MBDDiff: an R package designed specifically for processing MBDcap-seq datasets

Liu, Yuanhang; Wilson, Desiree; Leach, Robin J.; Chen, Yidong

doi:10.1186/s12864-016-2794-z

Cited by 3 publications

(1 citation statement)

References 24 publications

(27 reference statements)

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“…Still, we do not explicitly relate the unsupervised uniformed probe regions we use with specific SEs. Furthermore, in the future, we are going to compare with modern algorithms, such as MBDDiff 41 .…”

Section: Discussionmentioning

confidence: 99%

Differentially Methylated Super-Enhancers Regulate Target Gene Expression in Human Cancer

Flam

Danilova

Kelley

et al. 2019

Sci Rep

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Current literature suggests that epigenetically regulated super-enhancers (SEs) are drivers of aberrant gene expression in cancers. Many tumor types are still missing chromatin data to define cancer-specific SEs and their role in carcinogenesis. In this work, we develop a simple pipeline, which can utilize chromatin data from etiologically similar tumors to discover tissue-specific SEs and their target genes using gene expression and DNA methylation data. As an example, we applied our pipeline to human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV + OPSCC). This tumor type is characterized by abundant gene expression changes, which cannot be explained by genetic alterations alone. Chromatin data are still limited for this disease, so we used 3627 SE elements from public domain data for closely related tissues, including normal and tumor lung, and cervical cancer cell lines. We integrated the available DNA methylation and gene expression data for HPV + OPSCC samples to filter the candidate SEs to identify functional SEs and their affected targets, which are essential for cancer development. Overall, we found 159 differentially methylated SEs, including 87 SEs that actively regulate expression of 150 nearby genes (211 SE-gene pairs) in HPV + OPSCC. Of these, 132 SE-gene pairs were validated in a related TCGA cohort. Pathway analysis revealed that the SE-regulated genes were associated with pathways known to regulate nasopharyngeal, breast, melanoma, and bladder carcinogenesis and are regulated by the epigenetic landscape in those cancers. Thus, we propose that gene expression in HPV + OPSCC may be controlled by epigenetic alterations in SE elements, which are common between related tissues. Our pipeline can utilize a diversity of data inputs and can be further adapted to SE analysis of diseased and non-diseased tissues from different organisms.

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Section: Discussionmentioning

confidence: 99%

Differentially Methylated Super-Enhancers Regulate Target Gene Expression in Human Cancer

Flam

Danilova

Kelley

et al. 2019

Sci Rep

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A model of pulldown alignments from SssI-treated DNA improves DNA methylation prediction

2019

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Background Protein pulldown using Methyl-CpG binding domain (MBD) proteins followed by high-throughput sequencing is a common method to determine DNA methylation. Algorithms have been developed to estimate absolute methylation level from read coverage generated by affinity enrichment-based techniques, but the most accurate one for MBD-seq data requires additional data from an SssI-treated Control experiment. Results Using our previous characterizations of Methyl-CpG/MBD2 binding in the context of an MBD pulldown experiment, we build a model of expected MBD pulldown reads as drawn from SssI-treated DNA. We use the program BayMeth to evaluate the effectiveness of this model by substituting calculated SssI Control data for the observed SssI Control data. By comparing methylation predictions against those from an RRBS data set, we find that BayMeth run with our modeled SssI Control data performs better than BayMeth run with observed SssI Control data, on both 100 bp and 10 bp windows. Adapting the model to an external data set solely by changing the average fragment length, our calculated data still informs the BayMeth program to a similar level as observed data in predicting methylation state on a pulldown data set with matching WGBS estimates. Conclusion In both internal and external MBD pulldown data sets tested in this study, BayMeth used with our modeled pulldown coverage performs better than BayMeth run without the inclusion of any estimate of SssI Control pulldown, and is comparable to – and in some cases better than – using observed SssI Control data with the BayMeth program. Thus, our MBD pulldown alignment model can improve methylation predictions without the need to perform additional control experiments. Electronic supplementary material The online version of this article (10.1186/s12859-019-3011-2) contains supplementary material, which is available to authorized users.

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Whole Transcriptome-Based Study to Speculate upon the Silkworm Yellow Blood Inhibitor (I) Gene and Analyze the miRNA-Mediated Gene Regulatory Network

Fan

et al. 2022

Processes

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White cocoon is developed and used as a natural fiber, and different silkworm strains have different cocoon colors. Natural-colored cocoons are preferred by people, however, the cocoon color mainly settles on sericin and it basically falls off after reeling. Currently, there are no varieties applied to production due to the formation mechanism of cocoon color is not clear. The formation of cocoon color involves multiple gene regulations. Previous studies have shown that the main genes regulating cocoon traits are the yellow blood (Y) gene, yellow blood inhibitor (I) gene, and yellow cocoon (C) gene. Among them, the products of the Y gene and C gene have been studied, but the I gene is still unclear. In this study, the midgut tissues of the yellow (NB) and the white (306) cocoon silkworm were analyzed by whole transcriptome sequencing. The results showed that there are 1639 DE-circRNAs, 70 DE-miRNAs, and 3225 DE-mRNAs, including 1785 up-regulated genes and 1440 down-regulated genes. GO and KEGG annotation results indicated that DE-mRNAs are mainly involved in intracellular transport, signal transduction, lipid transport, and metabolic processes. Two key genes, KWMTBOMO10339 and KWMTBOMO16553, were screened out according to the annotation results, which were involved in amino acid transport and ion exchange function, respectively. The interaction analysis between ncRNA and target genes showed that there were five miRNAs regulating these two genes. The qPCR analysis showed that the I gene was down-regulated, and the miRNA expression profiles were most up-regulated. Therefore, during the yellow and white cocoon formation, KWMTBOMO10339 and KWMTBOMO16553 may be regulated by miRNA, resulting in the non-expression of KWMTBOMO10339 and KWMTBOMO16553 in yellow cocoon silkworm, and the pigment molecules can enter hemolymph from the midgut to form yellow blood, then transport to the middle silk gland to finally form yellow cocoons.

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MBDDiff: an R package designed specifically for processing MBDcap-seq datasets

Cited by 3 publications

References 24 publications

Differentially Methylated Super-Enhancers Regulate Target Gene Expression in Human Cancer

Differentially Methylated Super-Enhancers Regulate Target Gene Expression in Human Cancer

A model of pulldown alignments from SssI-treated DNA improves DNA methylation prediction

Whole Transcriptome-Based Study to Speculate upon the Silkworm Yellow Blood Inhibitor (I) Gene and Analyze the miRNA-Mediated Gene Regulatory Network

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