Package 'EpiSemble'

Title: Ensemble Based Machine Learning Approach for Predicting Methylation States
Description: DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This 'EpiSemble' package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.
Authors: Dipro Sinha [aut, cre], Sunil Archak [aut], Dwijesh Chandra Mishra [aut], Tanwy Dasmandal [aut], Md Yeasin [aut]
Maintainer: Dipro Sinha <[email protected]>
License: GPL-3
Version: 0.1.1
Built: 2025-02-06 04:13:29 UTC
Source: https://github.com/cran/EpiSemble

Help Index


Epigenetic Modification Prediction

Description

Predicting sequences with 6mA sites.

Usage

epiPred(FastaData,Species)

Arguments

FastaData

Sequence file (.fasta format)

Species

Model organism

Value

MethStatus: Sequences with their methylation state (methylated or non-methylated)

References

Chen, W., Lv, H., Nie, F., & Lin, H. (2019). i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics, 35(16), 2796-2800.

Examples

library(EpiSemble)
data<-system.file("exdata/test.fasta", package = "EpiSemble")
pred<-epiPred(FastaData=data, Species="Rice")

Important Features

Description

Find out the most suitable features for predicting sequences with 6mA sites.

Usage

ImpFeatures(Fastafile,Species)

Arguments

Fastafile

Sequence file in .fasta format

Species

Model organism

Value

test_data_input: A matrix containing important features for prediction

References

Chen, W., Lv, H., Nie, F., & Lin, H. (2019). i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics, 35(16), 2796-2800.

Examples

library(EpiSemble)
data<-system.file("exdata/test.fasta", package = "EpiSemble")
imp<-ImpFeatures(Fastafile=data, Species="Rice")