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Type: Package
Title: Gradient Boosting Algorithm for Predicting Methylation States
Version: 0.1.0
Author: Dipro Sinha [aut, cre], Sunil Archak [aut], Dwijesh Chandra Mishra [aut], Tanwy Dasmandal [aut], Md Yeasin [aut]
Maintainer: Dipro Sinha <diprosinha@gmail.com>
Description: DNA methylation of 5-methylcytosine (5mC) is the result of a multi-step, enzyme-dependent process. Predicting these sites in-vitro is laborious, time consuming as well as costly. This ' Gb5mC-Pred ' package is an in-silico pipeline for predicting DNA sequences containing the 5mC sites. It uses a machine learning approach which uses Stochastic Gradient Boosting approach for prediction of the sequences with 5mC sites. This package has been developed by using the concept of Navarez and Roxas (2022) <doi:10.1109/TCBB.2021.3082184>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.2.1
Imports: stats, devtools, tidyverse, seqinr, Biostrings, splitstackshape, entropy, party, stringr, tibble, doParallel, parallel, e1071, caret, randomForest, gbm, foreach, ftrCOOL, iterators
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2023-07-10 17:22:06 UTC; YEASIN
Repository: CRAN
Date/Publication: 2023-07-11 15:40:05 UTC

Pred5mc

Description

Prediction of sequences with 5mc sites.

Usage

Pred5mc(Fastafile)

Arguments

Fastafile

Sequence file (.fasta format)

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(GB5mcPred)
data<-system.file("exdata/test.fasta", package = "GB5mcPred")
pred<-Pred5mc(Fastafile=data)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.