空间生成模型,由编码层、隐含层和解码层组成,拥有强大的重构输入数据能力。编码层进行均值和方差的推断;隐含层是通过编码层输出的均值和方差计算出的输入数据的专属正态分布;解码层对隐含层包含的特征进行解码生成重构后的数据。通过在肺癌和乳腺癌上的填补实验证明,VAE-MethImp提取的特征更具信息性。在填补精度上,VAE-MethImp比对照方法（均值（Mean）、最近邻（KNN）、主成分分析（PCA）和奇异值分解（SVD））中最优的SVD提升了4.8%。生存分析实验结果显示VAE-MethImp填补的数据具有更好的预测性,同时也证明DNA甲基化与癌症的生存存在直接关联。     

A DNA methylation assay for detection of ovarian cancer cells using a HpaII/MspI digestion-based PCR assay in an integrated microfluidic system

Early and accurate diagnosis of cancer plays a very important role in favorable clinical outcomes. DNA methylation of tumor suppressor genes has been recognized as a diagnostic biomarker for early carcinogenesis. The presence of 5-methylcytosine in the CpG islands in the promoter region of a tumor suppressor gene is an important indicator of DNA methylation. However, the standard detection assay utilizing a bisulfite treatment and HpaII/MspI endonuclease digestion is a tedious and lengthy process and requires a relatively large amount of DNA for testing. In this study, the methylated DNAs of various tumor suppressor genes, HAAO, HOXA9 and SFRP5, were chosen as candidates for detection of ovarian cancer cells. The entire experimental process for the DNA methylation assay, including target DNA isolation, HpaII/MspI endonuclease digestion, and nucleic acid amplification has been realized in an integrated microfluidic system. The limit of detection using this developed system has been experimentally determined to be 10² cells/reaction. The entire process from sample loading to analysis of the results only took 3 h which is much faster than the existing protocols. Different sources of biosamples, such as cells, ascites and serums, could be detected with the methylated DNA, indicating that this developed microfluidic system could be adapted for clinical use. Thus, this developed microsystem may be a promising platform for the rapid and early diagnosis of cancers.     

导向定位测序数据的甲基化序列比对算法优化

导向定位测序(GPS)是一种全基因组DNA甲基化检测的新测序技术,产生的测序数据具有成本低、没有序列偏好等优势.目前,甲基化分析中最重要的一步是将其测序产生的序列比对到参考基因组上.但是,现有导向定位测序的方法使用Smith-Waterman进行局部序列比对,时间消耗过大且容易对序列比对位置产生误判.因此,提出一种导向定位测序数据的改进比对算法,该算法利用其双端测序的优势,先用甲基化序列端数据进行序列比对,对多位置匹配的序列再利用常规数据端数据进行比对位置确定.实验结果表明:本文方法和现有方法的准确率相当,而具有更高的唯一比对比率,时间性能有3倍以上的提升.     

DNA甲基化差异模式识别方法综述

目前,微阵列芯片技术和重亚硫酸氢盐测序技术贡献了大量DNA甲基化实验数据,基于不同数据产生了众多识别差异甲基化位点及差异甲基化区域的方法。为了对DNA甲基化差异模式识别方法进行梳理,首先介绍了DNA甲基化研究现状,包括DNA甲基化检测方法和数据类型,以及两种DNA甲基化差异模式;接着详细阐述了芯片数据的差异甲基化位点和差异甲基化区域的识别方法,并介绍了基于八种不同算法原理的测序数据的差异甲基化模式识别方法,重点阐述了各种算法的原理、应用场景以及算法的优点和局限性;最后指出了现阶段DNA甲基化差异模式识别存在的问题和未来可能的发展趋势。     

Challenges in Understanding Genome-Wide DNA Methylation

DNA methylation is a chemical modification of the bases in genomes. This modification, most frequently found at CpG dinucleotides in eukaryotes, has been identified as having multiple critical functions in broad and diverse species of animals and plants, while mysteriously appears to be lacking from several other well-studied species. DNA methylation has well known and important roles in genome stability and defense, its pattern change highly correlates with gene regulation. Much evidence has linked abnorma...     

