ArticleFrontiers in plant science2020
Performance of Mapping Approaches for Whole-Genome Bisulfite Sequencing Data in Crop Plants.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 38 citations in OpenAlex.
- Genome-scale DNA methylome and transcriptome profiling of midgut of Bombyx mori infected with BmCPV.Scientific data · 2026Article
- Methylome differences among barley inbreds and their association with genomic, transcriptomic, and phenotypic variation.Journal of experimental botany · 2026Article
- DNA methylation in wheat: current understanding and future potential for enhancing biotic and abiotic stress tolerance.Physiology and molecular biology of plants : an international journal of functional plant biology · 2024Review
- Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data.Bioinformatics (Oxford, England) · 2023Article
- Systematic and benchmarking studies of pipelines for mammal WGBS data in the novel NGS platform.BMC bioinformatics · 2023Article
- Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.Plants (Basel, Switzerland) · 2022Review
- Low guanine content and biased nucleotide distribution in vertebrate mtDNA can cause overestimation of non-CpG methylation.NAR genomics and bioinformatics · 2022Article
- Analysis and Performance Assessment of the Whole Genome Bisulfite Sequencing Data Workflow: Currently Available Tools and a Practical Guide to Advance DNA Methylation Studies.Small methods · 2022Review
- Performance of methods to detect genetic variants from bisulphite sequencing data in a non-model species.Molecular ecology resources · 2022Article
- Benchmarking DNA methylation analysis of 14 alignment algorithms for whole genome bisulfite sequencing in mammals.Computational and structural biotechnology journal · 2022Article
- Rapid identification of methylase specificity (RIMS-seq) jointly identifies methylated motifs and generates shotgun sequencing of bacterial genomes.Nucleic acids research · 2021Article
- Challenges and Perspectives in the Epigenetics of Climate Change-Induced Forests Decline.Frontiers in plant science · 2021Review
- Developmental Methylome of the Medicinal PlantInternational journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation is involved in many different biological processes in the development and well-being of crop plants such as transposon activation, heterosis, environment-dependent transcriptome plasticity, aging, and many diseases. Whole-genome bisulfite sequencing is an excellent technology for detecting and quantifying DNA methylation patterns in a wide variety of species, but optimized data analysis pipelines exist only for a small number of species and are missing for many important crop plants. This is especially important as most existing benchmark studies have been performed on mammals with hardly any repetitive elements and without CHG and CHH methylation. Pipelines for the analysis of whole-genome bisulfite sequencing data usually consists of four steps: read trimming, read mapping, quantification of methylation levels, and prediction of differentially methylated regions (DMRs). Here we focus on read mapping, which is challenging because un-methylated cytosines are transformed to uracil during bisulfite treatment and to thymine during the subsequent polymerase chain reaction, and read mappers must be capable of dealing with this cytosine/thymine polymorphism. Several read mappers have been developed over the last years, with different strengths and weaknesses, but their performances have not been critically evaluated. Here, we compare eight read mappers: Bismark, BismarkBwt2, BSMAP, BS-Seeker2, Bwameth, GEM3, Segemehl, and GSNAP to assess the impact of the read-mapping results on the prediction of DMRs. We used simulated data generated from the genomes of
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