Comprehensive benchmarking of software for mapping whole genome bisulfite data: from read alignment to DNA methylation analysis

Abstract

Whole genome bisulfite sequencing is currently at the forefront of epigenetic analysis, facilitating the nucleotide-level resolution of 5-methylcytosine (5mC) on a genome-wide scale. Specialized software have been developed to accommodate the unique difficulties in aligning such sequencing reads to a given reference, building on the knowledge acquired from model organisms such as human, or Arabidopsis thaliana. As the field of epigenetics expands its purview to non-model plant species, new challenges arise which bring into question the suitability of previously established tools. Herein, nine short-read aligners are evaluated: Bismark, BS-Seeker2, BSMAP, BWA-meth, ERNE-BS5, GEM3, GSNAP, Last and segemehl. Precision-recall of simulated alignments, in comparison to real sequencing data obtained from three natural accessions, reveals on-balance that BWA-meth and BSMAP are able to make the best use of the data during mapping. The influence of difficult-to-map regions, characterized by deviations in sequencing depth over repeat annotations, is evaluated in terms of the mean absolute deviation of the resulting methylation calls in comparison to a realistic methylome. Downstream methylation analysis is responsive to the handling of multi-mapping reads relative to mapping quality (MAPQ), and potentially susceptible to bias arising from the increased sequence complexity of densely methylated reads.

The EpiDiverse Plant Epigenome-Wide Association Studies (EWAS) Pipeline

“…The EpiDiverse WGBS pipeline with the segemehl standalone tool [ 66 ] was used for methylation calling with the options --noDedup, --SE, and --unique, using the high confidence gene set as reference [ 52 ]. Overall, 15 to 85 million reads per sample were left after trimming, yielding a coverage of 8- to 26-fold ( Table S4 ).…”

“…These pipelines are modular and scalable and can easily connect their inputs and outputs (Figure 1), providing a suite of useful tools for whole-genome bisulfite sequencing (WGBS) methylation calling, single nucleotide polymorphism detection (SNP), differentially methylated position, and region (DMP and DMR) detection and EWAS ( , accessed on 1 March 2021). The software included in the WGBS and DMR pipelines was selected from the best performing tools in benchmarking studies [ 52 , 53 ]. Here, we describe and test the performance of the EpiDiverse EWAS pipeline using four different input types from two non-model plant datasets and test the effect of missing data.…”

EpiDiverse Toolkit: a pipeline suite for the analysis of bisulfite sequencing data in ecological plant epigenetics

“…The reference genome is optionally indexed by the pipeline itself, or provided alongside the relevant index files to begin with. Mapping of bisulfite sequencing data can be carried out either in ‘high-throughput mode’, with a low memory footprint and a runtime suitable for rapid analysis of population data, or ‘high-sensitivity mode’, with a demonstrable improvement in precision-recall and downstream methylation analysis for non-model plant species, as selected according to corresponding benchmarks ( 17 ). Multiple samples can be processed in parallel, and quality control (QC) is performed with a combination of published tools and in-house scripting.…”

Msuite2: All-in-one DNA methylation data analysis toolkit with enhanced usability and performance

“…In WGBS, the bisulfite treatment converts the unmethylated cytosines to thymines while leaves the methylated ones unchanged, and therefore allows the differentiation of methylated and unmethylated cytosines. However, the bisulfite treatment procedure alters the original DNA sequence, which reduces the complexity of the DNA and makes the data analysis task complex and time-consuming [13] . In addition, recent technical development of novel experimental assays, such as Tet-assisted pyridine borane sequencing (TAPS) [14] , have utilized different strategies in differentiating methylated and unmethylated cytosines, thus imposing new requirements in data analysis.…”

“…In past years, other researchers and we have developed a handful of bioinformatics tools for WGBS data analysis [13] , [15] , [16] . For instance, we have integrated quality control, read alignment, methylation call, and data visualization features in one package, Msuite (referred as Msuite1 hereafter) [15] .…”