Differentially methylated region identification software tools | Bisulfite sequencing data analysis
DNA methylation plays critical roles in transcriptional regulation and chromatin remodeling. Differentially methylated regions (DMRs) have important implications for development, aging and diseases. Therefore, genome-wide mapping of DMRs across various temporal and spatial methylomes is important in revealing the impact of epigenetic modifications on heritable phenotypic variation.
Allows to analyze, compare, and visualize next generation sequencing (NGS) data. CLC Genomics Workbench offers a complete and customizable solution for genomics, transcriptomics, epigenomics, and metagenomics. The software enables to generate custom workflows, which can combine quality control steps, adapter trimming, read mapping, variant detection, and multiple filtering and annotation steps into a pipeline.
An R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. methylKit is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.
Takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Qvalue generates various plots automatically, allowing one to make sensible significance cut-offs. It can be applied to problems in genomics, brain imaging, astrophysics, and data mining.
An R package for quantifying variation in DNA methylation as a cancer biomarker. EVORA uses differential variability to select features, and then uses an adaptive index algorithm over these features to assign a risk score to independent samples.
A command-line tool and a python library that manipulates BED files of possibly irregularly spaced P-values and (1) calculates auto-correlation, (2) combines adjacent P-values, (3) performs false discovery adjustment, (4) finds regions of enrichment (i.e. series of adjacent low P-values) and (5) assigns significance to those regions. In addition, tools are provided for visualization and assessment. The comb-p software is useful in contexts where auto-correlated P-values are generated across the genome. Because the library accepts input in a simple, standardized format and is unaffected by the origin of the P-values, it can be used for a wide variety of applications.
A complete, accurate and efficient solution for analysis of large scale base-resolution DNA methylation data, bisulfite sequencing or single molecule direct sequencing. MOABS seamlessly integrates alignment, methylation calling, identification of hypomethylation for one sample and differential methylation for multiple samples, and other downstream analysis.