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This package helps in visualizing methylation in CpG sites in NGS data for given datasets (normal/tumor) and to identify differentially methylated CpG sites in normal/tumor. This package to help in perform profile hidden markov modelling of given sequences.
● Data Source:
BioConductor
● Keywords: Methylation
● Alias: MethTargetedNGS, MethTargetedNGS-package
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Bisulfite sequences are the bisulfite treated DNA sequences where all cytosines except cytosine from CpG sites are converted to thymie. This technique is used to determine pattern of methylation. This function convert all cytosine except cytosines from CpG sites to thymine.
● Data Source:
BioConductor
● Keywords: Bisulfite Conversion, Methylation
● Alias: bconv
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This function perform complete methylation analysis of the data.
● Data Source:
BioConductor
● Keywords: Average, Entropy, Fisher Test, Methylation, Odd Ratio, Significant CpG sites
● Alias: compare_samples
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Fisher exact test is a test to calculate the statistical significance using contingency table. It was used to find the statistically significant differences in the methylation status of one particular CpG site between healthy and tumor sample. Contingency matrix was created for each CpG site. P-value was corrected for multiple testing using Benjamini-Hochberg method to calculate False Discovery Rate (FDR)
● Data Source:
BioConductor
● Keywords: Fisher Test, Methylation, Significant CpG
● Alias: fishertest_cpg
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This function creates profile hidden markov model of the given aligned sequences using HMMER algorithm.[1]
● Data Source:
BioConductor
● Keywords: ProfileHMM, hmmbuild
● Alias: hmmbuild
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This function allow users to align pool of sequences to the reference sequence.
● Data Source:
BioConductor
● Keywords: Methylation Analysis, Sequence Alignment
● Alias: methAlign
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Methylation average of a CpG site is the percentage of unmethylated cytosine or methylated cytosine in a particular CpG site. The methylation average of a particular CpG site was calculated by number of cytosine divided by sum of total number of methylated and unmethylated cytosine at particular CpG site in a group of reads.
● Data Source:
BioConductor
● Keywords: Average, Methylation
● Alias: methAvg
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Entropy comparison between healthy and tumor samples can identify significant CpG sites which are contributing most in the tumor development either by hypomethylation or hypermethylation. Also such way can help in understanding the randomness in methylation status. Sliding window of 4 was used to calculate the entropy in the sample, which can analyze 16 different pattern for entropy calculation.
● Data Source:
BioConductor
● Keywords: Entropy, Methylation Entropy
● Alias: methEntropy
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Heatmaps are the way of visualizing methylation statuses of a sample. This function allows user to visualize methylation statuses at each CpG site for every sequence available in pool.
● Data Source:
BioConductor
● Keywords: Heatmap, Methylation
● Alias: methHeatmap
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This function calculates likelihood score of given pool of sequences against given profile hidden markov model using HMMER algorithm.[1]
● Data Source:
BioConductor
● Keywords: HMMER, Methylation, ProfileHMM
● Alias: nhmmer
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