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ENmix
Provides a background correction method which uses a mixture of exponential and truncated normal distributions to flexibly model signal intensity and uses a truncated normal distribution to model background noise. Depending on data availability, three approaches are employed to estimate background normal distribution parameters using (i) internal chip negative controls, (ii) out-of-band Infinium I probe intensities or (iii) combined methylated and unmethylated intensities. Evaluation results in both duplicates and experimental standard samples showed that ENmix outperformed commonly used background subtraction methods in terms of improvement in replicability and accuracy as well as reducing probe design bias. After ENmix background correction the resulting data can be used with other commonly-used preprocessing methods including quantile normalization for between-sample normalization and BMIQ for further correction of probe-design bias.
BS Seeker
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Allows to map the bisulfite-treated short reads. BS Seeker is a bisulfite sequencing (BS) alignment tool that performs genome indexing, read alignment and DNA methylation levels calling for each cytosine. The software was improved utilizing multiple short-read mapping aligners, supporting gapped mapping and local alignment and building special indexes for handling reduced represented bisulfite sequencing (RRBS) data.
methylKit
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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.
Bismark
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A software package to map and determine the methylation state of BS-Seq reads. Bismark is easy to use, very flexible and is the first published BS-Seq aligner to seamlessly handle single- and paired-end mapping of both directional and non-directional bisulfite libraries. The output of Bismark is easy to interpret and is intended to be analysed directly by the researcher performing the experiment.
GBSA / Genome Bisulfite Sequencing Analyser
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An open-source software tool capable of analysing whole-genome bisulfite sequencing data with either a gene-centric or gene-independent focus. GBSA’s output can be easily integrated with other high-throughput sequencing data, such as RNA-Seq or ChIP-seq, to elucidate the role of methylated intergenic regions in gene regulation. In essence, GBSA allows an investigator to explore not only known loci but also all the genomic regions, for which methylation studies could lead to the discovery of new regulatory mechanisms.
CGmapTools
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Provides a standard for bisulfite sequencing data related manipulation. CGmapTools is a command-line bisulfite sequencing analysis toolkit with enhanced features on single-nucleotide variant (SNV) calling and allele specific methylations and visualizations. It includes modules for better data storage, extraction, visualization and improved performance in single-nucleotide polymorphism (SNP) calling.
BatMeth
An efficient and accurate general-purpose bisulfite sequence mapping program. BatMeth can be deployed for the analysis of genome-wide bisulfite sequencing using either base reads or color reads. It allows asymmetric bisulfite conversion to be detected by labeling the corresponding reference genome with the hit. It integrates novel mismatch counting, list filtering, mismatch stage filtering and fast mapping onto two indexes components to improve unique mapping rate, speed and precision. Experimental results show that BatMeth is faster and more accurate than existing tools.
Lux
A generative model to quantify DNA methylation modifications from any combination of bisulfite sequencing approaches, including reduced, oxidative, TET-assisted, chemical-modification assisted, and methylase-assisted bisulfite sequencing data. Lux models all cytosine modifications (C, 5mC, 5hmC, 5fC, and 5caC) simultaneously together with experimental parameters, including bisulfite conversion and oxidation efficiencies, as well as various chemical labeling and protection steps. We show that Lux improves the quantification and comparison of cytosine modification levels and that Lux can process any oxidized methylcytosine sequencing data sets to quantify all cytosine modifications.
MethylSeekR
A computational tool to accurately identify footprints from bisulfite-sequencing data. MethylSeekR incorporates several methodological improvements and extensions that make it robust and generally applicable. The method is based on a cutoff approach that identifies hypomethylated regions as stretches of consecutive CpGs with methylation levels below a fixed threshold. To achieve high accuracy and sensitivity, MethylSeekR incorporates important preprocessing and filtering steps, and controls segmentation parameters via false discovery rate (FDR) calculations. MethylSeekR is an easy-to-use package that describes in detail each step of the analysis and produces several control plots to facilitate the interpretation of the results and to avoid potential pitfalls in the analysis.
COHCAP / City of Hope CpG Island Analysis Pipeline
Provides tools for analysing single-nucleotide resolution methylation data. COHCAP is a pipeline that covers most user needs for differential methylation and integration with gene expression data. The software includes quality control metrics, defining differentially methylated CpG sites, defining differentially methylated CpG islands and visualization of methylation data. It contains two different methods of CpG island analysis. COHCAP has been shown scalable for high-quality integrative analysis of cell line data as well as large heterogeneous patient samples.
