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Type: Package
Title: Selective Sweep Discovery Tool
Version: 0.1.1
Author: Abhik Sarkar [aut, cre], Dwijesh Chandra Mishra [aut], Dipro Sinha [aut], Saikath Das [aut], Md Yeasin [aut]
Maintainer: Abhik Sarkar <abhik.jenkins.sarkar@gmail.com>
Description: Selective sweep is a biological phenomenon in which genetic variation between neighboring beneficial mutant alleles is swept away due to the effect of genetic hitchhiking. Detection of selective sweep is not well acquainted as well as it is a laborious job. This package is a user friendly approach for detecting selective sweep in genomic regions. It uses a Random Forest based machine learning approach to predict selective sweep from VCF files as an input. Input of this function, train data and new data, can be computed using the project https://github.com/AbhikSarkar1999/SweepDiscovery in 'GitHub'. This package has been developed by using the concept of Pavlidis and Alachiotis (2017) <doi:10.1186/s40709-017-0064-0>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.2.1
Imports: stats, utils, randomForest
NeedsCompilation: no
Packaged: 2023-12-13 17:09:43 UTC; YEASIN
Depends: R (≥ 3.5.0)
Repository: CRAN
Date/Publication: 2023-12-14 06:20:02 UTC

SweepPrediction

Description

SweepPrediction

Usage

SweepPrediction(Traindata = NULL, Newdata)

Arguments

Traindata

Dataset for training

Newdata

New data for prediction

Value

References

Examples


library("SweepDiscovery")
data <- system.file("extdata", "data.csv", package = "SweepDiscovery")
Data<- read.csv(data)
pred<-SweepPrediction(Traindata=NULL,Newdata=Data)

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