DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes

Pavlović, Miloš; Ray, Pradipta; Pavlović, Kristina; Kotamarti, Aaron; Chen, Min; Zhang, Michael Q.

doi:10.1093/bioinformatics/btx316

Cited by 18 publications

(16 citation statements)

References 43 publications

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“…It is worth noting that also in previous works [ 53 – 58 ] DNA methylation has been used to classify data samples (and patients) of cancer, but only a subset of single methylated sites have been used as features. Recently, the authors of [ 59 ] perform the classification task by considering all the single methylated sites in the genome with big data techniques.…”

Section: Methodsmentioning

confidence: 99%

Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction

2018

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BackgroundIn the Next Generation Sequencing (NGS) era a large amount of biological data is being sequenced, analyzed, and stored in many public databases, whose interoperability is often required to allow an enhanced accessibility. The combination of heterogeneous NGS genomic data is an open challenge: the analysis of data from different experiments is a fundamental practice for the study of diseases. In this work, we propose to combine DNA methylation and RNA sequencing NGS experiments at gene level for supervised knowledge extraction in cancer.MethodsWe retrieve DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas (TCGA), focusing on the Breast Invasive Carcinoma (BRCA), the Thyroid Carcinoma (THCA), and the Kidney Renal Papillary Cell Carcinoma (KIRP). We combine the RNA sequencing gene expression values with the gene methylation quantity, as a new measure that we define for representing the methylation quantity associated to a gene. Additionally, we propose to analyze the combined data through tree- and rule-based classification algorithms (C4.5, Random Forest, RIPPER, and CAMUR).ResultsWe extract more than 15,000 classification models (composed of gene sets), which allow to distinguish the tumoral samples from the normal ones with an average accuracy of 95%. From the integrated experiments we obtain about 5000 classification models that consider both the gene measures related to the RNA sequencing and the DNA methylation experiments.ConclusionsWe compare the sets of genes obtained from the classifications on RNA sequencing and DNA methylation data with the genes obtained from the integration of the two experiments. The comparison results in several genes that are in common among the single experiments and the integrated ones (733 for BRCA, 35 for KIRP, and 861 for THCA) and 509 genes that are in common among the different experiments. Finally, we investigate the possible relationships among the different analyzed tumors by extracting a core set of 13 genes that appear in all tumors. A preliminary functional analysis confirms the relation of part of those genes (5 out of 13 and 279 out of 509) with cancer, suggesting to focus further studies on the new individuated ones.Electronic supplementary materialThe online version of this article (10.1186/s13040-018-0184-6) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction

2018

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“…The characteristics of methylation sites are inevitably digitized when using computational methods that identify them. Many previous studies [13–16] have demonstrated that the sequence of neighboring nucleotides of one methylation site is specific and that the methylation state is closely related to the sequence information, which allows for the prediction of the methylation state only based on the sequence composition. The Methylator method [14] proposed by Bhasin et al used conventional binary sparse encoding to directly convert sequences into a feature vector.…”

Section: Introductionmentioning

confidence: 99%

LightCpG: a multi-view CpG sites detection on single-cell whole genome sequence data

Jiang

Wang²,

Tang

et al. 2019

BMC Genomics

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Background DNA methylation plays an important role in multiple biological processes that are closely related to human health. The study of DNA methylation can provide an insight into the mechanism behind human health and can also have a positive effect on the assessment of human health status. However, the available sequencing technology is limited by incomplete CpG coverage. Therefore, it is crucial to discover an efficient and convenient method capable of distinguishing between the states of CpG sites. Previous studies focused on identifying methylation states of the CpG sites in single cell, which only evaluated sequence information or structural information. Results In this paper, we propose a novel model, LightCpG, which combines the positional features with the sequence and structural features to provide information on the CpG sites at two stages. Next, we used the LightGBM model for training of the CpG site identification, and further utilized sample extraction and merged features to reduce the training time. Our results indicate that our method achieves outstanding performance in recognition of DNA methylation. The average AUC values of our method using the 25 human hepatocellular carcinoma cells (HCC) cell datasets and six human heptoplastoma-derived (HepG2) cell datasets were 0.9616 and 0.9213, respectively. Moreover, the average training times for our method on the HCC and HepG2 datasets were 8.3 and 5.06 s, respectively. Furthermore, the computational complexity of our model was much lower compared with other available methods that detect methylation states of the CpG sites. Conclusions In summary, LightCpG is an accurate model for identifying the DNA methylation status of CpG sites in single cells. Furthermore, three types of feature extraction methods and two strategies used in LightCpG are helpful for other prediction problems. Electronic supplementary material The online version of this article (10.1186/s12864-019-5654-9) contains supplementary material, which is available to authorized users.

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“…Feature selection techniques have been successfully applied in many real-world applications, such as large-scale biological data analysis [ [24] , [25] , [26] ], text classification [ 27 ], information retrieval [ 28 ], near-infrared spectroscopy [ 29 ], mass spectroscopy data analysis [ 30 ], drug design [ 31 , 32 ], and especially the quantitative structure-activity relationship (QSAR) modeling [ 33 , 34 ]. In cancer research community, feature selection has also been widely applied in different omics data analyses: mRNA data [ 9 , 35 ], miRNA data [ 36 , 37 ], whole exome sequencing data [ 38 ], DNA-methylation data [ 39 , 40 ], and proteomics data [ 41 , 42 ]. Recently, some researchers have applied feature selection techniques on integrative analysis of multi-omics data.…”

Section: Feature Selection Techniquesmentioning

confidence: 99%

A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis

Liang

Yang

et al. 2018

Computational and Structural Biotechnology Journal

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With the rapid accumulation of gene expression data from various technologies, e.g., microarray, RNA-sequencing (RNA-seq), and single-cell RNA-seq, it is necessary to carry out dimensional reduction and feature (signature genes) selection in support of making sense out of such high dimensional data. These computational methods significantly facilitate further data analysis and interpretation, such as gene function enrichment analysis, cancer biomarker detection, and drug targeting identification in precision medicine. Although numerous methods have been developed for feature selection in bioinformatics, it is still a challenge to choose the appropriate methods for a specific problem and seek for the most reasonable ranking features. Meanwhile, the paired gene expression data under matched case-control design (MCCD) is becoming increasingly popular, which has often been used in multi-omics integration studies and may increase feature selection efficiency by offsetting similar distributions of confounding features. The appropriate feature selection methods specifically designed for the paired data, which is named as matched-pairs feature selection (MPFS), however, have not been maturely developed in parallel. In this review, we compare the performance of 10 feature-selection methods (eight MPFS methods and two traditional unpaired methods) on two real datasets by applied three classification methods, and analyze the algorithm complexity of these methods through the running of their programs. This review aims to induce and comprehensively present the MPFS in such a way that readers can easily understand its characteristics and get a clue in selecting the appropriate methods for their analyses.

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DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 18 publications

References 43 publications

Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction

Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction

LightCpG: a multi-view CpG sites detection on single-cell whole genome sequence data

A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis

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