Quantitative score assessment software tools | Bisulfite sequencing data analysis
Recently, great interest has been aroused in decoding DNA methylation patterning to understand the generation of cell diversity. In addition to tracing the cell lineage. In particular, with the emergence of next-generation sequencing techniques, rapidly accumulating deep bisulfite sequencing data allow the securitization of DNA methylation patterns on genome-wide scale. However, existing DNA methylation analysis tools mainly focus on the bisulfite sequencing data mapping and the comparison at DNA methylation level. No software has been developed to assess DNA methylation variations embedded in bisulfite sequencing data.
A program to enable the visualisation and analysis of mapped sequence data. SeqMonk was written for use with mapped next generation sequence data but can in theory be used for any dataset which can be expressed as a series of genomic positions. It's main features are: (i) Import of mapped data from mapped data (BAM/SAM/bowtie etc), (ii) Creation of data groups for visualisation and analysis, (iii) Visualisation of mapped regions against an annotated genom, (iv) Flexible quantitation of the mapped data to allow comparisons between data sets, (v) Statistical analysis of data to find regions of interest and (vi) Creation of reports containing data and genome annotation.
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.
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.
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.
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