Methrix: an R/Bioconductor package for systematic aggregation and analysis of bisulfite sequencing data

2020 ◽  
Author(s):  
Anand Mayakonda ◽  
Maximilian Schönung ◽  
Joschka Hey ◽  
Rajbir Nath Batra ◽  
Clarissa Feuerstein-Akgoz ◽  
...  
Keyword(s):  
Bisulfite Sequencing ◽  
R Package ◽  
Supplementary Information ◽  
Differentially Methylated Region ◽  
File Format ◽  
Sequencing Data ◽  
Systematic Analysis ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Easy Integration

Abstract Motivation Whole-genome bisulfite sequencing (WGBS) measures DNA methylation at base pair resolution resulting in large bedGraph like coverage files. Current options for processing such files are hindered by discrepancies in file format specification, speed, and memory requirements. Results We developed methrix, an R package, which provides a toolset for systematic analysis of large datasets. Core functionality of the package includes a comprehensive bedGraph or similar tab-separated text file reader—which summarizes methylation calls based on annotated reference indices, infers and collapses strands and handles uncovered reference CpG sites while facilitating a flexible input file format specification. Additional optimized functions for quality control filtering, subsetting and visualization allow user-friendly and effective processing of WGBS results. Easy integration with tools for differentially methylated region (DMR) calling and annotation further eases the analysis of genome-wide methylation data. Overall, methrix enriches established WGBS workflows by bringing together computational efficiency and versatile functionality. Availability and implementation Methrix is implemented as an R package, made available under MIT license at https://github.com/CompEpigen/methrix and can be installed from the Bioconductor repository. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Xubin Zheng ◽  
Qiong Wu ◽  
Haonan Wu ◽  
Kwong-Sak Leung ◽  
Man-Hon Wong ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Dna Sequences ◽  
Bisulfite Sequencing ◽  
Differentially Methylated Region ◽  
Sequencing Data ◽  
Reduced Representation ◽  
Targeted Bisulfite Sequencing ◽  
Differentially Methylated Genes ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data

Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.

scholarly journals GBSA: a comprehensive software for analysing whole genome bisulfite sequencing data

2012 ◽  
Vol 41 (4) ◽  
pp. e55-e55 ◽  
Author(s):  
Touati Benoukraf ◽  
Sarawut Wongphayak ◽  
Luqman Hakim Abdul Hadi ◽  
Mengchu Wu ◽  
Richie Soong
Keyword(s):  
Bisulfite Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data

scholarly journals MethGo: a comprehensive tool for analyzing whole-genome bisulfite sequencing data

BMC Genomics ◽  
2015 ◽  
Vol 16 (Suppl 12) ◽  
pp. S11 ◽  
Author(s):  
Wen-Wei Liao ◽  
Ming-Ren Yen ◽  
Evaline Ju ◽  
Fei-Man Hsu ◽  
Larry Lam ◽  
...  
Keyword(s):  
Bisulfite Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data

scholarly journals wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Marius Wöste ◽  
Elsa Leitão ◽  
Sandra Laurentino ◽  
Bernhard Horsthemke ◽  
Sven Rahmann ◽  
...  
Keyword(s):  
Bisulfite Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Whole Genome Bisulfite Sequencing ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
End To End

scholarly journals Focal disruption of DNA methylation dynamics at enhancers in IDH-mutant AML cells

Leukemia ◽  
2021 ◽  
Author(s):  
Elisabeth R. Wilson ◽  
Nichole M. Helton ◽  
Sharon E. Heath ◽  
Robert S. Fulton ◽  
Jacqueline E. Payton ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Myeloid Leukemia ◽  
Bisulfite Sequencing ◽  
Sequencing Data ◽  
Genome Wide ◽  
Cd34 Cells ◽  
Recurrent Mutations ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Acute Myeloid

AbstractRecurrent mutations in IDH1 or IDH2 in acute myeloid leukemia (AML) are associated with increased DNA methylation, but the genome-wide patterns of this hypermethylation phenotype have not been comprehensively studied in AML samples. We analyzed whole-genome bisulfite sequencing data from 15 primary AML samples with IDH1 or IDH2 mutations, which identified ~4000 focal regions that were uniquely hypermethylated in IDHmut samples vs. normal CD34+ cells and other AMLs. These regions had modest hypermethylation in AMLs with biallelic TET2 mutations, and levels of 5-hydroxymethylation that were diminished in IDH and TET-mutant samples, indicating that this hypermethylation results from inhibition of TET-mediated demethylation. Focal hypermethylation in IDHmut AMLs occurred at regions with low methylation in CD34+ cells, implying that DNA methylation and demethylation are active at these loci. AML samples containing IDH and DNMT3AR882 mutations were significantly less hypermethylated, suggesting that IDHmut-associated hypermethylation is mediated by DNMT3A. IDHmut-specific hypermethylation was highly enriched for enhancers that form direct interactions with genes involved in normal hematopoiesis and AML, including MYC and ETV6. These results suggest that focal hypermethylation in IDH-mutant AML occurs by altering the balance between DNA methylation and demethylation, and that disruption of these pathways at enhancers may contribute to AML pathogenesis.

Development of a method for identifying and functionally analyzing allele-specific DNA methylation based on BS-seq data

Epigenomics ◽  
2019 ◽  
Vol 11 (15) ◽  
pp. 1679-1692
Author(s):  
Jiang Zhu ◽  
Mu Su ◽  
Yue Gu ◽  
Xingda Zhang ◽  
Wenhua Lv ◽  
...  
Keyword(s):  
Dna Methylation ◽  
High Throughput ◽  
Bisulfite Sequencing ◽  
The Cancer Genome Atlas ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data ◽  
Allele Specific ◽  
New Perspective

Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

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