Read simulation software tools | Bisulfite sequencing data analysis
As the number of studies looking at differences between DNA methylation increases, there is a growing demand to develop and benchmark statistical methods to analyse these data. To date no objective approach for the comparison of these methods has been developed and as such it remains difficult to assess which analysis tool is most appropriate for a given experiment. As a result, there is an unmet need for a DNA methylation data simulator that can accurately reproduce a wide range of experimental setups, and can be routinely used to compare the performance of different statistical models.
A package for simulating DNA sequencing errors, polymorphisms, cytosine methylation and bisulfite conversion. DNemulator simulates polymorphisms based on real polymorphisms and their frequencies, diploid genomes, sequencing errors based on real per-sequence, per-base error probabilities and various cytosine methylation rates depending on cg-context, and bisulfite conversion.
Provides a simulation tool to generate multi-omics data. OmicsSMILA leans on two main modules: SeqSIMLA and pWGBSSimla. SeqSIMLA allows users to simulate sequence data in families with various parameters for sibling and case-control samples and under different disease models. pWGBSSimla focuses on whole-genome DNA methylation data simulation by modeling methylation quantitative trait loci, allele-specific methylations and differentially methylated regions. This tool is available as a desktop and a web-application version.
Mimics FastQ files for high-throughput sequencing (HTS) experiments. Sherman reproduces ungapped high-throughput datasets for bisulfite sequencing (BS-Seq) or standard experiments. It can insert basecall errors, single nucleotide polymorphisms (SNPs), adapter fragments and other contaminants into the sequence. This tool permits the assessment of the influence of common problems observed in many Next-Gen Sequencing experiments.
A flexible stochastic simulation tool that generates single-base resolution DNA methylation data genome-wide. Several simulator parameters can be derived directly from real datasets provided by the user in order to mimic real case scenarios. Thus, it is possible to choose the most appropriate statistical analysis tool for a given simulated design.
Simulates whole-genome inherited bisulphite sequencing data. MethInheritSim simulates a multi-generation methylation case versus control experiment with inheritance relation using a real control dataset.
Allows users to optimize reduced representation bisulfite sequencing (RRBS) for the biological system of interest by using combinations of restriction enzymes. cuRRBS generalizes the problem of genomic enrichment with restriction enzymes by allowing users to define both the genome and the particular sites of interest. It provides users with a variety of measures to compare the different suggested protocols.
Mimics reduced representation bisulfite sequencing (RRBS) data. RRBSsim constructs paired-end bisulfite-sequencing reads that contain significant attributes of real data. It implements sequence context-dependent error models. This tool can be used to recognize differential methylated regions (DMRs) and assess them. It is useful for the simulation of analogous distribution of methylation level and read length.