Molecular interaction

We introduce and study abstract structures which are suitable for expressing molecular interaction. The abstract structures are able to manage shared resources and to describe the use of shared resources. We show that these structures can provide an interpretation of the π-calculus, a known calculus of communicating concurrent systems. We briefly describe DNA methylation by using the π-calculus. Molecular interactions during DNA methylation imply changes of conformation and other modifications; these changes can be modelled by substitutions. Formally, we use some notions and results of the concurrency theory, particularly related to the π-calculus and multiset semantics.     

基于深度森林和DNA甲基化的癌症分类研究

作为人类基因组重要的表观遗传现象，DNA甲基化对基因的表达发挥着重要的调控作用，与癌症的关系密切。针对癌症基因组图谱（TCGA）庞大数据的类不平衡和高维度，致使假阴率大幅增加的问题，提出了一种混合采样的不平衡数据集成分类算法，使用合成少数过采样（SMOTE）算法生成新的少数类样本，得到扩充后的数据集，通过Tomek Link算法剔除样本扩充过程中引入的噪声，得到相对平衡的数据集。在此基础上，利用深度森林（gcForest）算法的级联森林结构，每一层选取两种随机森林结构，以增强模型的泛化能力，得到最终的分类模型。对6种癌症的DNA甲基化数据实验表明混合采样的不平衡数据集成分类算法在保证多数类分类精度的前提下，有效地提高了对于少数类的灵敏度。     

Simultaneous metabolic labeling of cells with multiple amino acids: Localization and dynamics of histone acetylation and methylation

Simultaneous metabolic labeling of cells with multiple amino acids combined with acetic acid urea-PAGE and MS was used to characterize histones in Kasumi-1 cells treated with the histone deacetylase inhibitor depsipeptide (DDP). The approach allowed for rapid targeting, identification, and subsequent characterization of peptides containing sites of acetylation or methylation. Multiple methylation sites were determined for histone H3 including: di- and tri-methylation of K9 and K27; mono- and di-methylation of K36 and K79; and mono-methylation of K37. The acetylation patterns for histones H4 and H3 were established. Quantitative analysis of the modification change after treatment with DDP was also performed and the dynamics of H4 acetylation determined. Functional analysis by RT-PCR showed that DDP unregulated p21 expression with a maximum after 18-h exposure. Chromatin immunoprecipitation experiments indicated that DDP treatment caused an accumulation of hyperacetylated histone H4 and H3 isoforms and a decrease in K9 di-methylation of H3 on the p21 promoter.     

Isoform-specific expression and characterization of 14-3-3 proteins in human glioma tissues discovered by stable isotope labeling with amino acids in cell culture-based proteomic analysis

Human 14-3-3 proteins have isoform-specific expression and functions in different kinds of normal or tumor cells and tissues. However, the expression profiling of 14-3-3 proteins and isoform-specific biological functions are unclear in human glioma so far. In our study, the expression levels and characterization of 14-3-3 isoforms in human glioma tissues were investigated by a sensitive, accurate stable isotope labeling with amino acids in cell culture-based quantitative proteomic strategy. As a result, except unexpressed 14-3-3σ, the other six isoforms, with different expression levels, were existed in glioma tissues and para-cancerous brain tissues (PBTs). 14-3-3β and η were upregulated, whereas 14-3-3ζ was downregulated in glioma tissues compared with that in PBTs. And the other three isoforms 14-3-3ε, θ, and γ had similar expression levels in human glioma tissues and PBTs. Western blot and immunohistochemistry analysis were both consistent with the quantitative proteomic data. The loss of expression of 14-3-3σ was further discovered due to DNA high methylation in its coding region in glioma by methylation-specific PCR analysis. These results indicated that the four isoforms, including 14-3-3β, η, ζ, and σ, may play important roles in tumorigenesis of human glioma, which is probably used as potential biomarkers for diagnosis and targets for treatment of human gliomas in future.     

