Differential methylation site identification software tools | DNA methylation microarray data analysis
After correct preprocessing of the data (i.e. filtering out problematic probes and normalizing the data), differential methylation analysis can be performed. Generally, the first approach consists in a single-probe analysis. Statistical tests (such as the t-test or Mann–Whitney test) are used, and when the P-values obtained are below a given threshold (e.g. <0.05), the sites are considered as differentially methylated and referred as differentially methylated positions (DMPs). In this way, several researchers have identified numerous DMPs although the absolute difference in methylation of the CpG sites between two groups of samples was small (i.e. below 5% of methylation difference).
An R package for comprehensive analysis of DNA methylation data obtained with any experimental protocol that provides single-CpG resolution, including Infinium 450K microarray and bisulfite sequencing protocols, but also MeDIP-seq and MBD-seq.
A suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. minfi provides methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. Several preprocessing algorithms are available and the infrastructure provides a convenient way for developers to easily implement their techniques as Bioconductor tools. By making SNP annotation available, users can choose to be cautious about probes that may behave unexpectedly due to the inclusion of a SNP in the probe sequence. minfi is unique in that it provides both bump hunting and block finding capabilities, and the assessment of statistical significance for the identified regions. Finally, because the package is implemented in Bioconductor, it gives users access to the countless analysis and visualization tools available in R.
Allows Illumina HumanMethylation BeadChip analysis. ChAMP is an integrated analysis pipeline including functions for (i) filtering low quality probes, adjustment for Infinium I and Infinium II probe design, (ii) batch effect correction, detecting differentially methylated positions (DMPs), (iii) finding differentially methylated regions (DMRs) and (iv) detection of copy number aberrations. The software also allows detection of differentially methylated genomic blocks (DMB) and Gene Set Enrichment Analysis (GSEA).
A Windows command-line application that implements a Bayesian wavelet-based functional mixed model methodology for functional data analysis. WFMM can be generally applied to any complex functional data sampled on a fine grid, not just methylation data, and so can be readily applied to other genome-wide data including copy number and tiling transcriptome arrays. The method is computationally intensive, but the software is optimized so that it can handle very large data sets.
An R package that can efficiently perform the statistical analysis needed for increasingly large methylation datasets. CpGassoc can perform standard analyses of large datasets very quickly with no need to impute the data. It can handle mixed effects models with chip or batch entering the model as a random intercept. CpGassoc also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.
A method for detection of differential distribution of methylation, based on distribution-valued data. D3M can detect the differences in high-order moments, such as shapes of underlying distributions in methylation profiles, based on the Wasserstein metric. We test the significance of the difference between case and control groups and provide an interpretable summary of the results. The simulation results show that the proposed method achieves promising accuracy and shows favorable results compared with previous methods.
Performs analyses of array-based DNA methylation data. MethLAB is a graphical user interface (GUI) package. For each cytosine–phosphate–guanine (CpG) site, the software models methylation as a function of a categorical or continuous phenotype and other covariates. This package can incorporate both continuous and categorical covariates, as well as fixed or random batch or chip effects. It has been designed to optimize memory use during analyses.
Assists users to detect gene probes with different means or variances between two groups. DiffMeanVar uses joint score test to detect methylation sites relevant to a disease. The joint score test is based on both differential methylation level and differential variability.
Allows to analyze and visualize differential methylation in genomic regions. ADMIRE is a semi-automatic pipeline that features five different normalization methods and performs two one-sided two-sample rank tests (Mann–Whitney U tests). The software features arbitrary experimental settings, quality control, automatic filtering, normalization, multiple testing, differential analyses on arbitrary genomic regions. It additionally implements a gene set enrichment procedure.
Allows to incorporate adjustment for confounding variables that potentially affect methylation levels. methylDMV works about simultaneous detection of differential mean (DM) and differential variability (DV) in methylation analysis. This tool realizes systematic selection of cytosine-phosphate-guanine sites (CpGs) which exhibit differential mean and/or differential variability in the prediction algorithm in order to improve prediction accuracy and biological interpretation.
A native python module for analysis of 450k methylation platform. PyMAP can be easily deployed to cloud platforms that support python scripting language for large-scale methylation studies. By implementing fast parsing functionality, this module can be used to analyze large-scale methylation datasets. Additionally, command-line executables shipped with the module can be used to perform common analysis tasks on personal computers.
Assists users in investigating normalized omics large datasets. Qlucore Omics Explorer is a modular platform divided into four main functionalities (i) Visualization includes features for generating various plots types including principal component analysis (PCA) plots and real-time visualization; (ii) Exploration furnishes tools for comparing, browsing and selecting targeted information (iii) Analysis includes statistical methods such as quadratic regression, f-tests, or ANOVA and (iv) Sharing allows users to export results as multiple formats including videos or variable lists.
Implements analysis tools for DNA methylation data generated using Nimblegen microarrays and the McrBC protocol. It finds differentially methylated regions between samples, calculates percentage methylation estimates and includes array quality assessment tools.
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