Simultaneous Methylation-Level Assessment of Hundreds of CpG Sites by Targeted Bisulfite PCR Sequencing (TBPseq)

Jeon, Kyuheum; Min, Byungkuk; Kang, Yong‐Kook

doi:10.3389/fgene.2017.00097

Cited by 7 publications

(7 citation statements)

References 44 publications

(47 reference statements)

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“…As a drawback of the MBD‐seq method, it is semiquantitative in the sense that it does not yield direct estimates of CpG methylation levels. Thus, the previously suggested pipeline of methylome study (Aberg et al, ) looks relevant: MBD‐seq offers a first pass at global methylation analysis, and then, the identified genomic loci of interest are subject to a base‐level validation using technologies, such as pyrosequencing and targeted bisulfite PCR sequencing (TBP‐seq; Jeon, Min, Park, & Kang, ).…”

Section: Discussionmentioning

confidence: 99%

Demethylation and derepression of genomic retroelements in the skeletal muscles of aged mice

2019

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Changes in DNA methylation influence the aging process and contribute to aging phenotypes, but few studies have been conducted on DNA methylation changes in conjunction with skeletal muscle aging. We explored the DNA methylation changes in a variety of retroelement families throughout aging (at 2, 20, and 28 months of age) in murine skeletal muscles by methyl‐binding domain sequencing (MBD‐seq). The two following contrasting patterns were observed among the members of each repeat family in superaged mice: (a) hypermethylation in weakly methylated retroelement copies and (b) hypomethylation in copies with relatively stronger methylation levels, representing a pattern of “regression toward the mean” within a single retroelement family. Interestingly, these patterns depended on the sizes of the copies. While the majority of the elements showed a slight increase in methylation, the larger copies (>5 kb) displayed evident demethylation. All these changes were not observed in T cells. RNA sequencing revealed a global derepression of retroelements during the late phase of aging (between 20 and 28 months of age), which temporally coincided with retroelement demethylation. Following this methylation drift trend of “regression toward the mean,” aging tended to progressively lose the preexisting methylation differences and local patterns in the genomic regions that had been elaborately established during the early period of development.

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Section: Discussionmentioning

confidence: 99%

Demethylation and derepression of genomic retroelements in the skeletal muscles of aged mice

2019

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“…Jeon et al . presented a targeted bisulfite PCR sequencing technology for the assessment of methylation levels of CpGs residing at promoters of cancer‐associated genes over the genome 15 . It can be observed that CpGs at LIFR gene promoter, the known hypermethylated region in CRC, were heavily methylated in colon cancer cells while it is not observable in other four types of cancer cells.…”

Section: Sequencing Methods For Dna With Different Derivationsmentioning

confidence: 99%

“…There are four widely adopted methods to characterize genome‐scale patterns of DNA methylation: (i) WGBS; (ii) Infinium HumanMethylation450 BeadChip; (iii) reduced representation bisulfite sequencing; and (iv) methylated DNA binding domain sequencing and methylation DNA immunoprecipitation sequencing 15 . Among those methods, a recent study combined methylation DNA immunoprecipitation sequencing and machine learning to detect and localize cancer tissues including CRC.…”

Section: Sequencing Methods For Dna With Different Derivationsmentioning

confidence: 99%

“…17 There are four widely adopted methods to characterize genome-scale patterns of DNA methylation: (i) WGBS; (ii) Infinium HumanMethylation450 BeadChip; (iii) reduced representation bisulfite sequencing; and (iv) methylated DNA binding domain sequencing and methylation DNA immunoprecipitation sequencing. 15 Among those methods, a recent study combined methylation DNA immunoprecipitation sequencing and machine learning to detect and localize cancer tissues including CRC. Current methods to detect and classify early-stage cancer by mutations of cfDNA in plasma are still of low sensitivity because the available recurrent mutations in cfDNA that can be used to distinguish cancer samples from healthy samples are limited.…”

Section: Sequencing Methods For Dna With Different Derivationsmentioning

confidence: 99%

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Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome

Zhang

Chen

Wong

2021

J of Gastro and Hepatol

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The maturing development in artificial intelligence (AI) and genomics has propelled the advances in intestinal diseases including intestinal cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). On the other hand, colorectal cancer is the second most deadly and the third most common type of cancer in the world according to GLOBOCAN 2020 data. The mechanisms behind IBD and IBS are still speculative. The conventional methods to identify colorectal cancer, IBD, and IBS are based on endoscopy or colonoscopy to identify lesions. However, it is invasive, demanding, and time‐consuming for early‐stage intestinal diseases. To address those problems, new strategies based on blood and/or human microbiome in gut, colon, or even feces were developed; those methods took advantage of high‐throughput sequencing and machine learning approaches. In this review, we summarize the recent research and methods to diagnose intestinal diseases with machine learning technologies based on cell‐free DNA and microbiome data generated by amplicon sequencing or whole‐genome sequencing. Those methods play an important role in not only intestinal disease diagnosis but also therapy development in the near future.

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“…LIFR(p=4.90e-04) had 4 hit papers and encodes protein that belongs to the type I cytokine receptor family. One of the studies used the meta-analysis with public cancer methylome data verified the colon cancer specificity of LIFR promoter methylation (40). Kim et al suggested that a missense mutation of LIFR rs3729740 may be useful as a biomarker for predicting whether metastatic colorectal adenocarcinoma patients were sensitive to relevant target regimens (41).…”

Section: Genes Significant Only In An Individual Cancermentioning

confidence: 99%

WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

Jiang

Zheng

Kwan

et al. 2018

Preprint

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Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools. Running title: powerful cancer-driver gene detection methodIn the present study, we built a model to predict high-frequency cancer driver potential to use as prior weights, by the random forest trained in a large cancer somatic mutation database, COSMIC (V83). (See details in the supplementary notes). One can also use other methods to produce the prior weights. Tier III: A schedule of integrate independent reference samples to stabilize the regression model for small samplesWhen the sample size is small, it is difficult to build a stable regression model. Note that the key idea of ITER and WITER is to build a prediction model for background passenger genes. When the mutation rates of passenger genes of two cancers are similar, it may be workable to integrate background genes of one cancer into another cancer. We proposed a reference sample strategy for construct a stable ITER or WITER model in small samples. This is carried out at two stages. i. The above ITER or WITER is used to produce p-values for excess of somatic mutations at genes in a reference sample which have enough variants. Genes with p-values less than a very loose cutoff, say FDR 0.3, are then excluded. ii. The somatic mutations of retained genes are integrated with the local small sample and input into ITER or WITER to build a new model. The excess of somatic mutations and corresponding p-values at genes are calculated based on the new model. Curation of cancer-specific predictors of somatic mutationsWe collected 4 types of cancer-specific predictors for somatic mutations, copy number variation (CNV), gene expression, DNA methylation, and chromatin accessibility by ATAC-Seq. All data were produced from TCGA cohorts and the preprocessed data were downloaded from https://xenabrowser.net. The pan-CNVs mapped onto genes were downloaded by the link, https://tcga.xenahubs.net/download/TCGA.PANCAN.sampleMap/Gistic2_CopyNumber_Gistic2 _all_thresholded.by_genes.gz. We then wrote a progr...

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Simultaneous Methylation-Level Assessment of Hundreds of CpG Sites by Targeted Bisulfite PCR Sequencing (TBPseq)

Cited by 7 publications

References 44 publications

Demethylation and derepression of genomic retroelements in the skeletal muscles of aged mice

Demethylation and derepression of genomic retroelements in the skeletal muscles of aged mice

Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome

WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

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