CpG_MPs
A comprehensive tool for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG_MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy.
segmentSeq
Analyses small RNA sequencing data from multiple biological sources, taking into account replicate information, to identify robust sets of siRNA precursors. The segmentSeq R package has been extended to identify methylation loci from high-throughput sequencing data from multiple conditions. A statistical model is then developed that accounts for biological replication and variable rates of non-conversion of cytosines in each sample to compute posterior likelihoods of methylation at each locus within an empirical Bayesian framework.
MethylCoder
Generates per-base methylation data given a set of bisulfite-treated reads. MethylCoder provides the option to use either of two existing short-read aligners, each with different strengths. It accounts for soft-masked alignments and overlapping paired-end reads. MethylCoder outputs data in text and binary formats in addition to the final alignment in SAM format, so that common high-throughput sequencing tools can be used on the resulting output. It is more flexible than existing software tool and competitive in terms of speed and memory use.
DMEAS / DNA Methylation Entropy Analysis Software
Analyzes the distribution of DNA methylation patterns for the quantification of epigenetic heterogeneity. DMEAS supports the analysis of both locus-specific and genome-wide bisulfite sequencing data. It progressively scans the mapping results of bisulfite sequencing reads to extract DNA methylation patterns for contiguous CpG dinucleotides. It determines the DNA methylation level and calculates methylation entropy for genomic segments to enable the quantitative assessment of DNA methylation variations observed in cell populations.
BEAT / BS-Seq Epimutation Analysis Toolkit
Delivers methods for the estimation of methylation levels, methylation status and for calling epimutation events in a two-sample comparison. BEAT implements all bioinformatics steps required for the quantitative high-resolution analysis of DNA methylation patterns from bisulfite sequencing data, including the detection of regional epimutation events, i.e. loss or gain of DNA methylation at CG positions relative to a reference. Using a binomial mixture model, the BEAT package aggregates methylation counts per genomic position, thereby compensating for low coverage, incomplete conversion and sequencing errors.
BRAT-nova / Bisulfite-treated Reads Analysis Tool-nova
A completely rewritten and improved implementation of the mapping tool BRAT-BW for bisulfite-treated reads (BS-seq). BRAT-nova employs a novel space-efficient representation of the genome and supports local alignment by allowing one indel per read. It is very fast and accurate. On the human genome, BRAT-nova is 2-7 times faster than state-of-the-art aligners, while maintaining the same percentage of uniquely mapped reads and space usage. On synthetic reads, BRAT-nova is 2-8 times faster than state-of-the-art aligners while maintaining similar mapping accuracy, methylation call accuracy, methylation level accuracy, and space efficiency.
bicycle / BIsulfite-based methylCYtosine CalLEr
Analyzes whole genome bisulfite sequencing (WGBS) data. bicycle is a next-generation sequencing (NGS) bioinformatic pipeline that can process data from directional (Lister) and non-directional (Cokus) bisulfite sequencing protocols and from single-end and paired-end sequencing. It also performs methylation calls for cytosines in CG and non-CG contexts (CHG and CHH). It provides statistical methylcytosine calling and offers several filters to screen reads.
AgIn / Aggregate on Intervals
A linear-time algorithm that combines the kinetic information for neighboring CpG sites and increases the confidence in identifying the methylation states of those sites. Using a practical read coverage of ∼30-fold from an inbred strain medaka (Oryzias latipes), we observed that both the sensitivity and precision of our method on individual CpG sites were ∼93.7%. We also observed a high correlation coefficient (R = 0.884) between our method and bisulfite sequencing, and for 92.0% of CpG sites, methylation levels ranging over [0,1] were in concordance within an acceptable difference 0.25.
SMAP / Streamlined Methylation Analysis Pipeline
Allows to extract multiple types of information (such as DMCs, DMRs, SNPs and ASM) from various types of RRBS and Bis-seq data. SMAP is designed to be an easy-to-use, one-stop and sophisticated package for methylation analyses. The pipeline consists of seven operational stages: (i) reference preparation, (ii) read preparation, (iii) alignment, (iv) calculation of methylation rate, (v) differentially methylated regions (DMR) detection, (vi) single nucleotide polymorphism (SNP) and allele-specific DNA methylation (ASM) calling and (vii) summarization.