基于信息量的DNA序列相似性分析

针对传统方法在分析DNA序列相似性方面的不足, 提出了一种新的基于信息量的DNA序列相似性分析算法, 该方法将DNA序列视为基于符号集{A, C, G, T}的信号序列, 全部待比较的DNA序列组合成一个以字符A、C、G、T为属性值的信息系统。在所得数据库系统中引进DNA序列的信息量、联合信息量、条件信息量、交互信息量等概念, 讨论这些信息量的性质并给出它们之间的一些关系式, 然后在此基础上构建DNA序列相似性分析模型。仿真实验结果表明, 该方法不但能快速、有效地分析DNA序列相似性, 而且较好地克服了DNA碱基数量很大且不同物种的DNA序列长短不同的不足。     

Similarity/dissimilarity calculation methods of DNA sequences: A survey

DNA sequence similarity/dissimilarity analysis is a fundamental task in computational biology, which is used to analyze the similarity of different DNA sequences for learning their evolutionary relationships. In past decades, a large number of similarity analysis methods for DNA sequence have been proposed due to the ever-growing demands. In order to learn the advances of DNA sequence similarity analysis, we make a survey and try to promote the development of this field. In this paper, we first introduce the related knowledge of DNA similarities analysis, including the data sets, similarities distance and output data. Then, we review recent algorithmic developments for DNA similarity analysis to represent a survey of the art in this field. At last, we summarize the corresponding tendencies and challenges in this research field. This survey concludes that although various DNA similarity analysis methods have been proposed, there still exist several further improvements or potential research directions in this field.     

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.     

Analysis of microdensitometric data in terms of probability of cleavage of DNA

Computer-aided analysis of autoradiographic films of DNA fragments is presented. The Powell least-squares procedure is used for optimization of parameters for components of complex densitometric curves. Since each densitometric spectrum may be divided for several non-overlapped blocks of bands, there is no upper limit on the number of parameters which must be optimized. Eight shapes for the component bands are utilized: symmetric and asymmetric Gauss and Cauchy functions, direct, symmetric and asymmetric product of Gauss function and inverse of Cauchy function, and log-normal function. The probability of DNA cleavage is calculated with correction for multiple cuts. The methods presented was applied to detailed analysis of densitometric spectra of a 21-bp DNA restriction fragment and allowed for direct correlation between structural microheterogeneity of DNA and the resulting cutting pattern. This method should facilitate the analysis of densitometric data from antibiotic-induced cleavage of DNA and footprinting experiments.     

DNA生物传感器的进展

DNA生物传感器是分子生物学与微电子学、电化学、光学等相结合的产物 ,它将在生命科学与信息科学之间架起一道桥梁 ,成为DNA信息分析检测最重要的技术之一。简要介绍了电化学DNA生物传感器、压电石英晶体DNA生物传感器、光学DNA生物传感器的结构原理     

Pattern recognition techniques for the emerging field of bioinformatics: A review

The emerging field of bioinformatics has recently created much interest in the computer science and engineering communities. With the wealth of sequence data in many public online databases and the huge amount of data generated from the Human Genome Project, computer analysis has become indispensable. This calls for novel algorithms and opens up new areas of applications for many pattern recognition techniques. In this article, we review two major avenues of research in bioinformatics, namely DNA sequence analysis and DNA microarray data analysis. In DNA sequence analysis, we focus on the topics of sequence comparison and gene recognition. For DNA microarray data analysis, we discuss key issues such as image analysis for gene expression data extraction, data pre-processing, clustering analysis for pattern discovery and gene expression time series data analysis. We describe current methods and show how computational techniques could be useful in these areas. It is our hope that this review article could demonstrate how the pattern recognition community could have an impact on the fascinating and challenging area of genomic research.