CyMATE / Cytosine Methylation Analysis Tool for Everyone
A software platform to perform in silico analyses of DNA methylation at cytosine sites. CyMATE is suitable for analyses of sequence data obtained with bisulfite genomic sequencing and hairpin-bisulfite sequencing, i.e. single-strand and double-strand DNA data. A module for mutation analysis at non-methylation sites is also available. It has been designed as a universal tool for quick, comprehensive and automated analysis of bisulfite sequencing data, providing detailed qualitative and quantitative results. Analysis with CyMATE is simple, very fast, platform-independent and the most detailed of computational analysis methods.
methGraph
Assists users in visualizing in silico quality evaluation of polymerase chain reaction (PCR)-based methylation assays. MethGraph consists of three parts: (i) the submission of one or more primer pairs either separately or by means of a list of primer pairs, (ii) the organism of interest and the type of genome sequence for which the primers’ specificity must be checked, and (iii) the formatting (display mode, color, name and description) and visualization of the primer custom tracks.
Methy-Pipe
A software tool that not only fulfills the core data analysis requirements (e.g. sequence alignment, differential methylation analysis, etc.) but also provides useful tools for methylation data annotation and visualization. Specifically, Methy-Pipe uses Burrow-Wheeler Transform (BWT) algorithm to directly align bisulfite sequencing reads to a reference genome and implements a novel sliding window based approach with statistical methods for the identification of differentially methylated regions (DMRs). The capability of processing data parallelly allows it to outperform a number of other bisulfite alignment software packages.
WBSA / Web Service for Bisulfite Sequencing Data Analysis
A free web application for analysis of whole-genome bisulfite-sequencing (WGBS) and genome-wide reduced representation bisulfite sequencing (RRBS) data. WBSA not only focuses on CpG methylation, but also allows CHG and CHH analysis. BWA is incorporated as its mapping software. WBSA can be applied to DNA methylation researches for animals and plants and it provides advanced analysis for both WGBS and RRBS. It can also identify differently methylated regions (DMRs) in different strings. WBSA includes six modules: Home, WGBS, RRBS, DMR, Documents and Downloads, and provides the executable package for downloads and local installation. WGBS and RRBS modules include four main steps: pre-processing of reads and reference, alignment to the reference, identification of methylcytosines and annotation. DMR module includes DMRs identification and annotation of the correlative genes.
informME / information-theoretic analysis of MEthylation
Serves for inter-sample and differential methylation analysis of whole genome bisulfite sequencing (WGBS) data. informME takes in consideration all information available in methylation reads, and takes into account statistical dependencies between the methylation states of CpG sites. Moreover, it can quantify methylation stochasticity not by simple means and variances at individual CpG sites but by using joint probability distributions over the methylation states.
DeepCpG
A computational approach based on deep neural networks to predict DNA methylation states from DNA sequence and incomplete methylation profiles in single cells. We validate DeepCpG on mouse embryonic stem cells, where we report substantially more accurate predictions than previous methods. Additionally, we show that DeepCpG provides new insights for interpreting the sources of epigenetic diversity. Our model can be used to estimate the effect of single nucleotide changes and we uncover sequence motifs that are associated with DNA methylation level and epigenetic heterogeneity.
NGSmethPipe
Generates high-quality methylation maps. NGSmethPipe generates single base-pair-resolution methylation maps from bisulfite conversion high throughput sequencing experiments. It processes into 4 steps : sequence conversion into 3 letter alphabet building the Bowtie index, adapter removal and quality trimming, Bowtie alignment for single-end or pair-end reads, and post-processing including SNV and bisulfite failure detection, sequence error handling and extraction of methylation data for different sequence contexts.
RRBS-Analyser
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A comprehensive genome-scale DNA methylation analysis server based on RRBS data. RRBS-Analyser can assess sequencing quality, generate detailed statistical information, align the bisulfite-treated short reads to reference genome, identify and annotate the methylcytosines (5mCs) and associate them with different genomic features in CG, CHG, and CHH content. RRBS-Analyser supports detection, annotation, and visualization of differentially methylated regions (DMRs) for multiple samples from nine reference organisms. Moreover, RRBS-Analyser provides researchers with detailed annotation of DMR-containing genes, which will greatly aid subsequent studies. The input of RRBS-Analyser can be raw FASTQ reads, generic SAM format, or self-defined format containing individual 5mC sites. RRBS-Analyser can be widely used by researchers wanting to unravel the complexities of DNA methylome in the epigenetic community.
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